Detecting faces from color video by using paired wavelet features

Szu-Hao Huang, Shang Hong Lai

Research output: Contribution to journalConference articlepeer-review

16 Scopus citations


Detecting human face regions in color video is normally required for further processing in many practical applications. In this paper, we propose a learning-based algorithm that determines the most discriminative pairs of Haar wavelet coefficients of color images for face detection. To select the most discriminative features from the vast amount (1,492,128) of possible pairs of three-channel color wavelet coefficients, we employ two procedures to accomplish this task. At first, we choose a subset of effective candidate pairs of wavelet coefficients based on the Kullback Leibler (KL) distance between the conditional joint distributions of the face and non-face training data. Then, the adaboost algorithm is employed to incrementally select a set of complementary pairs of wavelet coefficients and determine the best combination of weak classifiers that are based on the joint conditional probabilities of these selected coefficient pairs for face detection. By applying Kalman filter to predict and update the face region in a video, we extending the face detection from a single image to a video sequence. In contrast to the previous face detection works, the proposed algorithm is based on finding the discriminative features of joint wavelet coefficients computed from all three channels of color images in an integrated learning framework. We experimentally show that the proposed algorithm can achieve high accuracy and fast speed for detecting faces from color video.

Original languageEnglish
Article number1384857
JournalIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Issue numberJanuary
StatePublished - 2004
Event2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2004 - Washington, United States
Duration: 27 Jun 20042 Jul 2004


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