1698results about "Matching and classification" patented technology

The embodiment of the invention provides an identity verification method and device, electronic equipment and a storage medium, and belongs to the technical field of financial science and technology. The method comprises the following steps: acquiring multi-modal biological recognition data of a user in a current environment; performing feature extraction on the face image through a convolutional neural network to obtain face features; performing feature extraction on the iris image and the fingerprint information through a support vector machine to generate iris features and fingerprint features; identifying the face posture of the user in the current environment and the environment type of the current environment; adjusting weight values corresponding to the face features, the iris features and the fingerprint features according to the face postures and the environment types; and performing weighted fusion on the face feature, the iris feature and the fingerprint feature according to the weight value, and generating an identity verification result of the corresponding user in the current environment. According to the embodiment of the invention, the security and accuracy of user identity verification can be improved, and identity verification can be intelligently and accurately carried out on users in different complex environments in a financial scene.

The present disclosure generally relates to enrolling a biometric feature for use with a peripheral device. While a computer system is connected to an external device, the computer system receives a first request to enroll a biometric feature. In response to receiving the request and in accordance with a determination that the request satisfies a first set of one or more criteria, the computer system displays a user interface object prompting a user to provide an authorization input to one or more input devices physically connected to the computer system. While displaying the first user interface object, the computer system receives user input. In response to receiving the user input and in accordance with a determination that the user input includes the authentication input, the computer system initiates a process to enroll the biometric feature using a biometric sensor that is integrated with the external device.

The invention relates to the technical field of image processing, and particularly discloses a fingerprint feature recognition analysis method based on a direction field guidance and space attention technology, and the method comprises the steps: carrying out the degeneration of a high-quality fingerprint generated by software, so as to obtain a low-quality potential fingerprint and a high-quality fingerprint data set which are paired; designing a double-branch space attention module and adding the double-branch space attention module into an enhanced network, so that the enhanced network can simultaneously learn characteristics of high-quality and low-quality fingerprint areas; using the reconstruction loss, the plurality of direction field losses and the generated fingerprint data set to guide training of an enhancement network, so that the enhancement network can learn direction field features of potential fingerprints; inputting a potential fingerprint to be enhanced into the trained enhancement network to obtain a high-quality enhancement result; the enhancement network can better extract the direction field characteristics of the potential fingerprints and the characteristics of different quality areas, so that the accuracy of identifying the potential fingerprints by the enhancement network is improved.

This disclosure describes a user-recognition system that may perform one or more verification methods upon identifying a previous image that matches a current image of a palm of a user. For instance, the user-recognition system may perform the verification method(s) as part of the recognition method (e.g., after recognizing a matching image), in response to an audit process, in response to a request to re-analyze the image data (e.g., because a user indicates that he or she was not associated with a particular purchase or shopping session), and / or the like.

Systems and methods for tokenless authorization are provided. Obtaining an electronic representation of an initial biometric sampling of a registrant. Applying the initial electronic representation to a first cryptographic function that produces a unique digital identifier (UDI). Obtaining account information constructs corresponding to an account by the registrant with a third party. Generating a unique secure identification number (SIN) using the UDI and the account information constructs. Storing a unique link from the UDI to the account information constructs. Receiving a request for service and an electronic representation of a second biometric sampling. Forming the UDI by applying the second electronic representation to the first cryptographic function. Verifying the UDI corresponds to the stored UDI to reconstruct the unique SIN from the UDI and using this unique SIN to retrieve the account information constructs through the indexed data structure. Transmitting the request and the unique SIN to the third party.

Disclosed in the embodiments of the present application are service processing methods, an apparatus, a device, a storage medium and a program product. A method comprises: receiving subject description information sent by a biological recognition apparatus, the subject description information comprising biological features and position information of a target subject; acquiring identity information of the target subject recorded in the biological features, and acquiring a service information set configured in association with the identity information; screening out from the service information set at least one piece of candidate service information matched with the position information; sending the at least one piece of candidate service information to a terminal device associated with the identity information, and in response to a selection operation triggered by the target subject, the terminal device determining at least one piece of target service information from amongst the at least one piece of candidate service information; and receiving the at least one piece of target service information returned by the terminal device, and processing a service corresponding to each piece of target service information amongst the at least one piece of target service information.

