FEATURE LEARNING FOR ONE-SHOT FACE RECOGNITION

One-shot face recognition is a challenging open problem which requires recognizing novel identities from only one gallery face. One-shot classes are squeezed and neglected in the feature space for classification due to data imbalance. Moreover, training samples deficience is a major obstacle to intra-class clustering. In this paper, we propose a novel framework based on CNN of balancing regularizer and shifting center regeneration which regulates norms of weight vector into same scale and adjusts clustering center to deal with deficient training data. Comprehensive evaluations on MS-celeb-1M low-shot face dataset demonstrate that our methods improve one-shot face recognition notablely which achieve 88.78% coverage at precision=0.99 using restricted data without hybrid classifiers or multi-model. Moreover, experiments on LFW prove that CNN model trained with proposed methods can obtain more discriminative and compact feature representations. Since there are many identities that have only few training samples available online, our methods have great significance for improving data utilization and strengthening feature representation for face recognition.