Gabor wavelets and AdaBoost in feature selection for face verification
MINNING ZHOU, Hongguang Wei, Stephen J. Maybank · 2006
Abstract. In this paper, we present a feature selection approach based on Gabor wavelets and AdaBoosting. The features are first extracted by a Gabor wavelet transform. A family of Gabor wavelets with 5 scales and 8 orientations is generated with the standard Gabor kernel. Convolved with the Gabor wavelets, the original images are transformed into vectors of Gabor wavelet features. Then for an individual person, a small set of significant features are selected by the AdaBoost algorithm from the pool of the Gabor wavelet features. In the feature selection process, each feature is the basis for a weak classifier which is trained with XM2VTS face images. In each round of AdaBoost learning, the feature with the lowest error of weak classifiers is selected. The results from the experiment have shown that the approach successfully selects meaningful and explainable features for face verification. The experiments suggest that the feature selection algorithm for face verification selects the features corresponding to the unique characteristics rather than common characteristics, and a large example size statistically benefits AdaBoost feature selection. 1