Abstract: An identity recognition method includes: acquiring biometric pattern information describing a biometric pattern of a first object; performing feature extraction on the biometric pattern information to obtain a global pattern feature and a local pattern feature; fusing the global pattern feature and the local pattern feature to obtain a fused pattern feature of the first object; and performing identity recognition on the first object based on the fused pattern feature of the first object.

Abstract: The present disclosure discloses an earphone. The earphone may include a hook-shaped component, a connecting component, and a holding component. When the earphone is in a wearing state, the hook-shaped component may be configured to hang between a rear side of an ear of a user and a head of the user. The holding component may be configured to contact a front side of the ear. The connecting component may be configured to connect the hook-shaped component and the holding component and extend from the head to an outside of the head to cooperate with the hook-shaped component to provide the holding component with a pressing force on the front side of the ear.

Abstract: A true three-dimensional physical simulation system for the influence of fault movement on tunnel operation and a test method are disclosed. The system includes a fault movement rack system, a fault movement jacking system, and a loading and movement control system. The fault movement rack system is configured to accommodate a movement model and serves as a loading reaction device. The fault movement jacking system is configured to implement fault movement and ensures that the model is not twisted and overturned in a fault movement process. The loading and movement control system is configured to perform initial ground stress loading on the model and control the fault movement jacking system to implement fault movement of the model.

Abstract: A method for generating a palmprint sample includes: generating positioning point data according to a palmprint principal line distribution rule, the positioning point data comprising first data corresponding to a first principal line positioning point and second data corresponding to a second principal line positioning point; generating adjusting point data according to a palmprint principal line curving rule, a principal line adjusting point corresponding to the adjusting point data being configured to control a curving parameter of a principal line formed by the first principal line positioning point and the second principal line positioning point; generating a palmprint principal line based on the first data, the second data and the adjusting point data; and generating at least one palmprint sample comprising the palmprint principal line.

Abstract: A true three-dimensional physical simulation system for the influence of fault movement on tunnel operation and a test method, including a fault movement rack system, a fault movement jacking system, and a loading and movement control system. The fault movement rack system accommodates a movement model and serves as a loading reaction device. The fault movement jacking system implements fault movement and ensures that the model is not twisted and overturned in a fault movement process. The loading and movement control system performs initial ground stress loading on the model and controls the fault movement jacking system to implement fault movement of the model. Fault movement of a deep stratum under a complex high ground stress condition can be simulated, the influence of underground fault movement on safe and stable operation of a deep tunnel is simulated, and a powerful technical support is provided for safe construction of deep engineering.

Abstract: Methods and systems for milling and imaging a sample based on multiple fiducials at different sample depths include forming a first fiducial on a first sample surface at a first sample depth; milling at least a portion of the sample surface to expose a second sample surface at a second sample depth; forming a second fiducial on the second sample surface; and milling at least a portion of the second sample surface to expose a third sample surface including a region of interest (ROI) at a third sample depth. The location of the ROI at the third sample depth relative to the first fiducial may be calculated based on an image of the ROI and the second fiducial as well as relative position between the first fiducial and the second fiducial.

Abstract: A face image quality assessment method includes acquiring a face image, and determining a similarity distribution distance of the face image, the similarity distribution distance including a distribution distance between an intra-person similarity distribution of the face image and an inter-person similarity distribution of the face image. The intra-person similarity distribution indicates a similarity distribution between the face image and a first-type image of a same person as the face image, and the inter-person similarity distribution indicates a similarity distribution between the face image and a second-type image of a different person from the face image. The method further includes determining image quality of the face image based on the similarity distribution distance of the face image.

Abstract: This application discloses a method and an apparatus for selecting a face image, a device, and a storage medium and relates to the field of artificial intelligence technologies. The method includes detecting, after a frame of face image is obtained, whether the face image meets a preliminary quality screening condition; determining, in response to a first face image meeting the preliminary quality screening condition, an overall quality score of the first face image, the overall quality score representing overall quality of the face image; and transmitting the first face image to a face recognition process in response to the overall quality score of the first face image being greater than a level-one threshold.

Abstract: Embodiments of this application disclose a palm print recognition method, a method for training a feature extraction model, a device, and a storage medium, and belong to the field of computer technologies. The method includes: obtaining sample hand images for each of a plurality of sample user identifiers, at least two of the sample hand images associated with the each of the plurality of sample user identifier being acquired from image acquisition devices having different image acquisition characteristics; calling a feature extraction model to extract sample palm print features of the plurality of sample user identifiers; and adjusting the feature extraction model according to the sample palm print features of the plurality of sample user identifiers.

Abstract: Methods and systems for milling and imaging a sample based on multiple fiducials at different sample depths include forming a first fiducial on a first sample surface at a first sample depth; milling at least a portion of the sample surface to expose a second sample surface at a second sample depth; forming a second fiducial on the second sample surface; and milling at least a portion of the second sample surface to expose a third sample surface including a region of interest (ROI) at a third sample depth. The location of the ROI at the third sample depth relative to the first fiducial may be calculated based on an image of the ROI and the second fiducial as well as relative position between the first fiducial and the second fiducial.

