Effective Face Detection Using a Small Quantity of Training Data
We present an effective and real-time face detection method based on Principal Component Analysis (PCA) and Support Vector Machines (SVMs). We extract simple Haar-like features from training images that consist of face and non-face images, reinterpret the features with PCA, and select useful ones from the large number of extracted features. With the selected features, we construct a face detector using an SVM appropriate for binary classification. The face detector is not affected by the size of a training dataset in a significant way, so that it works well with a small quantity of training data. It also shows a sufficiently fast detection speed for it to be practical for real-time face detection.
KeywordsSupport Vector Machine Face Image Training Image Support Vector Machine Classifier Face Detection
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