Evaluation of machine learning techniques for face detection and recognition

Biometric identification (BI) is one of the most explored topics in recent years. One of the most important techniques for BI is face recognition. Face recognition systems (FRSs) are an important field in computer vision, because it represents a non-invasive BI technique. In this paper, a FRS is proposed. In the first step, a face detection algorithm is used for extracting faces from video frames (training videos) and generating a face database. In a second step, filtering and preprocessing are applied to face images obtained in the previous step. In a third step, a collection of machine learning algorithms are trained using as input data the faces obtained in the previous step. Finally, the classifiers are used for classify faces obtained from video frames (test videos). The obtained results shows the suitability of this approach for analyzing large collections of videos where previous face labels are not available.