Payment method and apparatus, device, medium and program product

Abstract

A payment method, executed by a biometric authentication device, the method comprising: acquiring ambient light information of an environment where the biometric authentication device is currently located, and on the basis of the ambient light information, determining an ambient light color bias type of the environment (101); on the basis of the ambient light color bias type, determining ambient light compensation color information used for reducing color biases of the ambient light color bias type (102); on the basis of the ambient light compensation color information, generating a color compensation image, and displaying the color compensation image (103); in response to a biometric authentication payment instruction, collecting a biometric image under mixed ambient light which has been compensated with reflected light of the color compensation image in the environment where the biometric authentication device is located (104); and when biometric image-based identity authentication has passed, performing a payment operation (105).

Description

Payment methods, devices, equipment, media and program products

[0001] Related applications

[0002] This application claims priority to Chinese patent application number 202410396338.7, filed on April 1, 2024, entitled “Payment Methods and Related Devices,” the entire text of which is hereby incorporated by reference. Technical Field

[0003] The present application relates to the field of computer technology, specifically to the field of payment technology, and in particular to a payment method, device, equipment, medium and program product. Background Art

[0004] With the rapid development and increasing maturity of Internet technology and artificial intelligence technology, people's lifestyles are becoming more and more convenient and intelligent. Taking daily consumption as an example, after people finish shopping in offline stores, they can make payments through palm print recognition, facial recognition and other methods.

[0005] In current technology, when a user wants to purchase an item, they typically directly collect a biometric image of the user, such as a facial image or palm print image. Identity verification is then performed based on the collected biometric image, and payment is then made based on the verification result. However, the specific environments of offline stores can be complex, and the quality of the biometric images collected using this method can be relatively poor, leading to problems with identity verification and hindering payment efficiency. Summary of the Invention

[0006] Embodiments of the present application provide a payment method, apparatus, device, medium, and program product.

[0007] In one aspect, an embodiment of the present application provides a payment method, comprising:

[0008] Acquiring ambient light information of an environment in which the biometric verification device is currently located, and determining a color bias type of the ambient light of the environment based on the ambient light information;

[0009] determining, according to the ambient light color deviation type, ambient light compensation color information for alleviating the color deviation of the ambient light color deviation type;

[0010] generating a color-compensated image according to the ambient light compensation color information, and displaying the color-compensated image;

[0011] In response to a biometric verification payment instruction, capturing a biometric image under mixed ambient light in an environment where the biometric verification device is located that compensates for reflected light of the color-compensated image; and

[0012] When the identity authentication based on the biometric image is passed, the payment operation is performed.

[0013] In another aspect, an embodiment of the present application provides a payment device, including:

[0014] an acquisition unit, configured to acquire ambient light information of an environment in which the biometric verification device is currently located, and determine a color deviation type of the ambient light of the environment according to the ambient light information;

[0015] a determining unit, configured to determine, according to the ambient light color deviation type, ambient light compensation color information for alleviating the color deviation of the ambient light color deviation type;

[0016] a generating unit, configured to generate a color-compensated image according to the ambient light compensation color information, and display the color-compensated image;

[0017] a collection unit configured to collect a biometric image in response to a biometric verification payment instruction under mixed ambient light in an environment where the biometric verification device is located that compensates for reflected light of the color-compensated image; and

[0018] The execution unit is configured to execute a payment operation when the identity authentication based on the biometric image is passed.

[0019] A biometric verification device provided in an embodiment of the present application includes a processor and a memory, wherein the memory stores multiple instructions, and the processor loads the instructions to execute the steps in the payment method provided in the embodiment of the present application.

[0020] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the steps in the payment method provided in the embodiment of the present application are implemented.

[0021] In addition, an embodiment of the present application also provides a computer program product, including a computer program or instructions, which, when executed by a processor, implements the steps in the payment method provided in the embodiment of the present application.

[0022] The details of one or more embodiments of the present application are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the present application will become apparent from the description, drawings, and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0024] FIG1 is a schematic diagram of a payment method according to an embodiment of the present application;

[0025] FIG2 is a flow chart of a payment method provided in an embodiment of the present application;

[0026] FIG3 is an illustration of a payment method provided in an embodiment of the present application;

[0027] FIG4 is another flow chart of the payment method provided in an embodiment of the present application;

[0028] FIG5 is another diagram illustrating the payment method provided in an embodiment of the present application;

[0029] FIG6 is another diagram illustrating the payment method provided in an embodiment of the present application;

[0030] FIG7 is another diagram illustrating the payment method provided in an embodiment of the present application;

[0031] FIG8 is another flow chart of the payment method provided in an embodiment of the present application;

[0032] FIG9 is a schematic diagram of the structure of a payment device provided in an embodiment of the present application;

[0033] FIG10 is a schematic diagram of the structure of a biometric verification device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0035] It is understandable that the payment method of this embodiment can be executed on the biometric verification device, or can be executed jointly by the biometric verification device and the server. The above examples should not be understood as limiting the present application.

[0036] As shown in Figure 1, a payment method is performed jointly by a biometric verification device and a server. The payment system provided in this embodiment includes a biometric verification device 10 and a server 11. The biometric verification device 10 and the server 11 are connected via a network, such as a wired or wireless network. The payment device may be integrated into the biometric verification device.

[0037] The biometric verification device 10 can be configured to: obtain ambient light information of the environment in which the biometric verification device is currently located, and determine the ambient light color bias type of the environment based on the ambient light information; determine ambient light compensation color information for mitigating the color bias of the ambient light color bias type based on the ambient light color bias type; generate a color-compensated image based on the ambient light compensation color information and display the color-compensated image; in response to a biometric verification payment instruction, capture a biometric image under mixed ambient light in the environment in which the biometric verification device is located that compensates for the reflected light of the color-compensated image; and execute a payment operation when identity verification based on the biometric image is successful. The biometric verification device 10 can include a mobile phone, an in-vehicle device, an aircraft, a tablet computer, a laptop computer, or a personal computer (PC). A client can also be provided on the biometric verification device 10, which can be an application client or a browser client.

[0038] The server 11 can be configured to: receive ambient light information transmitted by the biometric verification device 10 to determine the ambient light color deviation type corresponding to the ambient light information; determine ambient light compensation color information for mitigating the color deviation of the ambient light color deviation type based on the ambient light color deviation type; generate a color-compensated image based on the ambient light compensation color information, and transmit the color-compensated image to the biometric verification device 10 for display. The server 11 can also receive a biometric image captured by the biometric verification device 10 to authenticate the biometric image and return the verification result to the biometric verification device 10. The server 11 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0039] The steps of generating the color compensation image in the server 11 may also be performed by the biometric verification device 10 .

[0040] The payment method provided in the embodiments of the present application relates to computer vision technology in the field of artificial intelligence.

[0041] Artificial Intelligence (AI) refers to the theories, methods, techniques, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, to perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results. In other words, AI is a comprehensive field of computer science that seeks to understand the essence of intelligence and produce new intelligent machines that can respond in a manner similar to human intelligence. AI also studies the design principles and implementation methods of various intelligent machines, enabling them to possess the capabilities of perception, reasoning, and decision-making. AI technology is an interdisciplinary discipline encompassing a wide range of fields, encompassing both hardware and software technologies. AI software technologies primarily include computer vision, speech processing, natural language processing, machine learning / deep learning, autonomous driving, and smart transportation.

[0042] Computer vision (CV) is the science of making machines "see." Specifically, it refers to machine vision, where cameras and computers replace the human eye in identifying and measuring targets, and further image processing is performed to transform the computer-generated images into images more suitable for human observation or transmission to instruments for detection. As a scientific discipline, computer vision studies related theories and technologies, attempting to build artificial intelligence systems that can extract information from images or multidimensional data. Computer vision technologies typically include image processing, image recognition, image semantic understanding, image retrieval, optical character recognition (OCR), video processing, video semantic understanding, video content / behavior recognition, three-dimensional object reconstruction, 3D technology, virtual reality, augmented reality, simultaneous positioning and mapping, autonomous driving, and smart transportation. It also includes common biometric recognition technologies such as facial recognition, palm print recognition, and fingerprint recognition.

[0043] It should be noted that the order of description of the following embodiments is not intended to limit the preferred order of the embodiments.

[0044] This embodiment will be described from the perspective of a payment device, which can be specifically integrated into a biometric verification device.

