Face recognition across illumination

Saleh Aly; Alaa Sagheer; Naoyuki Tsuruta; Rin-Ichiro Taniguchi

doi:10.1007/s10015-007-0437-9

Face recognition across illumination

Saleh Aly, Alaa Sagheer, Naoyuki Tsuruta, Rin-Ichiro Taniguchi

Real world robotics

Research output: Contribution to journal › Article › peer-review

9 Citations (Scopus)

Abstract

Illumination variation on images of faces is one of the most difficult problems in face recognition systems. The performance of a self-organizing map-based face recognition system is highly degraded when the illumination in test images differs from that of the training images. Illumination normalization is a way to solve this problem. Both global and local image enhancement methods are studied in this article. A local histogram equalization method strongly improves the recognition accuracy of the CMU-PIE face database.

Original language	English
Pages (from-to)	33-37
Number of pages	5
Journal	Artificial Life and Robotics
Volume	12
Issue number	1-2
DOIs	https://doi.org/10.1007/s10015-007-0437-9
Publication status	Published - Dec 1 2008

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Biochemistry, Genetics and Molecular Biology(all)

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10.1007/s10015-007-0437-9

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title = "Face recognition across illumination",

abstract = "Illumination variation on images of faces is one of the most difficult problems in face recognition systems. The performance of a self-organizing map-based face recognition system is highly degraded when the illumination in test images differs from that of the training images. Illumination normalization is a way to solve this problem. Both global and local image enhancement methods are studied in this article. A local histogram equalization method strongly improves the recognition accuracy of the CMU-PIE face database.",

author = "Saleh Aly and Alaa Sagheer and Naoyuki Tsuruta and Rin-Ichiro Taniguchi",

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AU - Aly, Saleh

AU - Sagheer, Alaa

AU - Tsuruta, Naoyuki

AU - Taniguchi, Rin-Ichiro

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AB - Illumination variation on images of faces is one of the most difficult problems in face recognition systems. The performance of a self-organizing map-based face recognition system is highly degraded when the illumination in test images differs from that of the training images. Illumination normalization is a way to solve this problem. Both global and local image enhancement methods are studied in this article. A local histogram equalization method strongly improves the recognition accuracy of the CMU-PIE face database.

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Face recognition across illumination

Abstract

All Science Journal Classification (ASJC) codes

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