Notebook computer startup verification method and device, notebook computer and storage medium

Abstract

The invention relates to the technical field of electronic equipment, and provides a notebook computer startup verification method and device, a notebook computer and a storage medium. According to the application, the startup signal of the notebook computer is detected and the ambient light brightness is acquired at the same time, so that the notebook computer can automatically adjust the acquisition brightness of the infrared camera according to the intensity of the ambient light brightness; the self-adaptive adjustment of the acquisition brightness of the infrared camera ensures that clear face images with appropriate contrast can be captured in different illumination environments. The collected face image is subjected to light reflection removal processing, so that the influence of factors such as ambient light and screen light reflection is eliminated or reduced, and the quality of the face image is further improved. According to the method, the notebook computer is subjected to startup verification based on the high-quality target image, so that identification errors caused by the image quality problem are reduced, and the safety of the notebook computer is enhanced. The technical problem of face verification failure caused by dynamic change of ambient light brightness is solved.

Description

Technical Field

[0001] The present application relates to the technical field of electronic equipment, and in particular to a method and device for verifying startup of a notebook computer, a notebook computer, and a storage medium. Background Art

[0002] With the development of science and technology, laptops have become an indispensable tool in people's daily life and work. In order to ensure data security and device security, power-on verification technology is widely used. Among them, the power-on verification method based on face recognition is favored for its convenience and accuracy.

[0003] However, in practical applications, the dynamic changes in ambient light brightness have become a challenge that cannot be ignored. When the ambient light is too strong or too weak, a camera with a fixed brightness may find it difficult to capture sufficiently clear and detailed facial features. This directly leads to difficulties for the face recognition algorithm when processing images, which in turn affects the accuracy of verification and may even cause verification failure. Summary of the invention

[0004] In view of this, the present application provides a laptop power-on verification method, device, laptop and storage medium to solve the technical problem of face verification failure caused by dynamic changes in ambient light brightness.

[0005] A first aspect of the present application provides a method for powering on a notebook computer, the method comprising: When a startup signal of a laptop computer is detected, the ambient light brightness of the environment in which the laptop computer is located is obtained; Adaptively adjusting the acquisition brightness of the infrared camera of the laptop computer according to the ambient light brightness; Control the infrared camera to collect facial images at the collection brightness; Performing de-reflection processing on the face image to obtain a target image; The laptop computer is powered on and verified based on the target image.

[0006] Optionally, the adaptively adjusting the acquisition brightness of the infrared camera of the laptop computer according to the ambient light brightness includes: Determine a gain coefficient according to the ambient light brightness, wherein the ambient light brightness is inversely proportional to the gain coefficient; Acquire a first sensitivity of the infrared camera; Performing gain processing on the first sensitivity according to the gain coefficient to obtain a second sensitivity; determining a brightness adjustment value according to the first sensitivity and the second sensitivity; The collection brightness of the infrared camera of the notebook computer is adaptively adjusted according to the brightness adjustment value.

[0007] Optionally, determining the brightness adjustment value according to the first sensitivity and the second sensitivity includes: Pre-acquire a first environment image using the first sensitivity, and acquire a first brightness value of the first environment image; Pre-acquire a second environment image using the second sensitivity, and acquire a second brightness value of the second environment image; A brightness adjustment value is determined according to the first brightness value and the second brightness value.

[0008] Optionally, performing de-reflection processing on the face image to obtain a target image includes: Detecting a face region in the face image; Identifying a reflective area within the face area; Calculating the area ratio of the reflective area to the face area; Determining a position influence value of the reflective area; The reflection influence value of the reflection area is obtained according to the area ratio and the position influence value: The reflective area is de-reflected according to the reflective influence value to obtain a target image.

[0009] Optionally, determining the position influence value of the reflective area includes: Dividing the face region into a plurality of key regions and assigning a position weight to each region; Calculating the degree of overlap between the reflective area and each key area; The position influence value of the reflective area is determined according to the overlap degree and the corresponding position weight.

[0010] Optionally, performing de-reflection processing on the reflective area according to the reflective influence value to obtain a target image includes: comparing the reflection impact value with a preset reflection impact threshold; When the reflection impact value is lower than the preset reflection impact threshold, adjusting the contrast of the reflection area so that the adjusted reflection area has the same contrast as the non-reflection area in the face image; When the reflection impact value is higher than the preset reflection impact threshold, an image restoration algorithm is used to fill the reflection area so that the contrast of the restored reflection area is coordinated with that of the non-reflection area in the face image.

