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

被引：17

作者：

Chen, Jun-Cheng ^{[1
]}

Ranjan, Rajeev ^{[1
]}

Kumar, Amit ^{[1
]}

Chen, Ching-Hui ^{[1
]}

Patel, Vishal M. ^{[2
]}

Chellappa, Rama ^{[1
]}

机构：

[1] Univ Maryland, College Pk, MD 20742 USA

[2] Rutgers State Univ, New Brunswick, NJ 08903 USA

来源：

2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOP (ICCVW) | 2015年

关键词：

D O I：

10.1109/ICCVW.2015.55

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：360 / 368

页数：9

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