Using Siamese Networks with Transfer Learning for Face Recognition on Small-Samples Datasets
Paper ID : 1098-MVIP2020
Mohsen Heidari1, Kazim Fouladi *2
1Deep Learning Research Lab, Department of Computer Engineering, Faculty of Engineering, College of Farabi, University of Tehran, Iran
2University of Tehran
Nowadays, computer-based face recognition is a mature and reliable mechanism that is significantly used in many access control scenarios along with other biometric methods. Face recognition consists of two subtasks including Face Verification and Face Identification. By comparing a pair of images, Face Verification determines whether those images are related to one person or not; and Face Identification has to identify a specific face within a set of available faces in the database. There are many challenges in face recognition such as angle, illumination, pose, facial expression, noise, resolution, occlusion and the few number of one-class samples with several classes. In this paper, we are carrying out face recognition by utilizing transfer learning in a siamese network which consists of two similar CNNs. In the siamese network, a pair of two face images is given to the network as input, then the network extracts the features of this pair of images and finally, it determines whether the pair of images belongs to one person or not by using a similarity criterion. The results show that the proposed model is comparable with advanced models that are trained on datasets containing large numbers of samples. furthermore, it improves the accuracy of face recognition in comparison with methods which are trained using datasets with a few number of samples, and the mentioned accuracy is claimed to be 95.62% on LFW dataset.
Face Recognition, Convolutional Neural Networks, Siamese Network, Transfer Learning, Small-Sample Dataset
Status : Paper Accepted (Oral Presentation)