Systems herein describe a secured transaction system for accessing a camera stream from a front-facing camera unit of a wearable computing device, identifying a visual marker in the camera feed associated with a payment application, in response to identifying the visual marker, transmitting a request to an operating system of the wearable computing device to isolate access of the camera stream to the payment application and in response to transmitting the request, initiating a transaction using the payment application.

The invention relates to the technical field of building design, and discloses an Internet-based building design dynamic process management system, which comprises a multi-factor identity verification module, a distributed data storage module, a process dynamic optimization engine, a behavior evaluation and intervention module and an intelligent audit tracking system. According to the building design dynamic process management system based on the Internet, through cooperative work of the process dynamic optimization engine and the behavior evaluation and intervention module, the problem that task optimization of a traditional process management system depends on a preset rule is solved; the flow dynamic optimization engine dynamically adjusts the task priority and resource allocation by using an improved ant colony algorithm based on the real-time task progress and the executor behavior data; meanwhile, the behavior evaluation and intervention module quantitatively evaluates the behavior of the executor according to a weighted scoring model, triggers an automatic intervention process when necessary, and can quickly adapt to sudden changes, so that the resource allocation efficiency is improved, and human errors are reduced.

Methods and systems are described for identity verification and deepfake prevention. A first video of a person is captured during an in-person interaction, the person's identity is verified, and the video is associated with the verified identity. In a subsequent electronic interaction, information about a second person is accessed, a second video of the second person is captured, and a trained neural network model is used to determine if the second person is attempting to identify as the first person. If the second person is not attempting to identify as the first person, the electronic interaction proceeds. If the second person is attempting to identify as the first person, the model determines if the first person is likely the same as the second person, transmitting a positive indicator if likely the same, and a negative indicator if not. Related networks, models, apparatuses, devices, techniques, and articles are also described.

The invention discloses a multi-level network identity verification and access control system, which is characterized in that a biological feature recognition technology is further introduced after traditional password verification, and multi-scale feature extraction is carried out on a fingerprint image input by a user by utilizing an image processing technology based on deep learning; shallow detail features and global structure features of fingerprints are captured, and comprehensive feature representation of a fingerprint image is obtained through feature selection enhancement and multi-level feature joint perception processing, so that a defogged fingerprint image is generated, and based on pixel-by-pixel fingerprint difference information between the defogged fingerprint image and an authorized fingerprint image, the defogged fingerprint image is obtained. A final verification result is generated, and it is ensured that only the verified user can access the system or data conforming to the role of the user. Through the mode, the problem of poor fingerprint image quality can be effectively solved, so that the accuracy and safety of identity verification are improved, and the overall performance of the system and the user experience are optimized.

An enhanced lifeform transmission system to locate a lifeform (e.g., human, animal, etc.) wanting to be identified as a lifeform for collision avoidance. The lifeform transceiver is worn on a limb or neck of the lifeform. The collision avoidance system is housed inside the vehicle and receives communications from the lifeform transceiver. The lifeform is automatically detected and the lifeform transmitter is automatically turned on and off when a lifeform enters and exits a vehicle. The lifeform transceiver also tracks and protects the lifeform in a manufacturing facility or at an outdoor location.

In various examples, infrared image data may be used to detect a subcutaneous characteristic(s) (e.g., a palm vein topology) of a person (e.g., a person requesting entry to a vehicle, a vehicle occupant) and authenticate the user based on the detected subcutaneous characteristic(s). For example, infrared image data representing one or more acquired subcutaneous characteristics (e.g., a topology of veins and / or other blood vessels in a region of the authenticating user's palm, hand, neck, forearm, face, fingertip, eye, etc.) may be generated. Hand and / or palm detection may be applied to detect a region depicting the user's hand or palm, and that region (or some subset thereof) may be segmented to generate a representation of an acquired vein topology. The acquired vein topology may be compared with one or more reference vein topologies stored in a database to determine whether the acquired vein topology matches one of the reference vein topologies.

