Face Recognition Using DRLBP and SIFT Feature Extraction

The detection of human face from images plays a vital role in Computer vision, cognitive science and Forensic Science. The various computational and mathematical models, for classifying face including Scale Invariant Feature Transform (SIFT) and Dominant Rotated Local Binary Pattern (DRLBP) have been proposed to yield better performance. This paper proposes a novel method of classifying the human face using Artificial Neural Network. This is done by pre-processing the face image at first and then extracting the face features using SIFT. Then the detection of human faces is done using Back Propagation Network (BPN). The process of combining SIFT and DRLBP perform better accuracy rather using separately.