Face recognition method and device

Abstract

The embodiment of the invention discloses a face recognition method and a device. The method comprises the steps of extracting the Haar feature of a current to-be-recognized face image, and detecting the human face area of the to-be-recognized face image by adopting an ADaBoost classifier so as to obtain a face region image; performing the multi-scale feature extraction on the face region image by utilizing a convolution neural network model, and obtaining a feature vector of the face region image; inputting the feature vector, a pre-built legal face database and a preset user similarity threshold value into a multi-task learning model pre-constructed according to a Softmax loss function and a Triplet loss function, and judging whether the to-be-recognized face image is a legal user or not according to the output value of the multi-task learning model. The extracted feature is good in robustness and good in generalization ability. Therefore, not only the face recognition rate improved, but also the accuracy of face recognition is improved. The safety of identity authentication is improved.

Description

Technical field [0001] The embodiments of the present invention relate to the field of image processing technology, and in particular, to a face recognition method and device. Background technique [0002] With the rapid development of computer technology and image processing technology, since the human face is innate with other biological characteristics of the human body (such as fingerprints, iris, etc.), its uniqueness and good characteristics such as difficult to copy provide the necessary for identity authentication. Therefore, it has been widely used in the field of public safety. Face recognition technology is a computer technology that uses the analysis and comparison of the visual feature information of the face to perform identity authentication. [0003] Face recognition is to use a camera or camera to collect images or video streams containing human faces, and automatically detect and track the human face in the image, and then extract the relevant feature information...

AI Technical Summary

Problems solved by technology

However, the above methods mostly rely on manual feature extraction, which is not only time-consuming and laborious, but also largely relies on experience or prior knowledge. The overall system recognition efficiency is low, and good generalization effects cannot be obtained. Manual participation cannot avoid extracting errors features, resulting in a decrease in the accuracy of the overall face recognition

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