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Entropy 2017, 19(5), 228; doi:10.3390/e19050228

Face Verification with Multi-Task and Multi-Scale Feature Fusion

1
College of Sciences, Northeastern University, Shenyang 110819, China
2
Department of Mathematics, New York University Shanghai, 1555 Century Ave, Pudong, Shanghai 200122, China
*
Author to whom correspondence should be addressed.
Academic Editor: Maxim Raginsky
Received: 18 March 2017 / Revised: 5 May 2017 / Accepted: 13 May 2017 / Published: 17 May 2017
(This article belongs to the Special Issue Information Theory in Machine Learning and Data Science)
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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. View Full-Text
Keywords: deep convolutional neural networks; identification; semi-verification; multi-scale features; face verification deep convolutional neural networks; identification; semi-verification; multi-scale features; face verification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Lu, X.; Yang, Y.; Zhang, W.; Wang, Q.; Wang, Y. Face Verification with Multi-Task and Multi-Scale Feature Fusion. Entropy 2017, 19, 228.

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