Authentication method and system using biometric data of a person. The method is flexibly usable on a plurality of distinct verification and authentication devices and comprises a verification process and an authentication process. First biometric data is captured by the verification device and compared to official biometric data. Captured and inputted data is certified and stored. At time of a required authentication of the person, live biometric data of the person is captured and compared to first biometric data. If the data is similar, the person is automatically authenticated.

The present disclosure provides a method for ultrasonic fingerprint identification, an ultrasonic fingerprint apparatus, and an electronic device. The method is performed by an ultrasonic fingerprint apparatus arranged below a screen of an electronic device, and the method includes: acquiring a correspondence between a plurality of temperatures and a plurality of parameter groups, wherein each of the parameter groups includes at least one of: a frequency of an ultrasonic signal transmitted to a finger, the number of collections of an ultrasonic fingerprint signal returned from the finger, and a delay of a collection window of the ultrasonic fingerprint signal with respect to a transmission moment of the ultrasonic signal; determining a target parameter group among the plurality of parameter groups based on a first temperature and the correspondence; and performing fingerprint identification at the first temperature based on parameters in the target parameter group.

The invention relates to the technical field of vulnerability detection, in particular to an industrial asset fingerprint identification-based vulnerability stain detection system, which comprises a data acquisition module, a data analysis module, a fingerprint identification module, a vulnerability matching module, a stain analysis module and a verification module. The flow data of each industrial asset device in operation is collected in real time to generate fingerprint information of the industrial asset device, the data collected in real time is matched with a vulnerability library according to the fingerprint information, existing known vulnerabilities are identified, and possible taint propagation paths are deduced according to network topology and taint sources by constructing a network topological graph, so that the taint propagation efficiency is improved. The method comprises the following steps: identifying affected equipment, evaluating the flow change of the affected equipment, calculating the influence degree of a taint source on a network, identifying abnormal activities in a taint propagation process, evaluating the network security risk, and adjusting a monitoring strategy and a threshold value in time according to an evaluation result to ensure the accuracy and reliability of a monitoring system.

This invention is directed to an electronic device with an embedded authentication system for restricting access to device resources. The authentication system may include one or more sensors operative to detect biometric information of a user, The sensors may be positioned in the device such that the sensors may detect appropriate biometric information as the user operates the device, without requiring the user to perform a step for providing the biometric information (e.g., embedding a fingerprint sensor in an input mechanism instead of providing a fingerprint sensor in a separate part of the device housing). In some embodiments, the authentication system may be operative to detect a visual or temporal pattern of inputs to authenticate a user. In response to authenticating, a user may access restricted files, applications (e.g., applications purchased by the user), or settings (e.g., application settings such as contacts or saved game profile).

Provided is an electronic device. The electronic device may include a display, a fingerprint sensor provided in an area of the display, and at least one processor. The at least one processor may receive a user input for fingerprint registration, based on the user input, control the display to display a first user interface displaying information to guide a position for a touch input based on a position of the fingerprint sensor at a first position on the display, and based on receiving a first touch on the first user interface, control the display to display a second user interface indicating a degree of a fingerprint recognition related to the first touch.

An electronic device, method, and computer program product automatically support fingerprint authentication using a fingerprint scanner on an opposite side even when the electronic device is detected as being grasped upside down. The electronic device includes front and back fingerprint scanners respectively exposed on front and back sides of a device housing. In response to determining that a requirement exists for scanning a fingerprint of a user while the electronic device is upside down, a controller of the electronic device triggers at least one of the front and the back fingerprint scanners to initiate scanning. The controller receives a fingerprint image from a corresponding one of the front and back fingerprint scanner. The controller authenticates the user in response to identifying that an upside-down version of the received fingerprint image corresponds to a fingerprint of an authorized user, based on authentication data stored in memory of the electronic device.