[0011] Optionally, performing power-on verification on the laptop computer based on the target image includes: Extracting facial features in the target image; Compare the extracted facial features with pre-stored facial features of legitimate users; Perform power-on verification based on the comparison results.

[0012] A second aspect of the present application provides a laptop computer startup verification device, the device comprising: A detection module, used to obtain the ambient light brightness of the environment where the laptop is located when detecting a startup signal of the laptop; An adjustment module, used for adaptively adjusting the acquisition brightness of the infrared camera of the laptop computer according to the ambient light brightness; An acquisition module, used for controlling the infrared camera to acquire a face image at the acquisition brightness; A processing module, used for performing a de-reflection process on the face image to obtain a target image; A verification module is used to perform power-on verification on the laptop computer based on the target image.

[0013] The third aspect of the present application provides a laptop computer, comprising an infrared camera, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the laptop computer power-on verification method when executing the computer program.

[0014] A fourth aspect of the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the notebook computer startup verification method.

[0015] The embodiments of the present application provide a method, device, laptop computer and storage medium for power-on verification of a laptop computer. The present application detects the power-on startup signal of the laptop computer and simultaneously obtains the ambient light brightness, so that the laptop computer can automatically adjust the acquisition brightness of the infrared camera according to the intensity of the ambient light brightness. The adaptive adjustment of the acquisition brightness of the infrared camera ensures that clear and contrast-appropriate facial images can be captured in different lighting environments. By performing de-reflection processing on the collected facial images, the influence of factors such as ambient light and screen reflection is eliminated or reduced, and the quality of the facial image is further improved. Power-on verification of the laptop computer is performed based on a high-quality target image, which reduces recognition errors caused by image quality problems, thereby enhancing the security of the laptop computer. The present application solves the technical problem of facial verification failure due to dynamic changes in ambient light brightness. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of a method for powering on a notebook computer shown in an embodiment of the present application; Figure 2 It is a functional module diagram of a notebook computer startup verification device shown in an embodiment of the present application; Figure 3 It is a structural diagram of a laptop computer shown in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations including one or more listed items.

[0018] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0019] The notebook computer startup verification method provided by the embodiment of the present invention is executed by the notebook computer, and correspondingly, the notebook computer startup verification device runs in the notebook computer.

[0020] Figure 1 The flowchart of the notebook computer startup verification method provided by the embodiment of the present invention is as follows. The notebook computer startup verification method specifically includes the following steps.

[0021] S11, when a startup signal of a laptop computer is detected, the ambient light brightness of the environment where the laptop computer is located is obtained.

[0022] When a user presses the power button of a laptop or performs other startup operations, the power management system inside the laptop first receives a startup signal, which is an electrical signal or a logic signal used to notify the laptop's computer hardware and software to prepare for startup.

[0023] At the same time or at a later point in time when the power-on signal is received, the ambient light sensor on the laptop is activated. The ambient light sensor can be located above the laptop screen or around the keyboard to sense the light intensity of the laptop's current environment. Ambient light brightness is the brightness effect of external ambient light shining on the laptop and its surrounding area. The ambient light sensor measures the intensity of light shining on its photosensitive element and converts it into an electrical signal or digital signal. This signal represents the light brightness level of the current environment and is stored in the laptop's memory or register in a certain format (such as voltage value, current value or digital code).

[0024] After obtaining the ambient light brightness, the laptop's power-on verification system will use the ambient light brightness to adjust the infrared camera's acquisition brightness to adapt to the current lighting environment.

[0025] S12, adaptively adjusting the collection brightness of the infrared camera of the laptop computer according to the ambient light brightness.

[0026] An infrared camera is a camera that uses infrared light to capture images. Compared with visible light cameras, it has better imaging effects under certain conditions (such as low light environments). However, under different lighting conditions, if the acquisition brightness of the infrared camera is fixed, it is easy to overexpose the image (too high brightness, loss of details) or underexpose (too low brightness, dim image). By adaptively adjusting the acquisition brightness, it can ensure that the infrared camera can capture clear and detailed images in different lighting environments, thereby improving the accuracy of face recognition and the verification pass rate of laptops.

