A Face Recognition Technique by Representation Learning with Quadruplets

Face recognition is a key task of computer vision research that has been employed for various purposes. There are many studies in the literature that attempts for solving this problem. Especially in recent years, deep learning methods have been utilized. With these methods, algorithms can reach higher accuracy levels. In addition to deep learning, the use of representative learning is used to aggregate face images in multidimensional metric spaces. While clustering, the similarities in the faces are generally utilized with the help of Siamese or triplet cost functions. In the studies following this approach, the recognition of the faces is done only by using identity labels. However, in this paper, we propose the method which is utilizing both the identity labels and the attribute labels such as the light intensity of the images and the direction of the face. Therefore, two labels are available for each image. In this study, the images in the Yale-B face dataset are used for training a state-of-the-art architecture, ResNet with the labels which are re-organized by us. In order to benefit from two labels during the training, we employ quadruplet selection methods and a quadratic cost function. The contribution of secondary labels on recognition accuracy was measured in experiments. In addition, the methods for selecting quadruplets are compared.