A wearable article includes: an annular casing that surrounds a space into which a body of a user is to be inserted; a light-emitting element that is provided in the casing, the light-emitting element emitting light towards the space; an imaging element that is provided in the casing, the imaging element capturing and obtaining an image of the space when the light-emitting element emits light; and an authentication circuit that authenticates the user based on a vein pattern obtained in advance and the image.

The embodiment of the disclosure provides an unlocking control method and related products. The method includes: acquiring an environmental parameter; acquiring first biometric information; determining a first biometric control parameter and second biometric control information corresponding to the environmental parameter; performing a first biometric recognition on the first biometric information according to the first biometric control parameter; when the first biometric information is recognized, acquiring second biometric information and performing a second biometric recognition on the second biometric information according to the second biometric control information; performing a next unlocking process when the second biometric information is recognized. Thus, control parameters of recognition processes can be set suitable for the environment, and recognition processes are controlled based on these control parameters, thereby improving the pass rate and the efficiency of the multi-biometric recognition.

An authentication method including: accepting biometric information to be authenticated detected by a biometric sensor; specifying, on a basis of an orientation of a line of sight or an orientation of a face of one or more persons that appear in a captured image captured by an imaging device at a time of detecting the biometric information to be authenticated, a face image of any one of the persons included in the captured image; selecting registered biometric information associated with registered face image information similar to the specified face image from among pieces of registered biometric information respectively associated with a plurality of pieces of registered face image information; and authenticating the biometric information to be authenticated on a basis of a comparison result of comparing the biometric information to be authenticated with the selected registered biometric information.

A detection device includes a substrate, a plurality of photodiodes that are arranged in a detection region of the substrate, a plurality of first terminals that are arranged in a first direction in a peripheral region outside the detection region of the substrate, an insulating film that covers the first terminals, and an anisotropic conductive film that is located above the insulating film, and covers the first terminals.

Provided are a virtual stent placement apparatus, a virtual stent placement method, and a virtual stent placement program that simplify an operation of virtually placing a stent in a blood vessel extracted from a medical image. An extraction unit (22) extracts a blood vessel region (30) from a three-dimensional image (V0). A display control unit (26) displays a three-dimensional image (V1) including the blood vessel region (30). The information acquisition unit (23) acquires information of the diameter of a virtual stent placed in the blood vessel region (30), a maximum contour length of the virtual stent, and a start position (S1) in a case in which the virtual stent is placed. A placement unit (24) places the virtual stent having a maximum contour length (L0) from the start position (S1) along a maximum contour line of the blood vessel region (30) in the blood vessel region (30).

A method and apparatus with selective combined authentication performs a single authentication based on a first modality among plural modalities, and in response to the single authentication having failed, determines whether to perform a combined authentication by a combination of two or more of the plural modalities, and selectively, depending on a result of the determining of whether to perform the combined authentication, performs the combined authentication.

Systems and methods are provided for multi-schema analysis of patient specific anatomical features from medical images. The system may receive medical images of a patient and metadata associated with the medical images indicative of a selected pathology, and automatically classify the medical images using a segmentation algorithm. The system may use an anatomical feature identification algorithm to identify one or more patient specific anatomical features within the medical images by exploring an anatomical knowledge dataset. A 3D surface mesh model may be generated representing the one or more classified patient specific anatomical features, such that information may be extracted from the 3D surface mesh model based on the selected pathology. Physiological information associated with the selected pathology for the 3D surface mesh model may be generated based on the extracted information.

Systems and methods are provided for multi-schema analysis of patient specific anatomical features from medical images. The system may receive medical images of a patient and metadata associated with the medical images indicative of a selected pathology, and automatically classify the medical images using a segmentation algorithm. The system may use an anatomical feature identification algorithm to identify one or more patient specific anatomical features within the medical images by exploring an anatomical knowledge dataset. A 3D surface mesh model may be generated representing the one or more classified patient specific anatomical features, such that information may be extracted from the 3D surface mesh model based on the selected pathology. Physiological information associated with the selected pathology for the 3D surface mesh model may be generated based on the extracted information.

The present invention relates to a biometric-based identity authentication method and system. The method includes: obtaining mobile terminal numbers of all users entering a specified area through a base station associated with the specified area which the users enter to generate a first mobile terminal number list; recognizing biometrics of users, and obtaining a second mobile terminal number list composed of n mobile terminal numbers with the highest similarity to the biometrics based on a pre-established binding relationship between biometrics of users and the mobile terminal numbers; and comparing the first mobile terminal number list with the second mobile terminal number list, wherein on the condition that the intersection of the two is one mobile terminal number, it is determined that the user of the mobile terminal number is the user with successful identity authentication, and on the condition that the intersection of the two is more than one number, it is determined that the user of the mobile terminal number with the highest biometric similarity in the intersection is the user with successful identity authentication. According to the present invention, the range of face recognition N can be narrowed down, and a user only needs to carry a mobile phone and 1:N face recognition can be completed without additional operations.

A data processing method includes: acquiring an initial sample angiography image set; performing data expansion processing on a first sample angiography image based on physical characteristics of blood vessels at a target site to obtain a processed sample angiography image, performing label conversion processing on a first label based on the physical characteristics of the blood vessels at the target site to obtain a second label of the processed sample angiography image, and adding the processed sample angiography image and the second label to a target sample angiography image set; and training an angiography image recognition model using the initial sample angiography image set and the target sample angiography image set to obtain a trained angiography image recognition model. The performance of the trained angiography image recognition model is improved by increasing the number of samples.