[0027] Based on the ambient light brightness provided by the ambient light sensor, the laptop's operating system or camera driver will determine the acquisition brightness that the infrared camera should use according to the preset brightness adjustment strategy (such as a lookup table, algorithm formula, etc.). The acquisition brightness is the brightness setting used by the infrared camera when acquiring images, which determines the camera's sensitivity to light and the overall brightness of the image. Adaptively adjusting the acquisition brightness of the infrared camera according to the ambient light brightness enables the infrared camera to capture clear and accurate facial images under different lighting conditions, improving the overall environmental adaptability of the system.

[0028] In an optional implementation, the adaptively adjusting the acquisition brightness of the infrared camera of the laptop computer according to the ambient light brightness includes: Determine a gain coefficient according to the ambient light brightness; Acquire a first sensitivity of the infrared camera; Performing gain processing on the first sensitivity according to the gain coefficient to obtain a second sensitivity; determining a brightness adjustment value according to the first sensitivity and the second sensitivity; The collection brightness of the infrared camera of the notebook computer is adaptively adjusted according to the brightness adjustment value.

[0029] The laptop reads the ambient light brightness provided by the ambient light sensor and maps the ambient light brightness to a gain coefficient according to a preset algorithm or lookup table. The gain coefficient is a parameter used to compensate for the effect of light changes on camera imaging quality. The gain coefficient is inversely proportional to the ambient light brightness. The brighter the environment, the smaller the gain coefficient, and the darker the environment, the larger the gain coefficient.

[0030] Sensitivity is the camera's sensitivity to light, which determines the camera's ability to capture images under different lighting conditions. You can query the camera driver or hardware register to obtain the current infrared camera's sensitivity, that is, the first sensitivity. Multiply the gain coefficient by the first sensitivity to get the second sensitivity.

[0031] According to the difference between the first sensitivity and the second sensitivity, a brightness adjustment value is calculated, and the brightness adjustment value is sent to a driver or a hardware register of the infrared camera to control the infrared camera to capture images according to the new acquisition brightness setting.

[0032] The above optional implementation method dynamically adjusts the infrared camera's acquisition brightness according to the ambient light brightness, so that the laptop's infrared camera can automatically adapt to various lighting environments, whether in bright daylight or dim night, and can maintain stable imaging performance. The enhanced environmental adaptability can ensure that the infrared camera can maintain appropriate acquisition brightness under different lighting conditions, thereby capturing clear and detailed images, avoiding the problem of overexposure or underexposure of images caused by excessive or weak light, and significantly improving image quality.

[0033] In addition, working under extreme lighting conditions for a long time may cause damage to the infrared camera's hardware. By adaptively adjusting the acquisition brightness, the camera can be prevented from being in extreme working conditions of high or low brightness for a long time, thereby reducing the hardware burden and extending the camera's service life.

[0034] In an optional implementation, determining the brightness adjustment value according to the first sensitivity and the second sensitivity includes: Pre-acquire a first environment image using the first sensitivity, and acquire a first brightness value of the first environment image; Pre-acquire a second environment image using the second sensitivity, and acquire a second brightness value of the second environment image; A brightness adjustment value is determined according to the first brightness value and the second brightness value.

[0035] The laptop computer first sets the sensitivity of the infrared camera to a first sensitivity, and controls the infrared camera to capture an image at the first sensitivity as a first environment image. The first environment image is subjected to brightness analysis, and the overall brightness value of the first environment image or the brightness value of a key area is extracted as a first brightness value.

[0036] The laptop computer then sets the sensitivity of the infrared camera to a second sensitivity, and controls the infrared camera to capture an image at the second sensitivity as a second environment image. The second environment image is subjected to the same brightness analysis to extract a second brightness value.

[0037] The first brightness value and the second brightness value are compared and the difference between the two is analyzed. According to a preset algorithm or rule, the brightness adjustment value is calculated in combination with the difference between the two and the hardware characteristics of the infrared camera. The brightness adjustment value is used to indicate how much the infrared camera should increase or decrease the brightness to achieve the best imaging effect.

[0038] The above optional implementation method can more accurately determine the brightness adjustment value by actually capturing the environment image and comparing the brightness values ​​under different sensitivities, thereby avoiding the error that may be caused by theoretical calculation alone. Through accurate brightness adjustment, the clarity and contrast of the image can be significantly improved, and the occurrence of overexposure or underexposure can be reduced.

[0039] S13, controlling the infrared camera to collect a face image at the collection brightness.