A multi-port interface device for connecting multiple computing devices for data and power transfer includes a housing with an internal power source. A processor is electrically coupled to the internal power source. A power input port electrically coupled to the internal power source is designed to receive a power cord. The power cord is electrically couplable to an extrinsic power source to transfer power from the extrinsic power source to the internal power source. A plurality of ports electrically coupled to the processor is inset into the housing. Each port of the plurality of ports is exposed within the housing. The plurality of ports includes a plurality of universal serial bus ports and a memory card port.

Embodiments of the present application provide a fingerprint anti-counterfeiting neural network training method, a fingerprint anti-counterfeiting method and device, comprising: obtaining multiple sets of training data, each set of training data including: first native domain data, second native domain data and third native domain data; using the multiple sets of training data to train an initial classification network to obtain a target classification network, the initial classification network including a fusion subnetwork and a classification subnetwork, for each set of training data, the fusion subnetwork is used to generate a first fingerprint matching pair based on the feature description matrix of the first native domain data and the second native domain data, and generate a second fingerprint matching pair based on the feature description matrix of the first native domain data and the third native domain data, and the classification subnetwork is used for fingerprint classification and identification based on the first fingerprint matching pair and the second fingerprint matching pair. Through the present application, the problem of low identification accuracy of true and false fingerprints in related technologies is solved, and the effect of improving the identification accuracy of true and false fingerprints is achieved.

A fingerprint sensing system. The fingerprint sensing system includes: at least one sensor; at least one display device; at least one application processor; and at least one secure enclave processor. The application processor(s) receives fingerprint data from the sensor(s) and provides the fingerprint data to the secure enclave processor(s). The secure enclave processor(s) decodes the fingerprint data and provides a signal indicative of at least one matched node. The application processor(s), responsive to receipt of the signal indicative of the matched node(s), presents at least a portion of a synthetic fingerprint image via at least one display device corresponding to the matched node(s).

A fingerprint biometric key generation method based on scale invariance and a deep neural network. The object is small sample data, and fingerprint image signal processing knowledge is combined with deep neural network feature stability; fingerprint features are further stabilized at feature values by means of layer-by-layer processing of a deep neural network; and finally, a normal fingerprint image high-intensity key sequence is generated by means of a fingerprint key fuzzy extractor. A high-intensity stable fingerprint biometric key can be directly extracted from a human fingerprint small sample by means of processing such as scale invariant feature conversion and deep neural network re-encoding, and fingerprint image signal processing knowledge is combined with deep neural network feature stability to better support generation of a small sample fingerprint biometric key, thereby significantly reducing the false recognition rate during fingerprint biometric key generation.

The invention relates to the technical field of signal processing, and discloses a fingerprint medium consistency identification method comprising the following steps: obtaining current verification fingerprint data; searching target fingerprint template data matched with the current verification fingerprint data in a registered fingerprint template data set; searching a target feature descriptor corresponding to the target fingerprint template data in a template feature descriptor set; and generating a verification feature descriptor by using the pre-trained feature extraction network according to the current verification fingerprint data, and generating a current medium consistency probability of verification fusion features by using a pre-trained feature fusion classification network according to the verification feature descriptor and the target feature descriptor. According to the invention, the accuracy of fingerprint classification identification is improved.

An apparatus includes a memory and a processor. The memory stores biometric data obtained from an account holder at a first location and identification information provided by the account holder that was used to open an existing account at the first location. The processor receives, from a device, data corresponding to an applicant that includes biometric data obtained from the applicant at a second location and identification information provided by the applicant at the second location. The applicant is requesting to open a new account using the identification information at the second location. In response to receiving the applicant's data, the processor determines that the applicant's biometric data matches the account holder's biometric data. The processor also determines that the applicant's identification information does not match the account holder's identification information. In response, the processor transmits an alert to the device indicating that the applicant's identity could not be verified.

