An End-to-End System for Unconstrained Face Verification with Deep Convolutional Neural Networks

Jun-Cheng Chen,Vishal M Patel,Rama Chellappa,Rajeev Ranjan,Amit Kumar,Ching-Hui Chen

doi:10.1109/iccvw.2015.55

An End-to-End System for Unconstrained Face Verification with Deep Convolutional Neural Networks

Jun-Cheng Chen, Vishal M Patel + Show 4 more

https://doi.org/10.1109/iccvw.2015.55

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Publication Date: Dec 1, 2015

Citations: 117

Affiliation: University of Maryland, College Park, Rutgers, The State University of New Jersey

#IARPA Janus Benchmark A #Labeled Faces In The Wild + Show 8 more

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Abstract

In this paper, we present an end-to-end system for the unconstrained face verification problem based on deep convolutional neural networks (DCNN). The end-to-end system consists of three modules for face detection, alignment and verification and is evaluated using the newly released IARPA Janus Benchmark A (IJB-A) dataset and its extended version Janus Challenging set 2 (JANUS CS2) dataset. The IJB-A and CS2 datasets include real-world unconstrained faces of 500 subjects with significant pose and illumination variations which are much harder than the Labeled Faces in the Wild (LFW) and Youtube Face (YTF) datasets. Results of experimental evaluations for the proposed system on the IJB-A dataset are provided.

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