[0040] After determining the appropriate acquisition brightness, the laptop sends an acquisition brightness setting instruction to the infrared camera through the driver. After setting the acquisition brightness of the infrared camera, start the face detection function. Once a face is detected, the infrared camera will capture the face image at the acquisition brightness. Accurately adjusting the acquisition brightness can minimize image quality issues caused by lighting changes, thereby providing a reliable data foundation for applications such as face recognition.

[0041] The collected facial images can be further processed, such as denoising, sharpening, cropping, etc., to improve image quality or meet specific needs. After the image processing is completed, it will be saved to a storage device for subsequent facial recognition, verification or other applications.

[0042] In some cases, to ensure the effectiveness of the brightness adjustment, the laptop can activate the infrared camera and capture a test image to check whether the brightness of the test image is as expected. If the brightness of the test image is not adjusted properly, the laptop can re-perform the adjustment process.

[0043] S14, performing de-reflection processing on the face image to obtain a target image.

[0044] Due to the reflection of ambient light sources (such as sunlight, lights, etc.), unnecessary reflective areas may be generated on the face image. These reflective areas not only affect the visual effect of the image, but may also interfere with subsequent image processing (such as face recognition). Especially when collecting face images of users wearing glasses, ambient light will affect iris recognition. Therefore, face images need to be de-reflected.

[0045] In an optional implementation, performing de-reflection processing on the face image to obtain a target image includes: Detecting a face region in the face image; Identifying a reflective area within the face area; Calculating the area ratio of the reflective area to the face area; Determining a position influence value of the reflective area; The reflection influence value of the reflection area is obtained according to the area ratio and the position influence value: The reflective area is de-reflected according to the reflective influence value to obtain a target image.

[0046] A face detection algorithm (such as Haar feature classifier, deep learning method, etc.) can be used to detect faces in face images. The face detection algorithm can locate the face area in the face image and give a bounding box of the face. After the face area is detected, it is necessary to further identify the reflective part in the face area. Use image processing algorithms (such as edge detection, threshold processing, etc.) to identify the reflective area in the face area. The reflective area usually appears as a part that is highlighted, sharp-edged, and discontinuous with the surrounding image content. In some embodiments, color space conversion (such as RGB to HSV or YUV) can be used to enhance the contrast between the reflective area and the surrounding area, so that the reflective area can be more easily detected.

[0047] By comparing the number of pixels in the reflective area with the number of pixels in the entire face area, the area ratio of the reflective area to the face area can be calculated. The area ratio reflects the coverage of the face image by the reflection and is an important reference for subsequent processing.

[0048] In addition to the area ratio, the position of the reflective area on the face is also an important factor affecting the de-reflection effect. For example, when the reflection appears in key feature areas such as the eyes and nose, it has a greater impact on face recognition. Therefore, it is necessary to assign an influence value to the position of the reflective area, which is determined by the proximity of the reflective area to the key feature area.

[0049] By combining the area ratio and the position influence value, a comprehensive reflection influence value can be obtained. The reflection influence value can be the sum of the area ratio and the position influence value, or the weighted sum of the area ratio and the position influence value, or the mean / weighted mean of the area ratio and the position influence value. This application does not impose any restrictions on this. The reflection influence value is used to quantify the overall impact of reflection on the face image, providing a basis for subsequent de-reflection processing.

[0050] Finally, according to the reflection impact value, a suitable de-reflection algorithm is selected to process the reflective area. The processing algorithm may include local repair, illumination model simulation, deep learning repair and other methods. After the de-reflection processing is completed, the target image with the reflection removed is obtained, which is superior to the original image in terms of visual effects and subsequent processing (such as face recognition).

[0051] In some implementations, the de-reflection processed image may be post-processed, such as smoothing, sharpening, color correction, etc., to further improve the image quality.

[0052] In order to more accurately evaluate the impact of reflective areas on face image quality or subsequent face recognition tasks, a fine-grained method is used in the following implementation to quantify this impact.

[0053] Determining the position influence value of the reflective area includes: Dividing the face region into a plurality of key regions and assigning a position weight to each region; Calculating the degree of overlap between the reflective area and each key area; The position influence value of the reflective area is determined according to the overlap degree and the corresponding position weight.