A system to generate an image classifier and test it nearly instantaneously is described herein. Image embeddings generated by an image fingerprinting model are indexed and an associated approximate nearest neighbors (ANN) model is generated. The embeddings in the index are clustered and the clusters are labeled. Users can provide just a few images to add to the index as a labeled cluster. The ANN model is trained to receive an image embedding as input and return a score and label of the most similar identified embedding. The label may be applied if the score exceeds a threshold value. The image classifier can be tested efficiently using Leave One Out Cross Validation (“LOOCV”) to provide near-instantaneous quality indications of the image classifier to the user. Near-instantaneous indications of outliers in the provided images can also be provided to the user using a distance to the centroid calculation.

Object description information that is transmitted by a biometric recognition apparatus is received, the object description information includes a biometric feature of a target object and location information of the target object. Identity information of the target object is obtained according to the biometric feature. A service information set in association with the identity information is obtained. From the service information set, one or more pieces of candidate service information are selected. The one or more pieces of candidate service information are transmitted to a terminal device associated with the identity information. At least a first piece of target service information returned by the terminal device is received. At least a first service corresponding to the first piece of target service information is processed. Apparatus and non-transitory computer-readable storage medium counterpart embodiments are also contemplated.

An authentication system comprises a storage device for storing biometric features, an imaging device for capturing a biometric feature, an image correction unit for converting the image quality of the image captured by the imaging device, and an authentication processing unit. The image correction unit generates an image before color correction of one or more images in which biometrics is captured and transforms the pre-color correction image by a plurality of values within a predetermined search range for color information to generate a plurality of color information transformed images. The image correction unit searches the color information conversion image that best represents the biometric feature for each biometric feature as the optimal color information for each biometric feature from the plurality of color information conversion images.

The invention discloses a PRNU-based camera source identification method, belongs to the technical field of camera traceability, and aims to solve the technical problem of how to improve the estimation quality of camera fingerprints and improve the accuracy of correlation scores in camera source identification. Comprising the following steps: constructing a camera fingerprint estimation model, introducing a channel attention mechanism into the camera fingerprint estimation model to perform feature extraction and weight distribution on an input image to obtain a weight feature map, extracting a PRNU of the input image through a PRNU extraction network, fusing the weight feature map and the PRNU of the input image, and outputting a camera fingerprint; and a correlation matching model is constructed, and the correlation matching model takes the PRNU of the query image and the camera fingerprint of the target camera as input, and predicts and outputs a correlation score between the query image and the target camera. And for a query image to be traced, outputting a correlation score between the query image and the target camera through the trained correlation matching model.

A palm image processing method is performed by a computer device. The method includes: obtaining a palm image, and performing feature extraction on the palm image, to obtain image features at a plurality of scales; fusing the image features at the plurality of scales, to obtain an image fusion feature; determining, based on the image fusion feature, a palm part frame identifying a palm part in the palm image as a palm part image; performing image encryption on the palm part image, to obtain encrypted palm part image data; and transmitting the encrypted palm part image data to a server, and the server performing identity recognition based on the encrypted palm part image data.

The invention relates to a fingerprint lock dynamic encryption authentication method and system and a medium. The method comprises a registration stage and an authentication stage. Wherein in the registration stage, lock body environment real-time physical parameters are collected, and a dynamic environment secret key is generated through a chaotic mapping function; acquiring a user fingerprint, identifying core and non-core feature regions, and encrypting the two types of regions by combining a dynamic environment key and a user key to generate registration encrypted fingerprint data; and dividing the data into two data fragments by using a quantum noise source true random sequence, storing the first data fragment in a local security chip, and encrypting the second data fragment and storing the encrypted second data fragment in a cloud server. In the authentication stage, environment parameters are collected in real time to generate a verification key, and the verification key is compared with a registration key; and when the two fragments are consistent, recombining the two fragments to restore the encrypted fingerprint data, authenticating the fingerprint input by the user, unlocking if the authentication succeeds, and triggering risk grading processing if the continuous failure reaches a threshold value. The overall safety and reliability of the fingerprint lock system are improved.