基于模板匹配与支持矢量机的人脸检测

人脸检测是人脸识别与基于内容的图像及视频检索的一项重要任务。由于非人脸样本相对于人脸样本的多样性和复杂性，使得人脸模式分类器的训练十分困难。该文提出了一种将模板匹配与支持矢量机（SVM）相结合的人脸检测算法。算法首先使用双眼－人脸模板对进行粗筛选，然后使用SVM分类器进行分类。在模板匹配限定的子空间内采用“自举”方法收集“非人脸”样本训练SVM，有效地降低了训练的难度，实验结果的对比数据表明，该算法是十分有效的。

Face Detection Based on Template Matching and Support Vector Machines

Face detection is an important task in face recognition and content based image and video retrieval. The difficulty in training a face pattern classifier as face detector is due to the diversity and complexity of non face patterns compared with face patterns. In this paper, we propose a subspace method for downsizing the training space via template matching filtering. Two types of templates, eyes in whole and face itself from an average face of a set of mugshot photos, are used in template matching for coarse filtration. Only when both eyes in whole template matching and face template matching are over corresponding thresholds, a candidate window is regarded as in the subspace. In this template matching constrained subspace, a bootstrap method is used to collect non face samples for SVM training, which greatly reduces the complexity of training SVM. The face detector SVM is trained by John Platt's Sequential Minimal Optimization (SMO) algorithm. During the detection procedure, an image and its scaled images are scanned, each candidate window will first be evaluated by both eyes in whole template matching and face template matching, and when both are over corresponding thresholds that candidate will be passed to the SVM classifier for the final decision. The detection results over all scales are then merged into final face detection output by way of fusion that keeps only the maximum one when overlap happens. In this way the training becomes much easier and the speed is improved to be used in practical applications. Experimental results with very promising performance compared with some well known existing detectors on both test sets of our own and the CMU's test set demonstrate its effectiveness.