[0054] First, the face area is divided into multiple key areas (Key Areas). These key areas can be defined based on the anatomical structure of the face, such as eyes, nose, mouth, cheeks, etc. The importance of each area for face recognition or face image analysis may be different, so a position weight (Position Weight) is assigned to each key area. The allocation of position weights can be determined based on specific application scenarios and experimental data. Generally, areas such as eyes, nose, and mouth are given higher weights because they contain rich feature information.

[0055] Next, calculate the overlap between the reflective area and each of the divided key areas. This can be achieved by calculating the intersection area of ​​the two areas in the image space and dividing it by the area of ​​the key area to get an overlap ratio. The overlap ratio reflects the degree of coverage of the reflective area to the key area and is an important indicator for evaluating the impact of reflections.

[0056] Finally, the position influence value of the reflective area is calculated based on the overlap between each key area and the reflective area and the position weight of the area. The position influence value can be obtained by multiplying the overlap ratio of each key area by its corresponding position weight and then adding the calculated results of all key areas.

[0057] The above optional implementation can more carefully and scientifically evaluate the influence of the position of the reflective area in the face image, and provide important reference information for subsequent image processing or face recognition tasks.

[0058] In an optional implementation, whether or not to perform a reflection removal process, as well as the priority and intensity of the process, may also be determined based on the position influence value of the reflection area.

[0059] In order to decide whether to perform reflection removal processing, one or more thresholds can be set. These thresholds can be set according to specific application scenarios, safety requirements, and the performance of image processing algorithms. For example, a higher threshold can be set, and only when the position impact value exceeds this threshold, the reflection is considered to have a significant impact on the image processing or face recognition task, thereby triggering the reflection removal processing.

[0060] If there are multiple reflective areas in the face image, the processing priority can be determined according to the position influence value of each reflective area. Reflective areas with higher position influence values ​​(i.e., reflective areas that overlap more with key areas or are located in important positions) have higher priorities and can be removed first to ensure that reflective areas that have a greater impact on image processing or face recognition tasks are removed first.

[0061] The intensity of the deglare treatment can also be adjusted based on the position impact value. For reflective areas with high position impact values, a more aggressive deglare algorithm or parameter setting can be used to ensure that the reflections are fully removed. For reflective areas with low position impact values, a gentler treatment can be used to avoid unnecessary damage to image quality.

[0062] In order to handle the reflection problem more finely and optimize the final target image quality, different de-reflection processing strategies can be adopted for the reflective area according to the different reflection impact values.

[0063] In an optional implementation, performing a de-reflection process on the reflective area according to the reflective influence value to obtain a target image includes: comparing the reflection impact value with a preset reflection impact threshold; When the reflection impact value is lower than the preset reflection impact threshold, adjusting the contrast of the reflection area so that the adjusted reflection area has the same contrast as the non-reflection area in the face image; When the reflection impact value is higher than the preset reflection impact threshold, an image restoration algorithm is used to fill the reflection area so that the contrast of the restored reflection area is coordinated with that of the non-reflection area in the face image.

[0064] The calculated reflection impact value is compared with the preset reflection impact threshold. This threshold is set according to the actual application scenario and the expected image quality requirements to distinguish the severity of the reflection impact on the image.

[0065] If the reflection impact value is lower than the preset reflection impact threshold, it means that the reflection has a relatively small impact on the image, and its impact can be reduced by adjusting the contrast of the reflection area. The goal here is to make the contrast of the adjusted reflection area consistent with the non-reflection area in the face image to reduce the visual abruptness. Contrast adjustment can be achieved by changing parameters such as brightness and saturation of the reflection area.

[0066] If the reflection impact value is higher than the preset reflection impact threshold, it means that the reflection has a greater impact on the image and more complex processing is required. In this case, an image restoration algorithm can be used to fill the reflective area. The information of the non-reflective area in the face image can be used to predict and fill the content of the reflective area, so that the restored reflective area is more visually coordinated with the surrounding non-reflective areas. Image restoration algorithms can include algorithms based on texture synthesis, algorithms based on deep learning, etc.

[0067] Whether filling the reflective area through contrast adjustment or image restoration algorithm, the ultimate goal is to coordinate the contrast of the repaired reflective area with the surrounding non-reflective area to ensure the visual consistency and naturalness of the entire image.

[0068] The above optional implementation manner adopts different de-reflection processing strategies according to different reflection impact values, which can handle the reflection problem more finely and optimize the final target image quality, improve the flexibility of de-reflection processing, and help improve the adaptability and robustness of the image processing algorithm.

