Gabor-Boosting Face Recognition
From Machine Learning Perspective
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In the past decade, face recognition has received much attention by both the commercial and public sectors in biometrics. This book describes a highly accurate appearance-based approach for face recognition - Gabor-Boosting.The strong performance of the Gabor-Boosting face recognition is highlighted by combining three key leading edge techniques - Gabor wavelet transform, AdaBoost, Support Vector Machine (SVM). The Gabor wavelet transform is used to extract features which describe texture variations of human faces. The AdaBoost algorithm is used to select most significant features which repres...
In the past decade, face recognition has received much attention by both the commercial and public sectors in biometrics. This book describes a highly accurate appearance-based approach for face recognition - Gabor-Boosting.The strong performance of the Gabor-Boosting face recognition is highlighted by combining three key leading edge techniques - Gabor wavelet transform, AdaBoost, Support Vector Machine (SVM). The Gabor wavelet transform is used to extract features which describe texture variations of human faces. The AdaBoost algorithm is used to select most significant features which represent different individuals. The SVM constructs a classifier with high recognition accuracy. The Gabor-Boosting face recognition is extended into multi-class classification domain. The results show that the performance is improved by applying loosely controlled face recognition in the multi-class classification. The Gabor-Boosting face recognition is robust under conditions of small number of examples and selection-bias. It gives no false detections for impostors and high acceptance rate for clients in face verification.
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