Abstract: The present disclosure describes method, apparatus, and storage medium for obtaining object information. The method includes obtaining a to-be-tracked image comprising at least one object and at least one reference image comprising a plurality of objects; extracting a to-be-tracked image block comprising a plurality of to-be-tracked points from the to-be-tracked image and extracting a reference image block comprising a plurality of reference points from a reference image of the at least one reference image; constructing a point transformation relationship between the to-be-tracked image block and the reference image block based on a position relationship between the plurality of to-be-tracked points and a position relationship between the plurality of reference points; and obtaining a position of a reference point in the reference image corresponding to a to-be-tracked point based on the point transformation relationship, to determine an object in the reference image corresponding to the at least one object.

Abstract: An image processing method is provided, including: obtaining a target image; invoking an image recognition model including: a backbone network, a pooling module and a dilated convolution module that are connected to the backbone network and that are parallel to each other, and a fusion module connected to the pooling module and the dilated convolution module; performing feature extraction on the target image by extracting, using the backbone network, a feature map of the target image, separately processing, using the pooling module and the dilated convolution module, the feature map, to obtain a first result outputted by the pooling module and a second result outputted by the dilated convolution module, and fusing the first result and the second result by using the fusion module into a model recognition result of the target image; and determining a semantic segmentation labeled image of the target image based on the model recognition result.

Abstract: The present disclosure describes method, apparatus, and storage medium for obtaining object information. The method includes obtaining a to-be-tracked image comprising at least one object and at least one reference image comprising a plurality of objects; extracting a to-be-tracked image block comprising a plurality of to-be-tracked points from the to-be-tracked image and extracting a reference image block comprising a plurality of reference points from a reference image of the at least one reference image; constructing a point transformation relationship between the to-be-tracked image block and the reference image block based on a position relationship between the plurality of to-be-tracked points and a position relationship between the plurality of reference points; and obtaining a position of a reference point in the reference image corresponding to a to-be-tracked point based on the point transformation relationship, to determine an object in the reference image corresponding to the at least one object.

Abstract: An image processing method is provided, including: obtaining a target image; invoking an image recognition model including: a backbone network, a pooling module and a dilated convolution module that are connected to the backbone network and that are parallel to each other, and a fusion module connected to the pooling module and the dilated convolution module; performing feature extraction on the target image by extracting, using the backbone network, a feature map of the target image, separately processing, using the pooling module and the dilated convolution module, the feature map, to obtain a first result outputted by the pooling module and a second result outputted by the dilated convolution module, and fusing the first result and the second result by using the fusion module into a model recognition result of the target image; and determining a semantic segmentation labeled image of the target image based on the model recognition result.

Abstract: The present disclosure discloses a living body identification method, an information generation method, and a terminal, and belongs to the field of biometric feature recognition. The method includes: providing lip language prompt information, the lip language prompt information including at least two target characters, and the at least two target characters being at least one of: characters of a same lip shape, characters of opposite lip shapes, or characters whose lip shape similarity is in a preset range; collecting at least two frame pictures; detecting whether lip changes of a to-be-identified object in the at least two frame pictures meet a preset condition, when the to-be-identified object reads the at least two target characters; and determining that the to-be-identified object is a living body, if the preset condition is met.

Abstract: An image classification method is provided. The method includes: inputting a to-be-classified image into a plurality of neural network models; obtaining data output by multiple non-input layers specified by each neural network model to generate a plurality of image features corresponding to the plurality of neural network models; respectively inputting the plurality of corresponding image features into linear classifiers, each of the linear classifiers being trained by one of the plurality of neural network models for determining whether an image belongs to a preset class; obtaining, using each neural network model, a corresponding probability that the to-be-classified image comprises an object image of the preset class; and determining, according to each obtained probability, whether the to-be-classified image includes the object image of the preset class.

Abstract: An image classification method is provided. The method includes: inputting a to-be-classified image into a plurality of neural network models; obtaining data output by multiple non-input layers specified by each neural network model to generate a plurality of image features corresponding to the plurality of neural network models; respectively inputting the plurality of corresponding image features into linear classifiers, each of the linear classifiers being trained by one of the plurality of neural network models for determining whether an image belongs to a preset class; obtaining, using each neural network model, a corresponding probability that the to-be-classified image comprises an object image of the preset class; and determining, according to each obtained probability, whether the to-be-classified image includes the object image of the preset class.

Abstract: The present disclosure discloses a living body identification method, an information generation method, and a terminal, and belongs to the field of biometric feature recognition. The method includes: providing lip language prompt information, the lip language prompt information including at least two target characters, and the at least two target characters being at least one of: characters of a same lip shape, characters of opposite lip shapes, or characters whose lip shape similarity is in a preset range; collecting at least two frame pictures; detecting whether lip changes of a to-be-identified object in the at least two frame pictures meet a preset condition, when the to-be-identified object reads the at least two target characters; and determining that the to-be-identified object is a living body, if the preset condition is met.