Face recognition model training method and device and face recognition method and device

Abstract

The invention provides a face recognition model training method and device and a face recognition method and device. The face recognition model training method comprises the following steps of: inputting a comparison image set into a first neural network and extracting a first feature vector for each comparison image in the comparison image set by using the first neural network; inputting a sourceimage set and a target image set into a second neural network, carrying out feature learning on source images in the source image set and target images in the target image set, and extracting a second feature vector for each source image in the source image set; inputting the second feature vectors into a face classifier to obtain classification results; training the second neural network and theface classifier on the basis of comparison results between the first feature vectors and the corresponding second feature vectors and the classification results; and repeatedly training the second neural network and the face classifier to obtain a face recognition model. The method is capable of effectively recognizing face images with relatively bad quality.

Description

technical field [0001] The present invention relates to the technical field of image processing, and in particular, to a face recognition model training method and device, and a face recognition method and device. Background technique [0002] Traditional identity authentication methods mainly use identity items (such as keys, ID documents, bank cards, etc.) and identity knowledge (such as user names and passwords) to prove identity. Since such methods require the help of external objects or the memory of the person themselves, once the personal identification items and identification knowledge to prove the identity are stolen or forgotten by the outside world, their identity is easily impersonated or replaced by others. Different from traditional identity authentication methods, biometrics (signature, fingerprint, face, iris, palm print, etc.) have become a popular feature of uniqueness, lifetime invariance, portable, not easy to be lost and fraudulently used, and good anti...

AI Technical Summary

Problems solved by technology

Due to the 3D structure of the face, the shadow cast by the light will strengthen or weaken the original face features

In addition, there may be various sources of face images. Due to different acquisition devices, the quality of face images obtained is also different, especially for those face images with low resolution, high noise and other poor quality. Face recognition methods are not effective for face recognition

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