Face recognition based on independent Gabor features and support vector machine
Wanle Chi, Guang Hua Dai, Lin Zhang · 2004
On the one hand, the support vector machine (SVM) has the high performance in tackling small sample size and high-dimensional data, and has the good generalization ability too. On the other hand, Gabor wavelet exhibits strong characteristics of spatial locality, scale, and orientation selectivity, and the Gabor representations of face images can produce salient local features that are most suitable for face recognition. This paper proposes a new face recognition method based on independent Gabor features (IGF) and SVM. The proposed method has four steps as follows: 1) an augmented Gabor feature vector (AGFV) is derived from a set of downsampled Gabor wavelet representations of face images; 2) an IGF is obtained by applying the independent component analysis (ICA) to the AGFV; 3) To decrease the computational complexity and improve the recognition rate, Genetic Algorithms (GA) is used to select the optimal IGF set for classification; 4) the SVM is used to classify the optimal IGF. The experiments tested on the Yale database show that this method is very effective.