Frequency decomposition techniques for increased discriminative 3D facial information capture
Data(s) |
17/05/2010
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Resumo |
Eigen-based techniques and other monolithic approaches to face recognition have long been a cornerstone in the face recognition community due to the high dimensionality of face images. Eigen-face techniques provide minimal reconstruction error and limit high-frequency content while linear discriminant-based techniques (fisher-faces) allow the construction of subspaces which preserve discriminatory information. This paper presents a frequency decomposition approach for improved face recognition performance utilising three well-known techniques: Wavelets; Gabor / Log-Gabor; and the Discrete Cosine Transform. Experimentation illustrates that frequency domain partitioning prior to dimensionality reduction increases the information available for classification and greatly increases face recognition performance for both eigen-face and fisher-face approaches. |
Formato |
application/pdf |
Identificador | |
Relação |
http://eprints.qut.edu.au/40951/1/3DPVT2010.pdf http://www.3dpvt2010.org/ Fookes, Clinton B., Cook, Jamie A., Sridharan, Sridha, & Tistarelli, Massimo (2010) Frequency decomposition techniques for increased discriminative 3D facial information capture. In Proceedings of the International Symposium on 3D Data Processing, Visualization and Transmission, Espace Saint Martin, Paris. |
Direitos |
Copyright 2010 [please consult the authors] |
Fonte |
Faculty of Built Environment and Engineering; Information Security Institute; School of Engineering Systems |
Palavras-Chave | #080104 Computer Vision #090609 Signal Processing #3D Face Recognition #Frequency Decomposition #Gabor #Wavelet #Discrete Cosine Transform |
Tipo |
Conference Paper |