Support vector machine based multi-view face detection and recognition

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Support vector machine based multi-view face detection and recognition (2004)

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by Yongmin Li , Shaogang Gong , Jamie Sherrah , Heather Liddell

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@MISC{Li04supportvector,
    author = {Yongmin Li and Shaogang Gong and Jamie Sherrah and Heather Liddell},
    title = {Support vector machine based multi-view face detection and recognition },
    year = {2004}
}

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Abstract

Detecting faces across multiple views is more challenging than in a fixed view, e.g. frontal view, owing to the significant non-linear variation caused by rotation in depth, self-occlusion and self-shadowing. To address this problem, a novel approach is presented in this paper. The view sphere is separated into several small segments. On each segment, a face detector is constructed. We explicitly estimate the pose of an image regardless of whether or not it is a face. A pose estimator is constructed using Support Vector Regression. The pose information is used to choose the appropriate face detector to determine if it is a face. With this pose-estimation based method, considerable computational efficiency is achieved. Meanwhile, the detection accuracy is also improved since each detector is constructed on a small range of views. We developed a novel algorithm for face detection by combining the Eigenface and SVM methods which performs almost as fast as the Eigenface method but with a significant improved speed. Detailed experimental results are presented in this paper including tuning the parameters of the pose estimators and face detectors, performance evaluation, and applications to video based face detection and frontal-view face recognition.

Keyphrases

multi-view face detection support vector machine face detector pose estimator face detection image regardless fixed view appropriate face detector novel approach pose information support vector regression frontal-view face recognition multiple view small range svm method several small segment frontal view performance evaluation significant non-linear variation novel algorithm eigenface method experimental result considerable computational efficiency detection accuracy

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