Wijaya, I., Uchimura, K., Hu, Z. (2008). Modified LDA classifier in multi resolution wavelet domain for multi-pose face recognition. test, 6(6th International Conference on Electrical Engineering ICEENG 2008), 1-12. doi: 10.21608/iceeng.2008.34359
I Gede Pasek Suta Wijaya; Keiichi Uchimura; Zhencheng Hu. "Modified LDA classifier in multi resolution wavelet domain for multi-pose face recognition". test, 6, 6th International Conference on Electrical Engineering ICEENG 2008, 2008, 1-12. doi: 10.21608/iceeng.2008.34359
Wijaya, I., Uchimura, K., Hu, Z. (2008). 'Modified LDA classifier in multi resolution wavelet domain for multi-pose face recognition', test, 6(6th International Conference on Electrical Engineering ICEENG 2008), pp. 1-12. doi: 10.21608/iceeng.2008.34359
Wijaya, I., Uchimura, K., Hu, Z. Modified LDA classifier in multi resolution wavelet domain for multi-pose face recognition. test, 2008; 6(6th International Conference on Electrical Engineering ICEENG 2008): 1-12. doi: 10.21608/iceeng.2008.34359

Modified LDA classifier in multi resolution wavelet domain for multi-pose face recognition

The International Conference on Electrical Engineering
Article 99, Volume 6, 6th International Conference on Electrical Engineering ICEENG 2008, May 2008, Pages 1-12    PDF (306.38 K)
Document Type: Original Article
DOI: 10.21608/iceeng.2008.34359
Authors
I Gede Pasek Suta Wijaya1; Keiichi Uchimura2; Zhencheng Hu2
1Electrical Engineering Dept., Engineering Faculty, Mataram University Indonesia.
2Computer Science and Electrical Engineering Graduate School of Science and Technology, Kumamoto University Kumamoto-Shi, Japan.
Abstract
Abstract:
This paper presents multipose faces recognition. The proposed scheme is based on
holistic information of face image and small modification of classical LDA (modified
LDA) classifier. The holistic information called as facial features is obtained by multiresolution
wavelet analysis. The modified LDA (MLDA) classifier that works based on
multivariate analysis classifies the facial features to a person’s class. The objectives of
the proposed method are to create a compact and meaningful facial features without
removing significant face image information, to build a simple classification technique
which can well classify face images to a person’s class, to make the M-LDA-based
training system to solve the retraining problem of the PCA and LDA based recognition
system, to reduce the high memory space requirement of classical LDA and PCA, and
to compare the effectiveness of proposed method to established LDA based recognition
systems such as RLDA, DLDA, and SLDA. The result shows that the proposed method
gives good enough performance i.e. high enough success rate, short time processing,
and small enough EER compare to establish LDA. In addition, the wavelet transforms is
an efficient way for reducing the dimensional size of original image.
Keywords
facial features; matching; wavelet; LDA; eigenface
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