Abstract
Face verification for unrestricted faces in the wild is a challenging task. This paper proposes a method based on two deep convolutional neural networks (CNN) for face verification. In this work, we explore using identification signals to supervise one CNN and the combination of semi-verification and identification to train the other one. In order to estimate semi-verification loss at a low computation cost, a circle, which is composed of all faces, is used for selecting face pairs from pairwise samples. In the process of face normalization, we propose using different landmarks of faces to solve the problems caused by poses. In addition, the final face representation is formed by the concatenating feature of each deep CNN after principal component analysis (PCA) reduction. Furthermore, each feature is a combination of multi-scale representations through making use of auxiliary classifiers. For the final verification, we only adopt the face representation of one region and one resolution of a face jointing Joint Bayesian classifier. Experiments show that our method can extract effective face representation with a small training dataset and our algorithm achieves 99.71% verification accuracy on Labeled Faces in the Wild (LFW) dataset.
Highlights
With the convolution neural network, in recent years, the vision community has made great progress in many challenge problems, such as object detection [1], semantic segmentation [2], object classifiaction [3] and so on
Face verification methods based on deep convolutional neural networks (CNNs) have achieved high performance [4,5,6,7]
The rest of this paper is constructed as follows: we introduce the semi-verification signal in Section 2, which is used for supervising the training of one deep CNN
Summary
With the convolution neural network, in recent years, the vision community has made great progress in many challenge problems, such as object detection [1], semantic segmentation [2], object classifiaction [3] and so on. Face verification methods based on deep convolutional neural networks (CNNs) have achieved high performance [4,5,6,7]. We use the deep CNN as a feature extractor and adopt an extra classifier to make face representation more discriminative as in [5,6,7]. Most face verification methods catenate face representations of multi-resolutions and multi-regions based on deep CNNs to construct a feature with high dimension [6,7]. This will conduct high computation and a large burden of storage.
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