[0045] It is understandable that in the specific implementation of this application, related data such as user information is involved. When the above embodiments of this application are applied to specific products or technologies, user permission or consent is required, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0046] As shown in Figure 2, the specific process of this payment method can be as follows:

[0047] 101. Obtain ambient light information of the environment in which the biometric verification device is currently located, and determine the color bias type of the ambient light of the environment based on the ambient light information.

[0048] Specifically, the biometric verification device may be a user identification device such as a palm verification device or a facial verification device. These devices are based on biometric recognition technology and utilize computer vision and pattern recognition technologies to verify user identity. The biometric verification device can generally be placed in an offline merchant store, and the current environment of the biometric verification device may be the offline merchant store where it is placed.

[0049] Among them, ambient light information (Environmental Light Information) is information about the characteristics and properties of light in the surrounding environment, which may include the brightness, color temperature, hue, etc. of the ambient light. By analyzing the ambient light information, the color cast of the current environment can be obtained. Specifically, the ambient light information of the current environment can be obtained through various sensors, such as photometers, cameras, light sensors, etc. These sensors can measure parameters such as the intensity and color of light and convert them into usable digital signals. In some embodiments, the ambient light information of the current environment can be obtained by collecting environmental images.

[0050] Environmental Light Color Bias Type refers to the color bias exhibited by ambient light, including blue, red, and green. A blue bias refers to a higher blue chromaticity component. This blue bias has a relatively high component value in the RGB color model. In the CIELAB color space, this blue bias manifests as a negative shift in the a value (ranging from red to green) and a relatively stable b value (ranging from blue to yellow), resulting in a blue-dominated color trend. A red bias refers to a higher red component value in the RGB color model. In the CIELAB color space, this red bias manifests as a significant positive shift in the a value and relatively little change in the b value, resulting in a red-dominated color characteristic. A green bias refers to a prominent green component value in the RGB color model. In the CIELAB color space, this green bias manifests as a slight negative shift in the a value (but not as pronounced as a blue bias) and a moderate positive shift in the b value, resulting in a green-dominated color trend.

[0051] Specifically, the characteristics of ambient light can affect the accuracy of tasks such as identity detection and recognition. The present application can analyze the ambient light color deviation type corresponding to the ambient light information, and then generate a color compensation image based on the ambient light color deviation type, thereby combining the diffuse reflection of the color compensation image to collect biometric images to obtain better image quality.

[0052] Optionally, in this embodiment, the step of "obtaining ambient light information of the environment in which the biometric verification device is currently located, and determining the ambient light color bias type of the environment based on the ambient light information" may include: collecting an environmental image of the environment in which the biometric verification device is currently located; performing color analysis on the environmental image to determine the ambient light color bias type of the environmental image.

[0053] The environmental image may be an image captured by a front camera of the biometric verification device. Specifically, the biometric verification device is integrated with a display screen and camera hardware, and the camera hardware is located on the same side of the display screen of the biometric verification device.

[0054] The collected environment image may include ambient light information. Specifically, the environment image may be an image in an RGB (red, green, blue) color mode.

[0055] Optionally, in this embodiment, the step of "performing color analysis on the environmental image to determine the ambient light color bias type of the environmental image" may include: performing color space conversion on the environmental image to obtain a converted image; dividing the converted image into regions to obtain multiple image regions; and determining the ambient light color bias type of the environmental image based on the color bias information of each image region.

[0056] The color space conversion of the environment image may be performed, specifically, converting the environment image from the RGB color space to another color space, such as YCbCr or Lab. These color spaces can separate information such as brightness and chromaticity, and can better represent the color characteristics of the image. The converted image can be an image in the YCbCr or Lab color space.

[0057] YCbCr is a color space where Y represents the luminance component, Cb the blue chrominance component, and Cr the red chrominance component. The Lab color model consists of three color components: luminance (L) and a and b. L represents luminosity, equivalent to brightness; a represents the range from red to green; and b represents the range from blue to yellow.

[0058] The converted image is divided into regions to analyze the color bias information of each region. There are various ways to divide the converted image into regions. For example, the region division method may be to divide the converted image into N image regions based on a preset number N. These N image regions may be of the same or different sizes. Another example is to divide the converted image into image regions of multiple preset sizes.

[0059] Optionally, in this embodiment, the step of "determining the ambient light color bias type of the ambient image based on the color bias information of each image area" may include: calculating the regional color temperature information of the image area for each image area; determining the color bias type of the image area based on the regional color temperature information of the image area; performing statistics on the color bias type of each image area to obtain the frequency information of the occurrence of each preset color bias type; and determining the ambient light color bias type of the ambient image based on the frequency information.

[0060] The color deviation type of the ambient light of the entire ambient image can be determined based on the color deviation information of each image region.

[0061] The preset color bias types may include bluish, reddish, greenish, etc.

[0062] Specifically, the higher the color temperature, the bluish the light; the lower the color temperature, the redder the light. The regional color temperature information of an image region can reflect the color cast of the ambient light. In this embodiment, the regional color temperature information of an image region can specifically be the average color temperature of that image region. By statistically analyzing the color cast types of each image region, the most frequently occurring color cast type is determined and used as the ambient light color cast type of the ambient image.

[0063] In one specific embodiment, the converted image can be divided into several small regions, for example, 8x8 or 16x16 blocks, each representing an image region. The average color temperature or color deviation of each image region is then calculated; this can better accommodate local color variations within the image. Statistical analysis is then performed on the average color temperature or color deviation of each image region to identify the most common color deviation type within the image (e.g., overall blue, red, green, etc.), ultimately determining the deviation of the current image.

[0064] 102. Determine, based on the ambient light color deviation type, ambient light compensation color information for alleviating the color deviation of the ambient light color deviation type.

[0065] Environmental Light Compensation Color Information: This refers to color information determined based on the ambient light color bias type using a color overlay algorithm, and is used to mitigate the color bias of that ambient light color bias type. For example, when the ambient light is bluish, the ambient light compensation color information can be red. By adding a color compensation image of the corresponding color to form diffusely reflected light, the color cast of the ambient light is neutralized.

[0066] Color Superposition Algorithm: Based on the color addition principle of RGB color space, in RGB color mode, when red, green, and blue are mixed in the same proportion, their color components cancel each other out, and the mixed color appears white. However, the superposition effect of different colors depends on the combination of their components in the RGB color space. This algorithm calculates the compensating color information corresponding to the color bias type of the ambient light to neutralize the color cast of the ambient light.

[0067] Specifically, the biometric verification device may determine ambient light compensation color information for alleviating the color bias of the ambient light color bias type using a color overlay algorithm based on the ambient light color bias type. The color bias alleviating the ambient light color bias type may be a color bias that harmonizes the ambient light color bias type.

[0068] Optionally, in this embodiment, the step of "determining ambient light compensation color information for reducing the color bias of the ambient light color bias type based on the ambient light color bias type" may include: obtaining a color compensation mapping relationship set based on a color overlay algorithm, the color compensation mapping relationship set including a mapping relationship between each preset color bias type and the ambient light compensation color information; determining the ambient light compensation color information for reconciling the ambient light color bias type based on the color compensation mapping relationship set.

[0069] The color overlay algorithm is based on the principle of color addition in the RGB color space. In the RGB color mode, when red, green, and blue are mixed in equal proportions, their color components cancel each other out, resulting in a white color. However, not all colors combined will produce white. The effect of different color overlays depends on the combination of their components in the RGB color space.

[0070] The color compensation mapping relation set may be a relation table between a preset color bias type and ambient light compensation color information, and the corresponding ambient light compensation color information is searched from the relation table based on the ambient light color bias type.

[0071] Preset Color Bias Type: This is a pre-set color bias type that may appear in ambient light, including blue, red, and green. By statistically analyzing the color bias types of each image area, the most frequently occurring preset color bias type is determined and used as the ambient light color bias type of the ambient image.

[0072] Specifically, when the preset color bias is bluish, the ambient light compensation color information can be red. A red color compensation image is added to form diffusely reflected light to neutralize the blue ambient light. When the ambient light is bluish, a red color compensation image is generated, and its diffusely reflected light can neutralize the blue ambient light. When the preset color bias is yellowish, the ambient light compensation color information can be blue. A blue color compensation image is added to form diffusely reflected light to neutralize the yellow ambient light. When the ambient light is yellowish, a blue color compensation image is generated, and its diffusely reflected light can neutralize the yellow ambient light.

[0073] It should be noted that the ambient light compensation color information here does not require a clear color value. Essentially, it only needs to generate a color compensation image of the corresponding color system. In this embodiment, the diffuse reflection of the color compensation image is used to adjust the color cast of the ambient light, which can help improve the quality of the collected biometric image.

