Local feature Hashing for face recognition

Zhihong Zeng; Tianhong Fang; Shishir Shah; Ioannis A. Kakadiaris

doi:10.1109/BTAS.2009.5339013

Local feature Hashing for face recognition

Zhihong Zeng, Tianhong Fang, Shishir Shah, Ioannis A. Kakadiaris

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

8 Scopus citations

Abstract

In this paper, we present Local Feature Hashing (LFH), a novel approach for face recognition. Focusing on the scalability of face recognition systems, we build our LFH algorithm on the p-stable distribution Locality-Sensitive Hashing (pLSH) scheme that projects a set of local features representing a query image to an ID histogram where the maximum bin is regarded as the recognized ID. Our extensive experiments on two publicly available databases demonstrate the advantages of our LFH method, including: i) significant computational improvement over naive search; ii) hashing in high-dimensional Euclidean space without embedding; and iii) robustness to pose, facial expression, illumination and partial occlusion.

Original language	English (US)
Title of host publication	IEEE 3rd International Conference on Biometrics
Subtitle of host publication	Theory, Applications and Systems, BTAS 2009
DOIs	https://doi.org/10.1109/BTAS.2009.5339013
State	Published - 2009
Event	IEEE 3rd International Conference on Biometrics: Theory, Applications and Systems, BTAS 2009 - Washington, DC, United States Duration: Sep 28 2009 → Sep 30 2009

Publication series

Name	IEEE 3rd International Conference on Biometrics: Theory, Applications and Systems, BTAS 2009

Conference

Conference	IEEE 3rd International Conference on Biometrics: Theory, Applications and Systems, BTAS 2009
Country/Territory	United States
City	Washington, DC
Period	9/28/09 → 9/30/09

ASJC Scopus subject areas

Biotechnology
Computational Theory and Mathematics
Computer Vision and Pattern Recognition

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10.1109/BTAS.2009.5339013

Cite this

Local feature Hashing for face recognition. / Zeng, Zhihong; Fang, Tianhong; Shah, Shishir et al.

IEEE 3rd International Conference on Biometrics: Theory, Applications and Systems, BTAS 2009. 2009. 5339013 (IEEE 3rd International Conference on Biometrics: Theory, Applications and Systems, BTAS 2009).

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

Zeng, Z, Fang, T, Shah, S & Kakadiaris, IA 2009, Local feature Hashing for face recognition. in IEEE 3rd International Conference on Biometrics: Theory, Applications and Systems, BTAS 2009., 5339013, IEEE 3rd International Conference on Biometrics: Theory, Applications and Systems, BTAS 2009, IEEE 3rd International Conference on Biometrics: Theory, Applications and Systems, BTAS 2009, Washington, DC, United States, 9/28/09. https://doi.org/10.1109/BTAS.2009.5339013

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abstract = "In this paper, we present Local Feature Hashing (LFH), a novel approach for face recognition. Focusing on the scalability of face recognition systems, we build our LFH algorithm on the p-stable distribution Locality-Sensitive Hashing (pLSH) scheme that projects a set of local features representing a query image to an ID histogram where the maximum bin is regarded as the recognized ID. Our extensive experiments on two publicly available databases demonstrate the advantages of our LFH method, including: i) significant computational improvement over naive search; ii) hashing in high-dimensional Euclidean space without embedding; and iii) robustness to pose, facial expression, illumination and partial occlusion.",

author = "Zhihong Zeng and Tianhong Fang and Shishir Shah and Kakadiaris, {Ioannis A.}",

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language = "English (US)",

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AU - Zeng, Zhihong

AU - Fang, Tianhong

AU - Shah, Shishir

AU - Kakadiaris, Ioannis A.

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N2 - In this paper, we present Local Feature Hashing (LFH), a novel approach for face recognition. Focusing on the scalability of face recognition systems, we build our LFH algorithm on the p-stable distribution Locality-Sensitive Hashing (pLSH) scheme that projects a set of local features representing a query image to an ID histogram where the maximum bin is regarded as the recognized ID. Our extensive experiments on two publicly available databases demonstrate the advantages of our LFH method, including: i) significant computational improvement over naive search; ii) hashing in high-dimensional Euclidean space without embedding; and iii) robustness to pose, facial expression, illumination and partial occlusion.

AB - In this paper, we present Local Feature Hashing (LFH), a novel approach for face recognition. Focusing on the scalability of face recognition systems, we build our LFH algorithm on the p-stable distribution Locality-Sensitive Hashing (pLSH) scheme that projects a set of local features representing a query image to an ID histogram where the maximum bin is regarded as the recognized ID. Our extensive experiments on two publicly available databases demonstrate the advantages of our LFH method, including: i) significant computational improvement over naive search; ii) hashing in high-dimensional Euclidean space without embedding; and iii) robustness to pose, facial expression, illumination and partial occlusion.

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SN - 9781424450206

T3 - IEEE 3rd International Conference on Biometrics: Theory, Applications and Systems, BTAS 2009

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T2 - IEEE 3rd International Conference on Biometrics: Theory, Applications and Systems, BTAS 2009

Y2 - 28 September 2009 through 30 September 2009

ER -

Local feature Hashing for face recognition

Abstract

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