Unconstrained face verification using deep CNN features

Jun Cheng Chen; Vishal M. Patel; Rama Chellappa

doi:10.1109/WACV.2016.7477557

Unconstrained face verification using deep CNN features

Jun Cheng Chen, Vishal M. Patel, Rama Chellappa

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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.

Original language	English (US)
Title of host publication	2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509006410
DOIs	https://doi.org/10.1109/WACV.2016.7477557
State	Published - May 23 2016
Externally published	Yes
Event	IEEE Winter Conference on Applications of Computer Vision, WACV 2016 - Lake Placid, United States Duration: Mar 7 2016 → Mar 10 2016

Publication series

Name	2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016

Conference

Conference	IEEE Winter Conference on Applications of Computer Vision, WACV 2016
Country/Territory	United States
City	Lake Placid
Period	3/7/16 → 3/10/16

ASJC Scopus subject areas

Computer Science Applications
Computer Vision and Pattern Recognition

Access to Document

10.1109/WACV.2016.7477557

Cite this

Chen, J. C., Patel, V. M., & Chellappa, R. (2016). Unconstrained face verification using deep CNN features. In 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016 Article 7477557 (2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WACV.2016.7477557

Unconstrained face verification using deep CNN features. / Chen, Jun Cheng; Patel, Vishal M.; Chellappa, Rama.
2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 7477557 (2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chen, JC, Patel, VM & Chellappa, R 2016, Unconstrained face verification using deep CNN features. in 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016., 7477557, 2016 IEEE Winter Conference on Applications of Computer Vision, WACV 2016, Institute of Electrical and Electronics Engineers Inc., IEEE Winter Conference on Applications of Computer Vision, WACV 2016, Lake Placid, United States, 3/7/16. https://doi.org/10.1109/WACV.2016.7477557

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abstract = "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.",

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year = "2016",

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doi = "10.1109/WACV.2016.7477557",

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AB - 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.

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Unconstrained face verification using deep CNN features

Abstract

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ASJC Scopus subject areas

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