A deep learning framework for face verification without alignment

Abstract

Most of the CNN (convolutional neural networks) methods require alignment, which will affect the efficiency of verification. This paper proposes a deep face verification framework without alignment. First and foremost, the framework consists of two training stages and one testing stage. In the first training stage, the CNN is fully trained on the large face dataset. In the second training stage, embedding triplet is adopted to fine-tune the models. Furthermore, in the testing stage, SIFT (scale invariant feature transform) descriptors are extracted from intermediate pooling results for cascading verification, which effectively improves the accuracy of face verification without alignment. Last but not least, two CNN architectures are designed for different scenarios. The CNN1 (convolutional neural networks 1), with fewer layers and parameters, requires a small amount of memory and computation in training and testing, so it is suitable for real-time system. The CNN2 (convolutional neural networks 2), with more layers and parameters, has excellent face verification. Through the long-term training on WEB-face dataset and experiments on the LFW (labled faces in the wild), YTB (YouTube) datasets, the results show that the proposed method has superior performance compared with some state-of-the-art methods.