[0069] In an optional implementation, the preset reflection impact threshold is determined in the following manner.

[0070] Get multiple experimental samples, and perform a reflection removal processing experiment on each experimental sample. Record the reflection impact value, preset reflection impact threshold, and image quality evaluation results after processing (such as contrast, clarity, naturalness, and other indicators) of each experimental sample. Compare and analyze the image quality evaluation results after processing with the preset reflection impact threshold. Observe which samples have better processing effects under the preset reflection impact threshold and which samples have poor processing effects. Find the statistical law between the reflection impact value and the image quality after processing. For example, the distribution of the image quality after processing of samples within different reflection impact value ranges can be analyzed. If it is found that a large number of samples with low reflection impact values ​​are mistakenly processed in a complex manner under the preset reflection impact threshold (such as using an image restoration algorithm), the preset reflection impact threshold can be increased to reduce unnecessary processing. On the contrary, if samples with high reflection impact values ​​are not fully processed under the preset reflection impact threshold, resulting in poor image quality, the preset reflection impact threshold can be lowered to ensure that these experimental samples can be properly processed. After adjusting the preset reflection impact threshold, re-execute the experiment to verify the adjustment effect. Ensure that the new preset reflection impact threshold can improve the accuracy and robustness of image processing.

[0071] The above optional implementation effectively adjusts the preset reflection influence threshold according to the experimental results, thereby improving the accuracy and robustness of the image processing algorithm.

[0072] S15, performing power-on verification on the laptop computer based on the target image.

[0073] The laptop computer is powered on and authenticated based on the target image (i.e., the clear face image after de-reflection processing).

[0074] In an optional implementation, the performing power-on verification on the laptop computer based on the target image includes: Extracting facial features in the target image; Compare the extracted facial features with pre-stored facial features of legitimate users; Perform power-on verification based on the comparison results.

[0075] A facial recognition algorithm can be used to extract features from the face in the target image. These features may include the shape and position of the eyes, nose, mouth, as well as the contours and textures of the face. The extracted facial features are compared with the facial features of the legitimate user pre-stored in the laptop. The facial features of the legitimate user are entered during the device setup phase or user registration.

[0076] The comparison process may be: calculating the similarity or distance between the extracted facial features and the facial features of the legal user. According to a preset threshold, judging whether the current face matches a pre-stored legal user face. If the comparison result shows that the current face is highly similar to a pre-stored legal user face (i.e., the similarity exceeds the threshold), the verification passes. If the comparison result shows that the current face is not similar to any pre-stored legal user face (i.e., the similarity is lower than the threshold), the verification fails.

[0077] The laptop can also perform corresponding actions based on the verification results. If the verification passes, the laptop will continue the boot process, load the operating system and unlock the locked screen. If the verification fails, the laptop will refuse to boot or keep the screen locked.

[0078] In some embodiments, when verification fails, the laptop computer may also display an error message, prompting the user to try again or select another verification method (such as entering a password, using a fingerprint, etc.). In some embodiments, the laptop computer may also record the number of failed verification attempts and perform further security measures (such as locking an account, triggering an alarm, etc.) according to the security policy.

[0079] Through power-on authentication, biometric technology (especially face recognition) is used to enhance the security of laptops, ensuring that only legitimate users can access the laptop. This authentication method not only improves security, but also provides a more convenient and intuitive user experience.

[0080] This application can understand the current usage environment in real time by detecting the power-on start-up signal of the laptop and obtaining the ambient light brightness at the same time, so that the laptop can automatically adjust the acquisition brightness of the infrared camera according to the strength of the external light, thereby avoiding the acquisition of poor quality images under different lighting conditions. The adaptive adjustment of the infrared camera acquisition brightness ensures that clear and contrast-appropriate facial images can be captured under different lighting environments, providing high-quality input data for subsequent face recognition algorithms, thereby improving the accuracy and stability of recognition. The collected facial images may be affected by factors such as ambient light and screen reflection. By de-reflecting the collected facial images, the influence of these adverse factors is eliminated or reduced, thereby further improving the quality of the facial images. Power-on verification based on high-quality target images reduces recognition errors caused by image quality problems, thereby enhancing the security of the laptop. This verification method based on biometric recognition is more reliable than traditional password or PIN code verification. In addition, there is no need for users to manually adjust the camera settings, and the system automatically completes all adjustments, which simplifies the use process and improves the user experience. The fast and accurate power-on verification process also reduces the user's waiting time.

