Version 1
: Received: 18 March 2017 / Approved: 20 March 2017 / Online: 20 March 2017 (09:06:18 CET)
How to cite:
Lu, X.; Yang, Y.; Zhang, W.; Wang, Q.; Wang, Y. Face Verification with Multi-Task and Multi-Scale Features Fusion. Preprints2017, 2017030152. https://doi.org/10.20944/preprints201703.0152.v1
Lu, X.; Yang, Y.; Zhang, W.; Wang, Q.; Wang, Y. Face Verification with Multi-Task and Multi-Scale Features Fusion. Preprints 2017, 2017030152. https://doi.org/10.20944/preprints201703.0152.v1
Lu, X.; Yang, Y.; Zhang, W.; Wang, Q.; Wang, Y. Face Verification with Multi-Task and Multi-Scale Features Fusion. Preprints2017, 2017030152. https://doi.org/10.20944/preprints201703.0152.v1
APA Style
Lu, X., Yang, Y., Zhang, W., Wang, Q., & Wang, Y. (2017). Face Verification with Multi-Task and Multi-Scale Features Fusion. Preprints. https://doi.org/10.20944/preprints201703.0152.v1
Chicago/Turabian Style
Lu, X., Qi Wang and Yang Wang. 2017 "Face Verification with Multi-Task and Multi-Scale Features Fusion" Preprints. https://doi.org/10.20944/preprints201703.0152.v1
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 to use identification signal 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 to use different landmarks of faces to solve the problems caused by poses. And the final face representation is formed by the concatenating feature of each deep CNN after PCA reduction. What's more, 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 LFW dataset.
Keywords
deep convolutional neural networks; identification; semi-verification; multi-scale features; face verification
Subject
Computer Science and Mathematics, Information Systems
Copyright:
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.