[0074] 103. Generate a color-compensated image based on the ambient light compensation color information, and display the color-compensated image.

[0075] In some embodiments, the color-compensated image may be a pure color image whose color is determined based on the ambient light compensation color information; in other embodiments, the ambient light compensation color information may be used as the background color of the color-compensated image, and other image elements may be added.

[0076] Optionally, in this embodiment, the step of "generating a color compensation image based on the ambient light compensation color information, and displaying the color compensation image" may include: performing object identification on the environmental image to obtain the type of item currently promoted in the environment where the biometric verification device is located; generating a color compensation image based on the ambient light compensation color information and the item type, and displaying the color compensation image.

[0077] Specifically, the environment in which the biometric verification device is located may be an offline merchant store, and the environmental image may include some commodities sold by the offline merchant store. By performing item recognition on the environmental image, the type of items currently promoted and sold by the offline merchant store can be determined, thereby generating a color compensation image based on the ambient light compensation color information and the item type; for example, the ambient light compensation color information can be used as the background color of the picture, and then relevant image elements of the item type can be added to generate a color compensation image. The generated color compensation image can be used as a poster for the offline merchant store, so as to promote these types of commodities through the poster.

[0078] Item Type: Refers to the type of merchandise currently being promoted in the environment where the biometric verification device is located. This is determined by performing item recognition processing on the environmental image, utilizing AI image recognition technology, extracting features through an image recognition model, and performing support vector machine classification. This can be used to generate color-compensated images, such as by adding relevant image elements, to promote merchandise on posters in offline stores.

[0079] Optionally, in some embodiments, generating a color-compensated image based on ambient light compensation color information and object type, and displaying the color-compensated image may include: inputting the ambient light compensation color information and object type into an artificial intelligence image generation model; and generating a color-compensated image based on the ambient light compensation color information and object type through the artificial intelligence image generation model.

[0080] Among them, AI image recognition technology can be used to identify objects in environmental images. Specifically, the image recognition model can be used to extract features of the environmental image to obtain image feature information, and then classification algorithms such as support vector machines (SVM) can be used to predict the category of objects in the environmental image based on the image feature information.

[0081] Among them, the image recognition model (Image Recognition Model): such as a convolutional neural network (CNN), is used to extract features from images and obtain image feature information to perform image recognition related operations, such as identifying and classifying objects in environmental images. The image recognition model can be a convolutional neural network (CNN), etc., and this embodiment does not limit this. Convolutional neural network (CNN): An image recognition model that can be used to perform operations such as feature extraction on images, such as extracting features from environmental images in AI image recognition technology to identify objects therein.

[0082] The biometric verification device or server can convert the ambient light compensation color information into corresponding color parameter data and the item type into corresponding feature identification data, which are then input into an artificial intelligence image generation model, such as a convolutional neural network. The model first extracts features from the input data and fuses the features of the ambient light compensation color information with those of the item type. Based on these fused features, the model then generates corresponding image elements, such as a product image element corresponding to the item type and an image background color determined by the ambient light compensation color information. Finally, the generated image elements are arranged and synthesized to produce a color-compensated image.

[0083] Optionally, in this embodiment, the step of "generating a color compensation image based on the ambient light compensation color information and displaying the color compensation image" may include: generating an initial compensation image based on the ambient light compensation color information and the type of item; obtaining a marketing demand selection instruction; when the marketing demand selection instruction indicates not to conduct marketing, determining the initial compensation image as the color compensation image to be displayed, and displaying the color compensation image.

[0084] Among them, in this embodiment, after the initial compensation image is generated based on the ambient light compensation color information and the type of object, the user can be prompted whether to enter marketing information to further regenerate the poster, and the marketing demand selection instruction input by the user can be obtained. If the user chooses not to conduct marketing, the initial compensation image can be directly used as the final color compensation image for display; if the user chooses to conduct marketing, the image needs to be regenerated.

[0085] Marketing Requirement Selection Instruction: An instruction entered by the user to indicate whether to conduct marketing. After the biometric verification device generates an initial compensated image based on the ambient light compensation color information and the item type, it prompts the user to enter this instruction. If the instruction is not to conduct marketing, the initial compensated image is directly displayed. If the instruction is to conduct marketing, marketing information must be obtained to regenerate the targeted promotional content.

[0086] Optionally, in this embodiment, the payment method may further include: obtaining marketing information when the marketing demand selection instruction indicates marketing; generating and displaying target promotion content based on ambient light compensation color information, item type and marketing information.

[0087] Among them, when the user chooses to conduct marketing, the marketing information configured by the user can be obtained. Marketing information (Marketing Information) can be marketing information for a certain product, which can specifically include the marketing title of the product, the picture of the product, the date of the event, and the discount amount, etc. After obtaining the marketing information, the ambient light compensation color information can be used as the background color of the picture, and then the marketing information and the item type corresponding to the environmental image can be integrated to generate target promotion content. Target Promotion Content (Target Promotion Content): When the user chooses to conduct marketing, the content used for promotion is generated based on the ambient light compensation color information, the item type and the marketing information, such as using the ambient light compensation color information as the background color of the picture, integrating the marketing information and the item type corresponding to the environmental image.

[0088] Optionally, in this embodiment, the step of "generating a color compensated image based on the ambient light compensation color information and displaying the color compensated image" may include: determining the promotional image style information for the environment in which the biometric verification device is located based on the ambient light compensation color information and the type of object; obtaining the original promotional image; fusing the promotional image style information with the original promotional image to obtain a color compensated image; and displaying the color compensated image.

[0089] Among them, the original promotional image can be a poster that is initially uniformly developed by the official or merchant; the promotional image style information is specifically an electronic poster style template suitable for the environment in which the biometric verification device is located, which can include ambient light compensation color information and the type of items in the environment in which the biometric verification device is located.

[0090] Among them, in specific scenarios, the electronic posters of user identity recognition devices are generally formulated uniformly by the official or merchants, but the environment of offline segmented merchant stores is complex and the products are diversified. The cost of merchant-side investigation and adaptation is too high. The biometric verification device of this application can automatically learn environmental knowledge and generate electronic posters (color compensation images) that are more attractive to users; specifically, by collecting environmental images, it can determine the ambient light information and the type of goods sold in the store, and then determine the ambient light compensation color information used to harmonize the ambient light information, so as to generate a color compensation image based on the ambient light compensation color information and the product type. The color compensation image generated in this way can be more attractive to users. Moreover, during the user payment process, the ambient light can be harmonized through the diffuse reflection of the color compensation image, which is conducive to improving the quality of the collected biometric image.

[0091] There are many ways to fuse the promotional image style information with the original promotional image, which are not limited in this embodiment. For example, the fusion method may be to add the promotional image style information to the original promotional image to obtain a color-compensated image.

[0092] 104. In response to the biometric verification payment instruction, a biometric image is captured under mixed ambient light in an environment where the biometric verification device is located that compensates for reflected light of the color-compensated image.

[0093] The biometric verification device can respond to the user's biometric verification payment instruction for the resource to be traded, and collect the user's biometric image under mixed ambient light in the environment where the biometric verification device is located, which compensates for the reflected light of the color compensation image.

[0094] Biometric Image: When the biometric verification device is a facial verification device, the biometric image can be a face image; when the biometric verification device is a palm verification device, the biometric image can be a palm print image. It is an image used for identity verification and identification.

[0095] Mixed Ambient Light: This refers to the light created by mixing the original ambient light and the reflected light from the color-compensated image in the biometric verification device's environment. This mixed light improves the color cast of the original ambient light, facilitating the capture of higher-quality biometric images.

[0096] In this embodiment, the reflected light generated by the color compensation image can be used to neutralize the color cast of the ambient light, thereby improving the color cast of the collected biometric image, which is beneficial to improving the quality of the biometric image.

[0097] The user can be the person conducting the transaction, specifically the person purchasing the product. A biometric verification payment instruction is an instruction issued by a user to perform biometric verification to complete payment for a resource to be traded (e.g., an item to be purchased). Resources to be traded are items that a user wishes to purchase, for which a user issues a biometric verification payment instruction to complete payment.

[0098] Specifically, when the biometric verification device is a face verification device, the biometric image may be a face image; when the biometric verification device is a palm verification device, the biometric image may be a palm print image.

[0099] 105. When the identity verification based on the biometric image is passed, the payment operation is performed.

[0100] After the biometric image is captured, it needs to be authenticated and recognized. Once the authentication is passed, the payment operation can be carried out, thus entering the payment process. Payment Process: After the biometric image authentication is passed, the payment process is carried out in a series of steps, including deducting the corresponding amount from the user's account and paying the amount to the recipient.

