An SVM-AdaBoost-based face detection system

Face detection is the first significant step in face recognition and many computer vision applications. The goal of this work was to improve detection accuracy as well as reducing the execution time. Images are pre-processed, scaled and normalised with the discrete cosine transform. Gabor feature extraction techniques were employed to extract thousands of facial vectors. An AdaBoost-based feature selection tool was formulated to select a few hundreds of the Gabor wavelets. These vectors representing significant salient local features are used as input vectors to a support vector machine classifier. The classifier is trained and becomes capable of detecting faces. A detection rate of 97.6% with acceptable false positives was registered with a test set of 507 faces. The execution time of a pixel of size 320x240 is 0.0285s, which is very promising. A comparative evaluation of receiver operating characteristic (ROC) curves of different detectors on FDDB set shows that the proposed method is very effective.