Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
On the Euclidean Distance of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
An adaptive image Euclidean distance
Pattern Recognition
Multi-view gender classification using local binary patterns and support vector machines
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Gabor parameter selection for local feature detection
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Independent component analysis of Gabor features for face recognition
IEEE Transactions on Neural Networks
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Gabor wavelet-based methods have been widely used to extract representative features for face analysis. However, the existing methods usually suffer from high computational complexity of Gabor wavelet transform (GWT), and the Gabor parameters are fixed to a few conventional values which are assumed to be the best choice. In this paper we show that, for some facial analysis applications, the conventional GWT could be simplified by selecting the most discriminating Gabor orientations. In the selection process, we analyze the histogram of oriented gradient (HOG) of the average face image in a dataset, and eliminate the less significant orientation combinations. Then we traverse the rest combinations and select the best according to classification performance. We find that the selected orientations match the analysis of HOG well, and are therefore consistent with the intrinsic gradient characteristics of human face images. In order to assess the performance of the selected Gabor filters, we apply the proposed method to two tasks: face recognition and gender classification. The experimental results show that our method improves the accuracy of the classifiers and reduces the computation cost.