Abstract: A method, storage media and neural network for rebuilding biometric feature is provided. The method includes inputting the partial texture image obtained to the neural network and outputting a predictive value of an entire texture image that is output by the neural network. The above technical solution is via the neural network used to process images and the neural network includes the feature value layer. A plurality of the partial texture images is converted to the feature values at the technical level, and the composite calculation of a plurality of partial texture images is avoided on the application level. Because the entire texture image is not synthesized in the end, data leakage and theft are avoided. Thus, the security of the method for analyzing texture image is improved.

Abstract: A biometric analysis structure, method and neural network with coded mask are provided. The biometric analysis structure includes a display panel, a light source, and a sensor. The sensor is disposed on the optical path of light from the light source and reflected by the display panel. The biometric analysis structure includes an coded mask. The coded mask is disposed on the optical path in front of the sensor. the coded mask is represented as a first matrix in a matrix form and the first matrix is a delta function after satisfying an autocorrelation operation. The resulting image can be inversely resolved based on the image of the coded mask. Thus, the security of the fingerprint recognition method is improved, and the thickness of the entire imaging structure is reduced.

Abstract: A biometric analysis method and a storage medium are provided. The biometric analysis method includes steps of acquiring a biometric image, acquiring a feature point in the biometric image, and storing descriptors corresponding to the feature point and corresponding relationship between the descriptors and the feature point. The biometric analysis method further includes a step of not storing the biometric image. The above solutions can effectively improve operational efficiency and user experience.

Abstract: A method, storage media and neural network for rebuilding biometric feature is provided. The method includes inputting the partial texture image obtained to the neural network and outputting a predictive value of an entire texture image that is output by the neural network. The above technical solution is via the neural network used to process images and the neural network includes the feature value layer. A plurality of the partial texture images is converted to the feature values at the technical level, and the composite calculation of a plurality of partial texture images is avoided on the application level. Because the entire texture image is not synthesized in the end, data leakage and theft are avoided. Thus, the security of the method for analyzing texture image is improved.

Abstract: A biometric analysis structure, method and neural network with coded mask are provided. The biometric analysis structure includes a display panel, a light source, and a sensor. The sensor is disposed on the optical path of light from the light source and reflected by the display panel. The biometric analysis structure includes an coded mask. The coded mask is disposed on the optical path in front of the sensor. the coded mask is represented as a first matrix in a matrix form and the first matrix is a delta function after satisfying an autocorrelation operation. The resulting image can be inversely resolved based on the image of the coded mask. Thus, the security of the fingerprint recognition method is improved, and the thickness of the entire imaging structure is reduced.

Abstract: A biometric analysis method and a storage medium are provided. The biometric analysis method includes steps of acquiring a biometric image, acquiring a feature point in the biometric image, and storing descriptors corresponding to the feature point and corresponding relationship between the descriptors and the feature point. The biometric analysis method further includes a step of not storing the biometric image. The above solutions can effectively improve operational efficiency and user experience.