Hierarchical combination of face/non-face classifiers based on Gabor wavelet and support vector machines
Young Hoon Sung, Tae‐Kyun Kim, Seok-Cheol Kee · 2002
In this paper, we propose a real-time face and eye detection algorithm for video surveillance and human computer interface. Different types of face/non-face classifiers are hierarchically combined for reliability and real-time performance. Each classifier is connected by consisdering the accuracy and translation/scale sensitivity of the previous classifier. First, face candidates are extracted using similarity matching of Gabor filter responses in M-style grid. Then, a hierarchy of SVM classifiers trained on PCA subspaces is applied to the candidates. Two steps of SVMs are trained on different number of PCA features and resolution images. The combination of classifiers based on different types of features, frequency and intensity, significantly reduces the false positives due to complementary characteristics of their domains in classification. Coarse-to-fine search by three steps of detection also reduces run-time complexity. Our system can speed up conventional SVM classifier by a factor of 40 resulting in comparable face detection rate (FDR). In addition, the center positions of both eyes are efficiently detected by iterative binary thresholding method with contour tracing in the accurately localized face region. Experimental results on the test set taken under office illumination show the accuracy with FDR of 98%, 0.5 % false positives, and eye detection rate (EDR) of 99%. Our current system detects a face in runtime of 250ms. 1