Know You at One Glance: A Compact Vector Representation for Low-Shot Learning

Yu Cheng, Jian Zhao, Zhecan Wang, Yan Xu, Karlekar Jayashree, Shengmei Shen, Jiashi Feng; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 1924-1932

In this paper, we propose an enforced Softmax optimization approach which is able to improve the model's representational capacity by producing a "compact vector representation" for effectively solving the challenging low-shot learning face recognition problem. Compact vector representations are significantly helpful to overcome the underlying multi-modality variations and remain the primary key features as close to the mean face of the identity as possible in the high-dimensional feature space. Therefore, the gallery facial representations become more robust under various situations, leading to the overall performance improvement for low-shot learning. Comprehensive evaluations on the MNIST, LFW, and the challenging MS-Celeb-1M Low-Shot Learning Face Recognition benchmark datasets clearly demonstrate the superiority of our proposed method over state-of-the-arts.