Unconstrained face verification using deep CNN features

In this paper, we present an algorithm for unconstrained face verification based on deep convolutional features and evaluate it on the newly released IARPA Janus Benchmark A (IJB-A) dataset as well as on the traditional Labeled Face in the Wild (LFW) dataset. The IJB-A dataset includes real-world un...

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Bibliographic Details
Published in2016 IEEE Winter Conference on Applications of Computer Vision (WACV) pp. 1 - 9
Main Authors Jun-Cheng Chen, Patel, Vishal M., Chellappa, Rama
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2016
Subjects
Bayes methods
Convolution
Face
Feature extraction
Lighting
Measurement
Training
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Summary:In this paper, we present an algorithm for unconstrained face verification based on deep convolutional features and evaluate it on the newly released IARPA Janus Benchmark A (IJB-A) dataset as well as on the traditional Labeled Face in the Wild (LFW) dataset. The IJB-A dataset includes real-world unconstrained faces from 500 subjects with full pose and illumination variations which are much harder than the LFW and Youtube Face (YTF) datasets. The deep convolutional neural network (DCNN) is trained using the CASIA-WebFace dataset. Results of experimental evaluations on the IJB-A and the LFW datasets are provided.
DOI:10.1109/WACV.2016.7477557