Face recognition working method through fusion of binary features and a joint laminated structure, and an intelligent chip

Abstract

The invention provides a face recognition working method through fusion of binary features and a joint laminated structure and an intelligent chip. The method comprises the following steps: S1, acquiring face feature data in image features, and constructing a face feature data hash layer in a convolutional neural network, S2, enabling a face feature data hash function to meet the constraints of independence and minimum quantization error, and solving the face feature data hash function; S3, performing deep face feature data transformation on the solved face feature data hash function by usingcascade structure data operation; s4, through a depth binary human face feature data hash function, extracted human face feature data and human face feature data key point detection, human face feature data posture estimation and human face feature data classification are carried out; and S5, after face feature data mining, face image comparison and recognition are carried out by utilizing similarity, face feature data are identified and extracted, and required face feature data are obtained.

Description

technical field [0001] The invention relates to the field of computer control, in particular to a face recognition working method and an intelligent chip through the fusion of binary features and a joint stacked structure. Background technique [0002] In the research and application of existing face recognition algorithms, the similar proximity search technology used in face verification or face identification has become one of the important factors affecting the speed of the algorithm. How to further improve the speed of similar proximity search technology has become a research topic. top priority. [0003] Researchers have proposed two ideas to solve this problem. One is to use dimensionality reduction methods on the original feature representation to obtain shorter feature representations, such as the principal component analysis (Principal Component Analysis, PCA) method; the other is to use The feature representation is converted into a binary feature representation; ...

AI Technical Summary

Problems solved by technology

However, at present, many supervised hashing methods usually use image pairs or image triplets for training, which requires a lot of calculation and storage costs in the training phase, and ultimately makes model training almost impossible.

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