Lighting variation is a major challenge for an automatic face recognition system. In order to overcome this problem, many methods have been proposed. Most of them try to extract features invariant to illumination changes or to reduce illumination changes in a pre-processing step and to extract features for recognition. In this paper, we present a procedure similar to the latter where the two steps are complementary. In the pre-processing step we deal with the illumination changes and in the features extraction step we use the BSIF (Binarized Statistical Image Features), a recently proposed textural algorithm. In our opinion, a method capable of reducing the lighting variations is ideal for an algorithm like the BSIF. The performance of our system has been tested on the FRGC dataset and the presented results show the validity of our approach.

On combining edge detection methods for improving BSIF based facial recognition performances

TUVERI, PIERLUIGI;GHIANI, LUCA;ABUKMEIL, MOHANAD A. M.;MARCIALIS, GIAN LUCA
2016

Abstract

Lighting variation is a major challenge for an automatic face recognition system. In order to overcome this problem, many methods have been proposed. Most of them try to extract features invariant to illumination changes or to reduce illumination changes in a pre-processing step and to extract features for recognition. In this paper, we present a procedure similar to the latter where the two steps are complementary. In the pre-processing step we deal with the illumination changes and in the features extraction step we use the BSIF (Binarized Statistical Image Features), a recently proposed textural algorithm. In our opinion, a method capable of reducing the lighting variations is ideal for an algorithm like the BSIF. The performance of our system has been tested on the FRGC dataset and the presented results show the validity of our approach.
9783319417776
Binarized statistical image features; Edge detection; Face recognition; Textural algorithm; Computer science (all); Theoretical computer science
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11584/180144
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