[0101] Specifically, before the biometric verification device configures the poster (i.e., the color compensation image) based on the environmental information, the biometric verification device needs to be initialized and the poster update capability configured as follows:

[0102] After the merchant store has installed the biometric verification device, it will be turned on and started. The biometric verification device will enter the initialization phase after startup. The biometric verification device can be bound to the payee based on the payee's user identity information (such as palm print information or facial information, etc.); when the binding is successful, the poster update capability of the biometric verification device can be configured.

[0103] In a specific scenario, the biometric verification device can be a palm verification device (specifically, a palm verification device), which can be used in offline merchant stores. As shown in Figure 3, the biometric verification device can be integrated with a camera and a display screen. The camera corresponds to the palm image acquisition area and can be used to collect palm print images. When a customer purchases a certain item, the palm print can be scanned in the palm image acquisition area of ​​the biometric verification device. The biometric verification device collects the customer's palm print image through the camera and then performs identity verification on the palm print image. When the verification is successful, the corresponding payment process is carried out. Finally, when the payment is completed, the payment result, such as the payment amount and successful payment information, can be displayed on the display screen of the biometric verification device. Palm Image Acquisition Area: The area corresponding to the camera on the palm verification device. The user scans the palm print in this area so that the palm verification device can collect the palm print image.

[0104] Specifically, the process of initializing the palm verification device, configuring the poster update capability, and palm verification payment can be shown in FIG4 and described as follows:

[0105] First, after the merchant store has installed the palm verification device, the cashier needs to turn on the palm verification device. After turning on, the palm verification device will enter the initialization stage, and then prompt the cashier to further collect the palm image for binding. After the cashier swipes the palm, the palm verification device will display the QR code information uniquely bound to the device. The QR code information is specifically the string serial number SN of the palm verification device. SN is the ID (Identification) that can uniquely identify a device; the cashier then uses the logged-in instant messaging application (App, Application) to scan the QR code to bind the current cashier application identity device relationship. After the binding is successful, the cashier needs to authorize the palm verification device to self-learn and update posters on the mini program on the mobile application side (generally, this capability is not enabled by default). At this point, the initialization of the palm verification device and the configuration of the poster update capability are completed. After the palm verification device successfully enables the self-learning and poster update capability, the successful activation result can be fed back to the cashier's mobile application. Mini-Program: A lightweight application that runs on platforms such as instant messaging applications. Cashiers can use the mini-program on their mobile phone to authorize their palm verification device to self-learn and update posters, enter marketing information, and perform other operations.

[0106] After the cashier configures the palm verification device to allow self-learning and updating of posters, the palm verification device will collect the current ambient light and the current environment type after it is turned on. In essence, the palm verification device takes several pictures of the current environment in its spare time and then uploads them to the application backend service for analysis and identification to obtain information about the lighting conditions of the store where the device is currently located and the types of goods sold.

[0107] Among them, the Application Back-end Service is responsible for receiving ambient light information, environmental images and other data uploaded by the biometric verification device, performing analysis and processing, such as determining the ambient light type, identifying the product type, generating electronic posters, etc., and returning the processing results to the biometric verification device. The purpose of collecting ambient light and sending it to the application back-end service for analysis is mainly to determine the current ambient light type, because different ambient color cast light scenes will cause the accuracy and pass rate of the palm verification algorithm to decrease, so it is necessary to compensate with color light. The palm imaging is harmonized mainly by taking advantage of the fact that the screen of the palm verification device also emits light, and posters of different colors will produce different colors of light for diffuse reflection, further affecting the final imaging of the current user's palm. By combining the ambient light to analyze the current ambient light color cast, and then combining it with the color light of the electronic poster, the color of the palm image can be harmonized.

[0108] Among them, the types of goods sold in the current store can be analyzed through store photos. Specifically, it is necessary to pre-process the store photos collected by the palm image acquisition camera, and then use AI image recognition technology to identify the goods in the store photos. For example, a convolutional neural network can be used to extract features from store photos, and classification algorithms such as support vector machines (SVM) can be used to classify the goods; then the identified goods are classified. Among them, the goods can be classified using a product classification database, or a method based on deep learning can be used to classify the goods. Finally, the types of goods sold in the store are analyzed based on the identified goods and the product classification; specifically, statistical methods can be used to classify and count the goods to obtain the types of goods sold in the store.

[0109] Commodity Classification Database: A database used to classify commodities. It can classify and count the identified commodities according to the classification rules to obtain the types of commodities sold in the store.

[0110] After receiving the ambient light type and the current store's product category, the backend service generates a corresponding electronic poster and returns it to the palm verification device. A quick poster replacement entry will then appear on the default poster interface. Clicking this prompts you to enter marketing products and regenerate the poster. If you choose not to enter, you can only use the current electronic poster generated based on the ambient light and store category.

[0111] If the cashier chooses to enter a marketing product, they will need to open the product entry template in the palm verification applet on the mobile app and fill in the product's marketing title, product image, event date, discount amount, etc. After the cashier enters the information, the current information will be passed to the application backend service. At the same time, the device will continue to query the marketing activity information configured by the cashier currently bound to the device. If the query is found, the previous ambient light, product type, and current marketing product information will be sent to the application backend service for AI GC machine analysis and generation of corresponding posters. The final result will be returned to the palm verification device. Among them, AIGC stands for Artificial Intelligence Generative Content.

[0112] Afterwards, when the user goes to the store, he or she can quickly obtain the current marketing and other related product information through the current electronic poster. At the same time, after confirming the product information, the user can perform palm verification payment. When collecting the palm image, it will be combined with the current ambient light and the light reflected by the electronic poster for fusion shooting, and then the current palm print image will be sent to the application backend service for palm print recognition. Specifically, the palm verification payment process can be: calling the 3D camera to collect the user's current palm streaming media data. After obtaining the streaming media, the biometric verification device optimizes the streaming media; preferably, the best palm image is selected through a comprehensive evaluation of coefficient indicators such as palm size, angle, image contrast, image brightness and clarity. The best palm image is then sent to the application backend service for palm recognition, and further information related to the user payment code corresponding to the palm is obtained.

[0113] The 3D (three-dimensional) camera is similar to a traditional camera, but with added liveness-related hardware and software, including a depth camera and an infrared camera, to ensure information security. Palm recognition uses multimedia information from the palm of your hand to exchange for personal identity information. Palm streaming data: Dynamic data of the user's palm collected by the 3D camera. Biometric verification equipment can optimize this data and select the optimal palm image for identity verification.

[0114] This application provides a solution for generating dynamic electronic posters based on environmental self-learning. Specifically, by combining the store's ambient light, product type, and cashier-related marketing strategies, it can generate electronic posters that are most suitable for the current store equipment. This provides a better business experience while supporting the merchant's marketing appeal. Furthermore, during the user's payment process, the diffuse reflection generated by the electronic poster generated by this method can be used to adjust the color cast of the ambient light, which is conducive to the collection of higher-quality biometric images.

[0115] Specifically, this application is to combine biometric verification equipment (palm verification equipment or facial verification equipment, etc.) after it is deployed in merchant stores, and use cameras to identify and classify environmental products and lights to obtain the electronic poster style template that is currently most suitable for display in merchant stores (suitable for marketing, and at the same time integrate environmental lights to generate electronic poster styles that are more suitable for palm fill light recognition), and then combine the cashier's periodic product marketing demands for the store, take pictures, etc., and generate the final electronic poster content suitable for the store through AIGC.

[0116] For example, when the color bias corresponding to the ambient light is detected as bluish, the corresponding ambient light compensation color information can be determined to be red based on the color compensation mapping relationship set. This allows a red electronic poster to be generated and displayed, as shown in Figure 5. When the ambient light is detected as bluish, a red electronic poster is generated and displayed. Its diffuse reflection can neutralize the blue ambient light, thereby improving the quality of palmprint image acquisition. When a user performs palm verification payment in front of the palm image acquisition area, the diffuse reflection generated by the red electronic poster can offset the bluish ambient light, allowing the palmprint image to be captured, thereby improving the color cast of the captured palmprint image.

[0117] For example, if the color bias corresponding to the detected ambient light is yellowish, the corresponding ambient light compensation color information can be determined to be blue based on the color compensation mapping relationship set. This allows a blue electronic poster to be generated and displayed, as shown in Figure 6. When a user performs palm verification payment in front of the palm image collection area, the diffuse reflection generated by the blue electronic poster can be used to offset the yellowish ambient light, thereby allowing the palm print image to be collected, thereby improving the color cast of the collected palm print image.