[0081] Figure 2 It is a functional module diagram of a notebook computer startup verification device shown in an embodiment of the present application.

[0082] In some embodiments, the notebook computer startup verification device 20 may include multiple functional modules composed of computer program segments. The computer programs of the various program segments in the notebook computer startup verification device 20 may be stored in the memory of the notebook computer and executed by at least one processor to execute (see Figure 1 Description) The function of power-on authentication for laptop computers.

[0083] In this embodiment, the laptop computer startup verification device 20 can be divided into multiple functional modules according to the functions it performs. The functional modules may include: a detection module 201, an adjustment module 202, a collection module 203, a processing module 204, a verification module 205 and an execution module 206. The module referred to in this application refers to a series of computer program segments that can be executed by at least one processor and can complete fixed functions, which are stored in a memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0084] The detection module 201 is used to obtain the ambient light brightness of the environment where the laptop computer is located when detecting a startup signal of the laptop computer.

[0085] The adjustment module 202 is used to adaptively adjust the collection brightness of the infrared camera of the notebook computer according to the ambient light brightness.

[0086] The acquisition module 203 is used to control the infrared camera to acquire a facial image at the acquisition brightness.

[0087] The processing module 204 is used to perform de-reflection processing on the face image to obtain a target image.

[0088] The verification module 205 is used to perform power-on verification on the laptop computer based on the target image.

[0089] The execution module 206 is used to execute corresponding operations according to the verification result.

[0090] It should be understood that the various variations and specific embodiments of the laptop computer power-on verification method provided in the above embodiments are also applicable to the laptop computer power-on verification device in this embodiment. Through the detailed description of the above laptop computer power-on verification method, those skilled in the art can clearly understand the implementation process of the laptop computer power-on verification device in this embodiment. For the sake of brevity of the specification, it will not be described in detail here.

[0091] The embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, all or part of the steps of the notebook computer power-on verification method are implemented.

[0092] See also Figure 3 FIG. 2 is a schematic diagram of the structure of a laptop computer provided in an embodiment of the present application. In a preferred embodiment of the present application, the laptop computer 3 includes an infrared camera 30 , a memory 31 , at least one processor 32 , at least one communication bus 33 and a sensor 34 .

[0093] Those skilled in the art should understand that Figure 3 The structure of the laptop computer shown does not constitute a limitation of the embodiments of the present application, and may be either a bus structure or a star structure. The laptop computer 3 may also include more or less other hardware or software than shown in the figure, or a different arrangement of components.

[0094] In some embodiments, the laptop computer 3 is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to processors, application-specific integrated circuits, programmable gate arrays, digital processors, and embedded devices. The laptop computer 3 may also include client devices, which include but are not limited to any electronic product that can interact with a client through a keyboard, mouse, remote control, touchpad, or voice-controlled device, such as a personal computer, tablet computer, smart phone, digital camera, etc.

[0095] It should be noted that the laptop computer 3 is only an example, and other existing or future electronic products that are suitable for the present application should also be included in the protection scope of the present application and included here by reference.

[0096] In some embodiments, the memory 31 stores a computer program, and when the computer program is executed by the at least one processor 32, all or part of the steps in the laptop computer startup verification method are implemented. The memory 31 includes a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data. Further, the computer-readable storage medium 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, and the like.

[0097] In some embodiments, the at least one processor 32 is the control core (Control Unit) of the laptop computer 3, and uses various interfaces and lines to connect various components of the entire laptop computer 3, and executes various functions and processes data of the laptop computer 3 by running or executing programs or modules stored in the memory 31, and calling data stored in the memory 31. For example, when the at least one processor 32 executes the computer program stored in the memory, it implements all or part of the steps of the laptop computer power-on verification method described in the embodiment of the present application; or implements all or part of the functions of the laptop computer power-on verification device. The at least one processor 32 can be composed of an integrated circuit, for example, it can be composed of a single packaged integrated circuit, or it can be composed of multiple integrated circuits with the same function or different functions, including one or more central processing units (CPUs), processors, digital processing chips, graphics processors, and various control chips.

