Authors:
I. Gede Pasek Suta Wijaya
1
;
Keiichi Uchimura
2
;
Gou Koutaki
2
and
Cuicui Zhang
3
Affiliations:
1
Kumamoto University and Mataram University, Japan
;
2
Kumamoto University, Japan
;
3
Kyoto University, Japan
Keyword(s):
Frequency analysis, Lighting normalization, Incremental LDA, Holistic features, Face recognition.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Image Understanding
;
Object Recognition
;
Pattern Recognition
;
Software Engineering
Abstract:
This paper presents an integration of Wavelet and Discrete Cosine Transform (DCT) based lighting normalization, and shifting-mean Linear Discriminant Analysis (LDA) based face classifiers for face recognition. The aims are to provide robust recognition rate against large face variability due to lighting variations and to avoid retraining problem of the classical LDA for incremental data. In addition, the compact holistic features is employed for dimensional reduction of the raw face image. From the experimental results, the proposed method gives sufficient and robust achievement in terms of recognition rate and requires short computational time.