[0118] The specific process of generating a poster through AIGC can be as follows:

[0119] The product type and ambient light color bias are input into an AI model, which can be an image generation model, specifically a convolutional neural network. The AI ​​model uses the product type as a keyword to generate corresponding product elements. Based on the ambient light color bias, it generates a corresponding background layer. The product elements are superimposed on the background layer, and the size and position of the product elements are controlled. Fine-tuning the layout completes the poster creation.

[0120] Image Generation Model: A type of AI model that generates image content based on input information, such as product type and ambient light color bias. For example, in this application, it is used to generate color-compensated images or posters.

[0121] Commodity Element: refers to a commodity-related image element generated based on the commodity type. For example, using the commodity type "children's short-sleeved shirt" as a keyword, various styles of children's short-sleeved shirt image elements are generated, which can be used to create images such as posters.

[0122] Background Layer: It is the background part of images such as posters generated according to the color bias of the ambient light. For example, when the color bias of the ambient light is yellowish, the compensation color information is determined to be blue, and the background layer can be set to blue.

[0123] For example, if the product type is short-sleeved children's clothing, you can directly use the product type "children's short-sleeved" as a keyword to generate corresponding clothing materials (i.e., the aforementioned product elements). This clothing material can be image elements of various styles of children's short-sleeved shirts. If the ambient light color bias type is yellowish, you can determine the ambient light compensation color information to be blue, set the background layer to blue, and then overlay the children's short-sleeved shirt image element on the blue background layer. Then adjust the layout of the image elements to complete the poster creation.

[0124] Specifically, when relevant marketing is required, marketing information can be input into the AI ​​model together with the product type and ambient light color bias type, and the marketing information can be integrated with the preset marketing template to obtain a fused marketing template image. The marketing information can include the product's marketing title, product image, event date, discount amount, etc. The preset marketing template can be a template image that includes various marketing information columns. The specific integration process of the marketing information and the preset marketing template can be: fill each marketing information into the corresponding marketing information column to obtain a fused marketing template image. The background color of the fused marketing template image is then adjusted to the ambient light compensation color information to obtain an adjusted marketing image. Finally, the product elements corresponding to the product type are superimposed on the adjusted marketing image to complete the poster production.

[0125] Fused Marketing Template Image: is an image obtained by fusing marketing information with a preset marketing template. Specifically, it is generated by filling various marketing information into corresponding marketing information columns of the preset marketing template.

[0126] Preset Marketing Template: A template image that includes various marketing information columns and is used to integrate with marketing information. For example, marketing information such as the product's marketing title, product image, event date, discount amount, etc. is filled into the corresponding marketing information columns to obtain a fused marketing template image.

[0127] In specific scenarios, sales data from POS (Point of Sales Terminal) machines or palm-face verification devices can also be obtained, and combined with this sales data to display hot-selling products or recommended products on posters (such as discounts, promotions, etc.). For example, sales data for the most recent month can be obtained. By analyzing this sales data, information such as sales volume, sales volume, and sales profit of various commodities can be obtained. Based on this information, the hot-selling products and products with the highest sales profit in the most recent month can be determined. In the process of generating posters through AIGC, corresponding image elements are generated based on these commodities and integrated into the poster to display these commodities. Hot-selling commodities: Commodities that have outstanding performance in sales volume, sales volume, and other indicators within a certain period of time, as determined by analyzing sales data, can be displayed on posters to attract customers.

[0128] As shown in Figure 7, this is the business architecture diagram of the present application. Specifically, after the palm verification device is deployed in the merchant store, the cashier can log in to the cashier's communication account through the login module of the instant messaging application on the cashier's mobile phone, and then scan the QR code information on the palm verification device through the scanning module of the instant messaging application. The palm verification device is bound to the cashier through the palm verification applet, and the cashier can also authorize the palm verification device to automatically update the poster. After the palm verification device is configured with the poster automatic update capability, the information collection module can use the palm image acquisition camera on it to collect ambient light information, thereby determining the ambient light compensation color information corresponding to the ambient light information, and the palm image acquisition camera can be used to collect the type of goods in the store, so as to generate a color compensation image (electronic poster) based on the ambient light compensation color information and the type of goods through the poster generation service provided by the poster module. When the cashier chooses to market a product, a color-compensated image needs to be regenerated. The palm verification device can obtain the marketing information configured by the cashier through the marketing product generation template, and generate a new color-compensated image based on the marketing information, ambient light compensation color information, and product type through the poster generation service, thereby improving the display of the color-compensated image by the picture carousel module in the poster module on the palm verification device. When the user purchases the product and pays, the diffuse reflection generated by the color-compensated image can be used to neutralize the color cast of the ambient light, thereby collecting the palm print image. The palm verification service provided by the palm verification device performs optimal biopsy and identity verification on the palm print image to determine the user's identity information, and then payment is made through the payment service. Optionally, the palm verification device can also provide a Bluetooth module, which can be used to communicate with the cashier's mobile phone.

[0129] Optimal Biopsy: The process by which the biometric verification device screens collected palm streaming data, selecting the optimal palm image for subsequent identity verification through a comprehensive evaluation of factors such as palm size, angle, image contrast, brightness, and clarity. Marketing Commodity Generation Template: A template that cashiers open in the palm verification mini-program on their mobile app. This template is used to fill in marketing information such as the product's marketing title, image, event date, and discount amount, in order to regenerate a poster containing marketing content.

[0130] As can be seen from the above, this embodiment can obtain the ambient light information of the environment in which the biometric verification device is currently located, and determine the ambient light color bias type of the environment based on the ambient light information; determine the ambient light compensation color information for reducing the color bias of the ambient light color bias type based on the ambient light color bias type; generate a color compensation image based on the ambient light compensation color information, and display the color compensation image; in response to the biometric verification payment instruction, collect a biometric image under the mixed ambient light in the environment in which the biometric verification device is located that compensates for the reflected light of the color compensation image; when the identity authentication based on the biometric image is passed, execute the payment operation.

[0131] The present application can generate a color compensation image for reconciling the color deviation type of the ambient light corresponding to the current ambient light, so that when the user makes a payment, the reflected light of the color compensation image can be combined to collect the user's biometric image. This can reconcile the color deviation of the collected biometric image, which is beneficial to improving the quality of the biometric image, thereby ensuring the accuracy and efficiency of the user's identity verification and improving payment efficiency.

[0132] The method described in the above embodiment is further described below.

[0133] This embodiment of the present application provides a payment method, as shown in FIG8 , the specific process of the payment method can be as follows:

[0134] 801. The biometric verification device collects an environmental image of the environment in which the biometric verification device is currently located.

[0135] The biometric verification device may be a user identification device such as a palm verification device or a facial verification device. The biometric verification device may generally be placed in an offline merchant store, and the current environment of the biometric verification device may be the offline merchant store where it is placed.

[0136] The environmental image may be an image captured by a front camera of the biometric verification device. Specifically, the biometric verification device is integrated with a display screen and camera hardware, and the camera hardware is located on the same side of the display screen of the biometric verification device.

[0137] The collected environment image may include ambient light information. Specifically, the environment image may be an image in an RGB (red, green, blue) color mode.

[0138] 802. The biometric verification device performs color analysis on the environment image to determine the color bias type of the ambient light of the environment image.

[0139] Optionally, in this embodiment, the step of "performing color analysis on the environmental image to determine the ambient light color bias type of the environmental image" may include: performing color space conversion on the environmental image to obtain a converted image; dividing the converted image into regions to obtain multiple image regions; and determining the ambient light color bias type of the environmental image based on the color bias information of each image region.

[0140] Converted Image: is the image obtained after color space conversion of the environment image. Specifically, the environment image can be converted from RGB color space to color space such as YCbCr or Lab. These color spaces can better represent the color characteristics of the image and separate information such as brightness and chromaticity.

[0141] The color space conversion is performed on the environment image, specifically, the environment image is converted from the RGB color space to other color spaces, such as YCbCr or Lab.

[0142] Optionally, in this embodiment, the step of "determining the ambient light color bias type of the ambient image based on the color bias information of each image area" may include: calculating the regional color temperature information of the image area for each image area; determining the color bias type of the image area based on the regional color temperature information of the image area; performing statistics on the color bias type of each image area to obtain the frequency information of the occurrence of each preset color bias type; and determining the ambient light color bias type of the ambient image based on the frequency information.