[0098] In some embodiments, the at least one communication bus 33 is configured to realize the connection and communication between the memory 31 and the at least one processor 32. Although not shown, the laptop computer 3 may also include a power supply (such as a battery) for supplying power to each component. Preferably, the power supply may be logically connected to the at least one processor 32 through a power management device, so as to realize the functions of managing charging, discharging, and power consumption management through the power management device. The power supply may also include any components such as one or more DC or AC power supplies, recharging power supply fault detection circuits, power converters or inverters, power status indicators, etc. The laptop computer 3 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0099] The above-mentioned integrated unit implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium, including a number of instructions for enabling a laptop computer (which can be a personal computer, a laptop computer, or a network device, etc.) or a processor to execute part of the method described in each embodiment of the present application.

[0100] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0101] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

Claims

1. A method for verifying the startup of a notebook computer, characterized in that: The method comprises: When a startup signal of a laptop computer is detected, the ambient light brightness of the environment in which the laptop computer is located is obtained; Adaptively adjusting the acquisition brightness of the infrared camera of the laptop computer according to the ambient light brightness; Control the infrared camera to collect a face image at the collection brightness; Performing de-reflection processing on the face image to obtain a target image; The laptop computer is powered on and verified based on the target image.

2. The notebook computer startup verification method according to claim 1, characterized in that: The step of adaptively adjusting the acquisition brightness of the infrared camera of the laptop computer according to the ambient light brightness includes: Determine a gain coefficient according to the ambient light brightness, wherein the ambient light brightness is inversely proportional to the gain coefficient; Acquire a first sensitivity of the infrared camera; Performing gain processing on the first sensitivity according to the gain coefficient to obtain a second sensitivity; determining a brightness adjustment value according to the first sensitivity and the second sensitivity; The collection brightness of the infrared camera of the notebook computer is adaptively adjusted according to the brightness adjustment value.

3. The notebook computer startup verification method according to claim 2, characterized in that: The determining of the brightness adjustment value according to the first sensitivity and the second sensitivity comprises: Pre-acquire a first environment image using the first sensitivity, and acquire a first brightness value of the first environment image; Pre-acquire a second environment image using the second sensitivity, and acquire a second brightness value of the second environment image; A brightness adjustment value is determined according to the first brightness value and the second brightness value.

4. The notebook computer startup verification method according to claim 1, characterized in that: The performing de-reflection processing on the face image to obtain a target image comprises: Detecting a face region in the face image; Identifying a reflective area within the face area; Calculating the area ratio of the reflective area to the face area; Determining a position influence value of the reflective area; The reflection influence value of the reflection area is obtained according to the area ratio and the position influence value: The reflective area is de-reflected according to the reflective influence value to obtain a target image.

5. The notebook computer startup verification method according to claim 4, characterized in that: Determining the position influence value of the reflective area includes: Dividing the face region into a plurality of key regions and assigning a position weight to each region; Calculating the degree of overlap between the reflective area and each key area; The position influence value of the reflective area is determined according to the overlap degree and the corresponding position weight.

6. The notebook computer startup verification method according to claim 4 or 5, characterized in that: The performing de-reflection processing on the reflective area according to the reflective influence value to obtain a target image comprises: comparing the reflection impact value with a preset reflection impact threshold; When the reflection impact value is lower than the preset reflection impact threshold, adjusting the contrast of the reflection area so that the adjusted reflection area has the same contrast as the non-reflection area in the face image; When the reflection impact value is higher than the preset reflection impact threshold, an image restoration algorithm is used to fill the reflection area so that the contrast of the restored reflection area is coordinated with that of the non-reflection area in the face image.

7. The notebook computer startup verification method according to claim 1, characterized in that: The performing power-on verification on the laptop computer based on the target image comprises: Extracting facial features in the target image; Compare the extracted facial features with pre-stored facial features of legitimate users; Perform power-on verification based on the comparison results.

8. A notebook computer startup verification device, characterized in that: The device comprises: A detection module, used to obtain the ambient light brightness of the environment where the laptop is located when detecting a startup signal of the laptop; An adjustment module, used for adaptively adjusting the acquisition brightness of the infrared camera of the laptop computer according to the ambient light brightness; An acquisition module, used for controlling the infrared camera to acquire a face image at the acquisition brightness; A processing module, used for performing a de-reflection process on the face image to obtain a target image; A verification module is used to perform power-on verification on the laptop computer based on the target image.

9. A notebook computer, characterized in that: The invention comprises an infrared camera, a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the notebook computer startup verification method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the notebook computer startup authentication method as described in any one of claims 1 to 7 are implemented.

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