Abstract
We introduce in this paper a novel Independent Gabor wavelet Features (IGF) method for face recognition. The IGF method derives first an augmented Gabor feature vector based upon the Gabor wavelet transformation of face images and using different orientation and scale local features. Independent Component Analysis (ICA) operates then on the Gabor feature vector subject to sensitivity analysis for the ICA transformation. Finally, the IGF method applies the Probabilistic Reasoning Model for classification by exploiting the independence properties between the feature components derived by the ICA. The feasibility of the new IGF method has been successfully tested on face recognition using 600 FERET frontal face images corresponding to 200 subjects whose facial expressions and lighting conditions may vary.
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Liu, C., Wechsler, H. (2001). Face Recognition Using Independent GaborWavelet Features. In: Bigun, J., Smeraldi, F. (eds) Audio- and Video-Based Biometric Person Authentication. AVBPA 2001. Lecture Notes in Computer Science, vol 2091. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45344-X_3
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DOI: https://doi.org/10.1007/3-540-45344-X_3
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