[0143] The preset color bias types may include bluish, reddish, greenish, etc.

[0144] Specifically, the higher the color temperature, the bluish the light, while the lower the color temperature, the redder the light. The regional color temperature information of the image area can reflect the color cast of the ambient light.

[0145] 803. The biometric verification device determines, based on the ambient light color deviation type, ambient light compensation color information for alleviating the color deviation of the ambient light color deviation type.

[0146] Optionally, in this embodiment, the step of "determining ambient light compensation color information for reducing the color bias of the ambient light color bias type based on the ambient light color bias type" may include: obtaining a color compensation mapping relationship set based on a color overlay algorithm, the color compensation mapping relationship set including a mapping relationship between each preset color bias type and the ambient light compensation color information; determining the ambient light compensation color information for reconciling the ambient light color bias type based on the color compensation mapping relationship set.

[0147] The color overlay algorithm, based on the additive principle of the RGB color space, calculates ambient light compensation color information corresponding to the ambient light color bias type to neutralize the ambient light color cast. The color overlay algorithm is based on the additive principle of the RGB color space. In the RGB color mode, when red, green, and blue are mixed in equal proportions, their color components cancel each other out, resulting in a white mixture. However, not all colors will produce white when overlaid; the resulting color overlay effect depends on the combination of their components in the RGB color space.

[0148] 804. The biometric verification device performs object recognition on the environment image to obtain the type of objects currently promoted in the environment where the biometric verification device is located.

[0149] Among them, AI image recognition technology can be used to identify objects in environmental images. Specifically, the image recognition model can be used to extract features of the environmental image to obtain image feature information, and then classification algorithms such as support vector machines (SVM) can be used to predict the category of objects in the environmental image based on the image feature information.

[0150] 805. The biometric verification device generates a color-compensated image based on the ambient light compensation color information and the object type, and displays the color-compensated image.

[0151] Specifically, the environment in which the biometric verification device is located may be an offline merchant store, and the environmental image may include some commodities sold by the offline merchant store. By performing item recognition on the environmental image, the type of items currently promoted and sold by the offline merchant store can be determined, thereby generating a color compensation image based on the ambient light compensation color information and the item type; for example, the ambient light compensation color information can be used as the background color of the picture, and then relevant image elements of the item type can be added to generate a color compensation image. The generated color compensation image can be used as a poster for the offline merchant store, so as to promote these types of commodities through the poster.

[0152] Optionally, in this embodiment, the step of "generating a color compensation image based on the ambient light compensation color information and displaying the color compensation image" may include: generating an initial compensation image based on the ambient light compensation color information and the type of item; obtaining a marketing demand selection instruction; when the marketing demand selection instruction indicates not to conduct marketing, determining the initial compensation image as the color compensation image to be displayed, and displaying the color compensation image.

[0153] Among them, in this embodiment, after the initial compensation image is generated based on the ambient light compensation color information and the type of object, the user can be prompted whether to enter marketing information to further regenerate the poster, and the marketing demand selection instruction input by the user can be obtained. If the user chooses not to conduct marketing, the initial compensation image can be directly used as the final color compensation image for display; if the user chooses to conduct marketing, the image needs to be regenerated.

[0154] Optionally, in this embodiment, the payment method may further include: obtaining marketing information when the marketing demand selection instruction indicates marketing; generating and displaying target promotion content based on ambient light compensation color information, item type and marketing information.

[0155] When a user selects a marketing campaign, the user's configured marketing information can be retrieved. This information can be specific to a specific product and may include the product's marketing title, product image, promotional date, and discount amount. After obtaining this marketing information, the ambient light compensation color information can be used as the image background color, combined with the marketing information and the item type corresponding to the ambient image to generate targeted promotional content.

[0156] 806. When receiving a biometric verification payment instruction from the user for the resource to be traded, the biometric verification device collects a biometric image of the user based on the current ambient light in the environment where the biometric verification device is located and the reflected light of the color compensation image.

[0157] In this embodiment, the reflected light generated by the color compensation image can be used to neutralize the color cast of the ambient light, thereby improving the color cast of the collected biometric image, which is beneficial to improving the quality of the biometric image.

[0158] Specifically, when the biometric verification device is a face verification device, the biometric image may be a face image; when the biometric verification device is a palm verification device, the biometric image may be a palm print image.

[0159] 807. When the biometric image verification passes, the biometric verification device performs a payment operation on the transaction resource.

[0160] Specifically, the characteristics of ambient light can affect the accuracy of tasks such as identity detection and recognition. The present application can analyze the ambient light color deviation type corresponding to the ambient light information, and then generate a color compensation image based on the ambient light color deviation type, thereby combining the diffuse reflection of the color compensation image to collect biometric images to obtain better image quality.

[0161] As can be seen from the above, this embodiment can collect an environmental image of the environment in which the biometric verification device is currently located through the biometric verification device; perform color analysis on the environmental image to determine the ambient light color bias type of the environmental image; determine ambient light compensation color information for reducing the color bias of the ambient light color bias type based on the ambient light color bias type; perform object recognition on the environmental image to obtain the type of object currently promoted in the environment in which the biometric verification device is located; generate a color compensation image based on the ambient light compensation color information and the item type, and display the color compensation image; in response to a biometric verification payment instruction, collect a biometric image under mixed ambient light in the environment in which the biometric verification device is located that compensates for the reflected light of the color compensation image; when the identity authentication based on the biometric image is passed, execute the payment operation.

[0162] The present application can generate a color compensation image for reconciling the color deviation type of the ambient light corresponding to the current ambient light, so that when the user makes a payment, the reflected light of the color compensation image can be combined to collect the user's biometric image. This can reconcile the color deviation of the collected biometric image, which is beneficial to improving the quality of the biometric image, thereby ensuring the accuracy and efficiency of the user's identity verification and improving payment efficiency.

[0163] In summary, the biometric verification device first obtains ambient light information from the current environment and, based on this information, determines the type of ambient light color bias. Subsequently, based on the type of ambient light color bias, the device determines ambient light compensation color information to mitigate this bias and generates a color-compensated image for display. When the user triggers a biometric verification payment instruction, the device captures a biometric image under mixed ambient light that has compensated for the reflected light from the color-compensated image. If identity verification based on this image is successful, the payment operation is executed. Ambient light compensation technology improves the quality of biometric image acquisition, thereby enhancing identity verification accuracy and payment efficiency.

[0164] Furthermore, the payment method based on environmental image analysis captures an image of the current environment and performs color analysis to determine the type of ambient light color bias. Based on this, the device generates and displays a color-compensated image. When a user initiates a biometric verification payment instruction, the device captures a biometric image under mixed ambient light and executes the payment operation after verification. By directly analyzing the ambient image to obtain ambient light information, the accuracy of the ambient light color bias determination is improved, further optimizing the biometric image acquisition process.

[0165] Furthermore, a payment method that incorporates item recognition not only analyzes the ambient light color bias after capturing an environmental image but also uses item recognition to determine the types of items being promoted in the environment. The device then generates a color-compensated image based on the ambient light compensation color information and the item type. When the user pays, the device captures a biometric image under mixed ambient light conditions and completes payment after verification. This combination of item recognition and ambient light compensation not only improves payment efficiency but also enables product promotion through color-compensated images, increasing commercial value.

[0166] Furthermore, the payment method based on AI image generation incorporates ambient light compensation color information and item type into the AI ​​image generation model to generate a color-compensated image. When a user pays, the device captures a biometric image under mixed ambient light and, after verification, executes the payment. Using AI technology to generate color-compensated images increases the flexibility and adaptability of image generation, further enhancing the user experience and image capture quality during the payment process.

[0167] Furthermore, the device uses a color analysis method based on region division to perform color space conversion on the captured ambient image and divide it into multiple image regions. By analyzing the color bias information of each region and counting the frequency of each color bias type, the ambient light color bias type is determined. Based on this, the device generates and displays a color-compensated image. When the user pays, the biometric image is collected under mixed ambient light, and payment is completed after verification. Through region division and frequency counting, the ambient light color bias type is determined more accurately, further optimizing the conditions for collecting biometric images.

[0168] Furthermore, a payment method based on regional color temperature analysis calculates color temperature information for each area of ​​the ambient image, determines the color bias type of each area, and then determines the overall ambient light color bias type by counting the frequency. The device then generates and displays a color-compensated image. When the user pays, the biometric image is captured and, after verification, the payment is executed. Accurately determining the ambient light color bias through color temperature analysis further improves the accuracy of biometric image capture and payment efficiency.

