Deep learning face verification method based on mixed training

Abstract

The invention provides a deep learning face verification method based on mixed training. The method comprises the steps that a face data set is prepared; face and face key point detection is conducted on all images; all faces are normalized to obtain a face image training set, the face image training set is partitioned into a training data set and a verification data set, a mean image of all face images is calculated; the mean image is subtracted from all the face images to obtain a mean training data set and a mean verification data set; a deep convolutional neural network is trained; a corresponding triad is generated for each face image, and a triad training data set and a triad verification data set are formed; the deep convolutional neural network is trained again; face and face feature point detection is conducted on two given images to be verified, the mean image is subtracted from the images, the images are input into the deep convolutional neural network, a network feedforward operation is conducted, and features are extracted; according to a selected threshold value, when the distance between the extracted features of the two images is larger than the threshold value, it is judged that the faces in the two images belong to a same person, and otherwise, it is judged that the faces belong to different persons.

Description

Technical field [0001] The invention relates to face recognition in computer vision, in particular to a deep learning face verification method based on mixed training. Background technique [0002] Face recognition is a method of biometric identification. Compared with other traditional biometric recognition methods, face recognition has the advantages of non-contact, concealment, and high user acceptance. Face recognition is widely used in national security, security, access control and other fields, and has huge market value and scientific research value. Face recognition is an image-based recognition method. The challenge of image-based recognition methods is how to obtain effective feature representations from images for subsequent tasks such as recognition and classification. [0003] The traditional face recognition method decomposes the recognition task into two independent parts of artificial feature design and classifier training for learning. Face feature extraction p...

AI Technical Summary

Problems solved by technology

However, there are still some problems in the various methods based on the deep convolutional neural network model: First, the large demand for training data

It is sometimes difficult to obtain a large amount of training data.

Second, the time complexity of model training is high and the amount of calculation is large

For example, in FaceNet, in order to train a good model, it takes more than 2,000h of training time

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