TY - GEN
T1 - Multi-band gradient component pattern (MGCP)
T2 - 16th Scandinavian Conference on Image Analysis, SCIA 2009
AU - Guo, Yimo
AU - Chen, Jie
AU - Zhao, Guoying
AU - Pietikäinen, Matti
AU - Xu, Zhengguang
PY - 2009
Y1 - 2009
N2 - A feature extraction method using multi-frequency bands is proposed for face recognition, named as the Multi-band Gradient Component Pattern (MGCP). The MGCP captures discriminative information from Gabor filter responses in virtue of an orthogonal gradient component analysis method, which is especially designed to encode energy variations of Gabor magnitude. Different from some well-known Gabor-based feature extraction methods, MGCP extracts geometry features from Gabor magnitudes in the orthogonal gradient space in a novel way. It is shown that such features encapsulate more discriminative information. The proposed method is evaluated by performing face recognition experiments on the FERET and FRGC ver 2.0 databases and compared with several state-of-the-art approaches. Experimental results demonstrate that MGCP achieves the highest recognition rate among all the compared methods, including some well-known Gabor-based methods.
AB - A feature extraction method using multi-frequency bands is proposed for face recognition, named as the Multi-band Gradient Component Pattern (MGCP). The MGCP captures discriminative information from Gabor filter responses in virtue of an orthogonal gradient component analysis method, which is especially designed to encode energy variations of Gabor magnitude. Different from some well-known Gabor-based feature extraction methods, MGCP extracts geometry features from Gabor magnitudes in the orthogonal gradient space in a novel way. It is shown that such features encapsulate more discriminative information. The proposed method is evaluated by performing face recognition experiments on the FERET and FRGC ver 2.0 databases and compared with several state-of-the-art approaches. Experimental results demonstrate that MGCP achieves the highest recognition rate among all the compared methods, including some well-known Gabor-based methods.
UR - https://www.scopus.com/pages/publications/70350633358
U2 - 10.1007/978-3-642-02230-2_24
DO - 10.1007/978-3-642-02230-2_24
M3 - Conference contribution
AN - SCOPUS:70350633358
SN - 3642022294
SN - 9783642022296
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 229
EP - 238
BT - Image Analysis - 16th Scandinavian Conference, SCIA 2009, Proceedings
Y2 - 15 June 2009 through 18 June 2009
ER -