[0169] Furthermore, a payment method based on a color overlay algorithm uses this algorithm to obtain a set of color compensation mapping relationships, determine the compensating color information used to balance the color bias of the ambient light, and generate a color-compensated image. When a user makes a payment, the device captures a biometric image under mixed ambient light and completes the payment after verification. The precise generation of compensating color information through the color overlay algorithm effectively improves the quality of biometric image capture and enhances payment efficiency.

[0170] Furthermore, incorporating a payment method tailored to marketing needs, the device generates an initial compensation image and then receives a marketing selection instruction from the user. If the user chooses not to proceed with marketing, the initial compensation image is displayed directly; if the user chooses to proceed with marketing, marketing information is obtained and targeted promotional content is generated. When the user pays, the device captures a biometric image under mixed ambient light and, upon successful verification, executes the payment. By flexibly adjusting the color compensation image content based on marketing needs, payment efficiency is improved while also meeting commercial promotion needs.

[0171] Furthermore, the payment method based on promotional image fusion uses ambient light compensation color information and item type to determine the promotional image style information, and fuses it with the original promotional image to generate a color-compensated image. When the user pays, the device captures a biometric image under mixed ambient light and completes the payment after verification. This image fusion technology generates a more targeted color-compensated image, further improving image acquisition quality and payment efficiency.

[0172] Furthermore, the payment method device, which is compatible with different biometric verification devices, supports either palm or facial verification. Depending on the device type, palm or facial images are captured as biometric images, and the capture is performed under mixed ambient light. When the user makes a payment, the device executes the payment operation after successful verification. By adapting to different biometric verification devices, the application scope of the payment method is expanded, and the versatility and flexibility of the payment system are enhanced.

[0173] To better implement the above method, an embodiment of the present application further provides a payment device, as shown in FIG9 , which may include an acquisition unit 901 , a determination unit 902 , a generation unit 903 , a collection unit 904 , and an execution unit 905 , as follows:

[0174] (1) Acquisition unit 901:

[0175] The acquisition unit 901 is configured to acquire ambient light information of an environment in which the biometric verification device is currently located, and determine a color deviation type of the ambient light of the environment according to the ambient light information.

[0176] Optionally, in some embodiments of the present application, the acquisition unit 901 may include a collection subunit and a color analysis subunit, as follows:

[0177] The acquisition subunit is used to acquire an environmental image of the environment in which the biometric verification device is currently located.

[0178] The color analysis subunit is used to perform color analysis on the environment image to determine the color deviation type of the ambient light of the environment image.

[0179] Optionally, in some embodiments of the present application, the color analysis subunit can be specifically used to perform color space conversion on the environmental image to obtain a converted image; divide the converted image into regions to obtain multiple image regions; and determine the ambient light color bias type of the environmental image based on the color bias information of each image region.

[0180] Optionally, in some embodiments of the present application, the color analysis subunit can be specifically used to calculate the regional color temperature information of each image area; determine the color bias type of the image area based on the regional color temperature information of the image area; perform statistics on the color bias type of each image area to obtain the frequency information of each preset color bias type; and determine the ambient light color bias type of the ambient image based on the frequency information.

[0181] (2) Determine unit 902:

[0182] The determining unit 902 is configured to determine, according to the ambient light color deviation type, ambient light compensation color information for alleviating the color deviation of the ambient light color deviation type.

[0183] Optionally, in some embodiments of the present application, the determining unit 902 may include a mapping relationship obtaining subunit and a color determining subunit, as follows:

[0184] The mapping relationship acquisition subunit is used to acquire a color compensation mapping relationship set based on a color superposition algorithm, where the color compensation mapping relationship set includes mapping relationships between various preset color bias types and ambient light compensation color information.

[0185] The color determination subunit is configured to determine, according to the color compensation mapping relationship set, ambient light compensation color information for reconciling the ambient light color deviation type.

[0186] (3) Generation unit 903:

[0187] The generating unit 903 is configured to generate a color-compensated image according to the ambient light compensation color information, and display the color-compensated image.

[0188] Optionally, in some embodiments of the present application, the generating unit 903 may include an item identifying subunit and a generating subunit, as follows:

[0189] The item recognition subunit is used to perform item recognition on the environment image to obtain the type of items currently promoted in the environment where the biometric verification device is located.

[0190] The generating subunit is configured to generate a color-compensated image according to the ambient light compensation color information and the object type, and to display the color-compensated image.

[0191] Optionally, in some embodiments of the present application, the generation subunit can be specifically used to generate an initial compensation image based on the ambient light compensation color information and the item type; obtain a marketing demand selection instruction; when the marketing demand selection instruction indicates not to conduct marketing, determine that the initial compensation image is the color compensation image to be displayed, and display the color compensation image.

[0192] Optionally, in some embodiments of the present application, the generating unit 903 may further include a marketing information acquiring subunit and a regenerating subunit, as follows:

[0193] The marketing information acquisition subunit is used to acquire marketing information when the marketing demand selection instruction indicates to conduct marketing.

[0194] The regeneration subunit is configured to generate and display target promotion content based on the ambient light compensation color information, the item type, and the marketing information.

[0195] Optionally, in some embodiments of the present application, the generating subunit may be specifically used to determine the promotional image style information for the environment in which the biometric verification device is located based on the ambient light compensation color information and the item type; obtain an original promotional image; fuse the promotional image style information with the original promotional image to obtain a color compensated image; and display the color compensated image.

[0196] (4) Collection unit 904:

[0197] The acquisition unit 904 is configured to acquire a biometric image in response to a biometric verification payment instruction under mixed ambient light in an environment where the biometric verification device is located that compensates for reflected light of the color-compensated image.

[0198] (5) Execution unit 905:

[0199] The execution unit 905 is configured to execute a payment operation when the identity authentication based on the biometric image is passed.

[0200] As can be seen from the above, in this embodiment, the acquisition unit 901 can acquire the ambient light information of the environment in which the biometric verification device is currently located, and determine the ambient light color deviation type of the environment based on the ambient light information; the determination unit 902 can determine the ambient light compensation color information for reducing the color deviation of the ambient light color deviation type based on the ambient light color deviation type; the generation unit 903 can generate a color compensation image based on the ambient light compensation color information and display the color compensation image; the acquisition unit 904 can respond to the biometric verification payment instruction and collect the biometric image under the mixed ambient light in the environment in which the biometric verification device is located that compensates for the reflected light of the color compensation image; the execution unit 905 can execute the payment operation when the identity authentication based on the biometric image is passed.

[0201] The present application can generate a color compensation image for reconciling the color deviation type of the ambient light corresponding to the current ambient light, so that when the user makes a payment, the reflected light of the color compensation image can be combined to collect the user's biometric image. This can reconcile the color deviation of the collected biometric image, which is beneficial to improving the quality of the biometric image, thereby ensuring the accuracy and efficiency of the user's identity verification and improving payment efficiency.

[0202] The present application also provides a biometric verification device, as shown in FIG10 , which shows a schematic diagram of the structure of the biometric verification device involved in the present application embodiment. Specifically:

[0203] The biometric verification device may include components such as a processor 1001 with one or more processing cores, a memory 1002 with one or more computer-readable storage media, a power supply 1003, and an input unit 1004. Those skilled in the art will appreciate that the structure of the biometric verification device shown in FIG10 does not limit the biometric verification device, and may include more or fewer components than shown, or combine certain components, or arrange the components differently. Among them:

[0204] Processor 1001 is the control center of the biometric authentication device. It connects the various components of the biometric authentication device using various interfaces and circuits. It executes the various functions of the biometric authentication device and processes data by running or executing software programs and / or modules stored in memory 1002 and accessing data stored in memory 1002. Optionally, processor 1001 may include one or more processing cores. Preferably, processor 1001 may integrate an application processor and a modem processor. The application processor primarily handles the operating system, user interface, and application programs, while the modem processor primarily handles wireless communications. It is understood that the modem processor may not be integrated into processor 1001.

[0205] The memory 1002 can be used to store software programs and modules. The processor 1001 executes various functional applications and data processing by running the software programs and modules stored in the memory 1002. The memory 1002 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created based on the use of a biometric verification device, etc. In addition, the memory 1002 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage device. Accordingly, the memory 1002 may also include a memory controller to provide the processor 1001 with access to the memory 1002.

[0206] The biometric verification device also includes a power supply 1003 for supplying power to various components. Preferably, the power supply 1003 can be logically connected to the processor 1001 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption. The power supply 1003 can also include one or more DC or AC power supplies, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and other arbitrary components.

