Research And Implementation Of Face Recognition Based On Caffe Platform With Deep Learning

With the rapid development of the artificial intellegence and pattern recognition, face recognition has become a research hotspot in the field of computer vision. Especially since deep learning was proposed, the performance of face recognition algorithm has been hugely promoted. Currently, there are varieties of applications based on face recognition in the market, such as expression recognition, face search, and so on. In addition, with the wide spread use of smartphone and fast mobile networks, face recognition, as a reliable authentication technology, has resulted in an increased interest in research. Face recognition system can be mainly divided into two broad categories: 1:1 face verification and 1:N face recognition. This paper focuses on 1:N face recognition, the difficulty of which is that with the increase of the number N, the performance of the traditional recognition algorithm is poor, but the essence of deep learning is to deal with big data. Therefore, deep learning is used to solve the problem of large scale face recognition. And a deep convolutional neural network was designed and implemented for face recognition task with the use of Caffe. Finally, face recognition softwares based on the Web and Android platform are designed. The main contents of this paper are as follows:This paper briefly introduces the related technology of face recognition and deep learning. And Caffe, a deep learning framework, which is used to design the deep neural network, is introduced in detail, as well as its advantages and how to setup the development enviroment. Then a deep convolutional neural network for face recognition task is implemented with the help of Caffe. The network which contains 5 convolutional layers and 3 pooling layers is trained on the Youtube face databases. From feature visualization analysis and experimental results, it shows that: compared with the handcrafted feature methods, the network designed in this paper can effectively represent the facial image, and the recognition rate surpasses the traditional methods such as eigenface and so on; compared with VGG-16 model, the designed network can satisfy the performance requirement using less weight layers. Then, a face recogniton software based on Web platform is designed and implemented, including face recognition module, URL bindings, file upload, and an HTML web page for users to browse. And an Android mobile phone client software is implemented.Finally, the software is tested and the results show that: the software designed in this paper gains a high recognition rate for frontal facial image and can satisfy the performance requirements under the circumstance of single background and good light condition. In addition, the designed software is proved practical, which can be applied to mobile phone for user login by brushing face and other systems.