Face recognition using support vector machines with the robust feature
Guang Hua Dai, Changle Zhou · 2004
Face recognition problem is challenging because face images can vary considerably in terms of facial expressions, lighting conditions and so on. This paper introduces a novel face recognition using support vector machines with the robust feature extracted by kernel principal component analysis (KPCA), which is robust to facial variations. This method derives firstly an augmented Gabor-face vector based on the Gabor wavelet transformation of face images using different orientation and scale local feature, which is robust to changes in facial expression and pose. KPCA is used to extract the feature of the augmented Gabor-face vector so that the principal components is computed within the space spanned by high-order correlation of input of augmented Gabor-face vector and produce a good performance. Finally, the support vector machine (SVM), which has high generalization capabilities and high performance in tackling small sample size in the pattern recognition task, is used to classify the feature. The comparative experiments in the ORL face database show that this algorithm is more effective than the previous methods.