[0207] The biometric verification device may further include an input unit 1004, which may be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.

[0208] Although not shown, the biometric verification device may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 1001 in the biometric verification device will load the executable files corresponding to one or more application processes into the memory 1002 according to the following instructions, and the processor 1001 will run the application stored in the memory 1002 to implement various functions as follows:

[0209] Acquire ambient light information of the environment in which the biometric verification device is currently located, and determine the ambient light color deviation type of the environment based on the ambient light information; determine ambient light compensation color information for alleviating the color deviation of the ambient light color deviation type based on the ambient light color deviation type; generate a color compensation image based on the ambient light compensation color information, and display the color compensation image; in response to a biometric verification payment instruction, collect a biometric image under mixed ambient light in the environment in which the biometric verification device is located that compensates for the reflected light of the color compensation image; when the identity authentication based on the biometric image is passed, execute the payment operation.

[0210] The specific implementation of the above operations can be found in the previous embodiments and will not be repeated here.

[0211] As can be seen from the above, this embodiment can obtain the ambient light information of the environment in which the biometric verification device is currently located, and determine the ambient light color deviation type of the environment based on the ambient light information; determine the ambient light compensation color information for reducing the color deviation of the ambient light color deviation type based on the ambient light color deviation type; generate a color compensation image based on the ambient light compensation color information, and display the color compensation image; in response to a biometric verification payment instruction, collect a biometric image under the mixed ambient light in the environment in which the biometric verification device is located that compensates for the reflected light of the color compensation image; when the identity authentication based on the biometric image is passed, execute the payment operation.

[0212] The present application can generate a color compensation image for reconciling the color deviation type of the ambient light corresponding to the current ambient light, so that when the user makes a payment, the reflected light of the color compensation image can be combined to collect the user's biometric image. This can reconcile the color deviation of the collected biometric image, which is beneficial to improving the quality of the biometric image, thereby ensuring the accuracy and efficiency of the user's identity verification and improving payment efficiency.

[0213] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be accomplished by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0214] To this end, an embodiment of the present application provides a computer-readable storage medium storing a plurality of instructions that can be loaded by a processor to execute the steps of any payment method provided in the embodiment of the present application. For example, the instructions can execute the following steps:

[0215] Acquire ambient light information of the environment in which the biometric verification device is currently located, and determine the ambient light color deviation type of the environment based on the ambient light information; determine ambient light compensation color information for alleviating the color deviation of the ambient light color deviation type based on the ambient light color deviation type; generate a color compensation image based on the ambient light compensation color information, and display the color compensation image; in response to a biometric verification payment instruction, collect a biometric image under mixed ambient light in the environment in which the biometric verification device is located that compensates for the reflected light of the color compensation image; when the identity authentication based on the biometric image is passed, execute the payment operation.

[0216] The specific implementation of the above operations can be found in the previous embodiments and will not be repeated here.

[0217] The computer-readable storage medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0218] Since the instructions stored in the computer-readable storage medium can execute the steps in any payment method provided in the embodiments of the present application, the beneficial effects that can be achieved by any payment method provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0219] According to one aspect of the present application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various optional implementations of the aforementioned payment aspects.

[0220] The above is a detailed introduction to a payment method, device, equipment, medium and program product provided in the embodiments of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, based on the ideas of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

[0221] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0222] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A payment method, performed by a biometric verification device, comprising: Acquiring ambient light information of an environment in which the biometric verification device is currently located, and determining a color bias type of the ambient light of the environment based on the ambient light information; determining, according to the ambient light color deviation type, ambient light compensation color information for alleviating the color deviation of the ambient light color deviation type; generating a color-compensated image according to the ambient light compensation color information, and displaying the color-compensated image; In response to a biometric verification payment instruction, capturing a biometric image under mixed ambient light in an environment where the biometric verification device is located that compensates for reflected light of the color-compensated image; and When the identity authentication based on the biometric image is passed, the payment operation is performed.

2. The method according to claim 1, wherein obtaining ambient light information of an environment in which the biometric verification device is currently located and determining the color bias type of the ambient light of the environment based on the ambient light information comprises: Collecting an environmental image of the environment in which the biometric verification device is currently located; Performing color analysis on the environment image to determine the type of color deviation of the ambient light of the environment image.

3. The method according to claim 2, wherein generating a color-compensated image based on the ambient light compensation color information and displaying the color-compensated image comprises: Performing object recognition on the environment image to obtain the types of items currently promoted in the environment where the biometric verification device is located; A color-compensated image is generated according to the ambient light compensation color information and the type of the object, and the color-compensated image is displayed.

4. The method according to claim 3, wherein generating a color-compensated image based on the ambient light compensation color information and the object type comprises: Inputting the ambient light compensation color information and the object type into an artificial intelligence image generation model; A color-compensated image is generated based on the ambient light compensation color information and the object type through the artificial intelligence image generation model.

5. The method according to any one of claims 2 to 4, wherein the performing color analysis on the environment image to determine the type of color deviation of the ambient light of the environment image comprises: Performing color space conversion on the environment image to obtain a converted image; Performing region division on the converted image to obtain a plurality of image regions; Based on the color deviation information of each image region, the ambient light color deviation type of the ambient image is determined.

6. The method according to claim 5, wherein determining the ambient light color deviation type of the ambient image based on the color deviation information of each image region comprises: For each image region, calculating regional color temperature information of the image region; determining a color deviation type of the image region according to regional color temperature information of the image region; Counting the color deviation types of each image area to obtain the frequency information of each preset color deviation type; The ambient light color deviation type of the ambient image is determined according to the frequency information.

7. The method according to any one of claims 1 to 6, wherein determining, based on the ambient light color deviation type, ambient light compensation color information for alleviating the color deviation of the ambient light color deviation type comprises: Based on a color superposition algorithm, a color compensation mapping relationship set is obtained, where the color compensation mapping relationship set includes mapping relationships between each preset color bias type and ambient light compensation color information; According to the color compensation mapping relationship set, ambient light compensation color information for adjusting the ambient light color deviation type is determined.

8. The method according to any one of claims 3 to 7, wherein generating a color-compensated image based on the ambient light compensation color information and displaying the color-compensated image comprises: generating an initial compensated image according to the ambient light compensation color information and the object type; Obtain marketing demand selection instructions; When the marketing demand selection instruction indicates not to conduct marketing, the initial compensation image is determined to be the color compensation image to be displayed, and the color compensation image is displayed.

9. The method according to claim 8, further comprising: When the marketing demand selection instruction indicates to conduct marketing, obtaining marketing information; Targeted promotional content is generated and displayed based on the ambient light compensation color information, the item type, and the marketing information.

10. The method according to any one of claims 3 to 7, wherein generating a color-compensated image based on the ambient light compensation color information and displaying the color-compensated image comprises: Determining promotional image style information for the environment in which the biometric verification device is located based on the ambient light compensation color information and the type of the item; Get the original promotional image; fusing the promotional image style information with the original promotional image to obtain a color-compensated image; The color-compensated image is displayed.

11. The method according to any one of claims 1 to 10, wherein the biometric verification device comprises a palm verification device or a facial verification device, and wherein collecting the biometric image comprises: When the biometric verification device is a palm verification device, capturing a palm image to obtain a biometric image; When the biometric verification device is a facial verification device, a facial image is collected to obtain a biometric image.

12. A payment device, used in a biometric verification device, comprising: an acquisition unit, configured to acquire ambient light information of an environment in which the biometric verification device is currently located, and determine a color deviation type of the ambient light of the environment according to the ambient light information; a determining unit, configured to determine, according to the ambient light color deviation type, ambient light compensation color information for alleviating the color deviation of the ambient light color deviation type; a generating unit, configured to generate a color-compensated image according to the ambient light compensation color information, and display the color-compensated image; a collection unit configured to collect a biometric image in response to a biometric verification payment instruction under mixed ambient light in an environment where the biometric verification device is located that compensates for reflected light of the color-compensated image; and The execution unit is configured to execute a payment operation when the identity authentication based on the biometric image is passed.

13. A biometric verification device comprising a memory and a processor; the memory stores an application, and the processor is used to run the application in the memory to execute the steps in the payment method according to any one of claims 1 to 11.

14. A computer-readable storage medium storing a plurality of instructions, wherein the instructions are suitable for being loaded by a processor to execute the steps of the payment method according to any one of claims 1 to 11.

15. A computer program product comprising a computer program or instructions, which implements the steps of the payment method according to any one of claims 1 to 11 when executed by a processor.

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