A Gabor-block-based kernel discriminative common vector approach using cosine kernels for human face recognition.
In this paper a nonlinear Gabor Wavelet Transform (GWT) discriminant feature extraction approach for enhanced face recognition is proposed. Firstly, the low-energized blocks from Gabor wavelet transformed images are extracted. Secondly, the nonlinear discriminating features are analyzed and extracted from the selected low-energized blocks by the generalized Kernel Discriminative Common Vector (KDCV) method. The KDCV method is extended to include cosine kernel function in the discriminating method. The KDCV with the cosine kernels is then applied on the extracted low-energized discriminating feature vectors to obtain the real component of a complex quantity for face recognition. In order to derive positive kernel discriminative vectors, we apply only those kernel discriminative eigenvectors that are associated with nonzero eigenvalues. The feasibility of the low-energized Gabor-block-based generalized KDCV method with cosine kernel function models has been successfully tested for classification using the L(1), L(2) distance measures; and the cosine similarity measure on both frontal and pose-angled face recognition. Experimental results on the FRAV2D and the FERET database demonstrate the effectiveness of this new approach.
Indian Statistical Institute, Kolkata 700108, India. kgparindamkar@gmail.com
Arindam
A
Kar
Debotosh
D
Bhattacharjee
Dipak Kumar
DK
Basu
Mita
M
Nasipuri
Mahantapas
M
Kundu
D000465:Algorithms; D019992:Databases as Topic / Q000706:statistics & numerical data; D004192:Discrimination, Psychological / Q000502:physiology*; D005145:Face; D006801:Humans; D008962:Models, Theoretical; D010364:Pattern Recognition, Visual / Q000502:physiology*; D010775:Photic Stimulation; D025341:Principal Component Analysis; D021641:Recognition, Psychology / Q000502:physiology*; D012680:Sensitivity and Specificity; D060388:Support Vector Machine