Face verification using Gabor wavelets and AdaBoostZhou, M. and Wei, H. (2006) Face verification using Gabor wavelets and AdaBoost. In: Tang, Y. Y., Wang, S. P., Lorette, G., Yeung, D. S. and Yan, H. (eds.) 18th International Conference on Pattern Recognition, Vol 1, Proceedings. International Conference on Pattern Recognition. IEEE Computer Soc, Los Alamitos, pp. 404-407. ISBN 1051-4651 0769525210 Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. Abstract/SummaryThis paper presents a new face verification algorithm based on Gabor wavelets and AdaBoost. In the algorithm, faces are represented by Gabor wavelet features generated by Gabor wavelet transform. Gabor wavelets with 5 scales and 8 orientations are chosen to form a family of Gabor wavelets. By convolving face images with these 40 Gabor wavelets, the original images are transformed into magnitude response images of Gabor wavelet features. The AdaBoost algorithm selects a small set of significant features from the pool of the Gabor wavelet features. Each feature is the basis for a weak classifier which is trained with face images taken from the XM2VTS database. The feature with the lowest classification error is selected in each iteration of the AdaBoost operation. We also address issues regarding computational costs in feature selection with AdaBoost. A support vector machine (SVM) is trained with examples of 20 features, and the results have shown a low false positive rate and a low classification error rate in face verification.
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