Next Article in Journal
Ion Hopping and Constrained Li Diffusion Pathways in the Superionic State of Antifluorite Li2O
Next Article in Special Issue
A Novel Geometric Dictionary Construction Approach for Sparse Representation Based Image Fusion
Previous Article in Journal
Investigation of the Intra- and Inter-Limb Muscle Coordination of Hands-and-Knees Crawling in Human Adults by Means of Muscle Synergy Analysis
Previous Article in Special Issue
Discovery of Kolmogorov Scaling in the Natural Language
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Entropy 2017, 19(5), 228;

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

College of Sciences, Northeastern University, Shenyang 110819, China
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)
Full-Text   |   PDF [2416 KB, uploaded 17 May 2017]   |  


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

Figure 1

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).

Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top