Contents I. Introduction
Biometric systems are increasingly developing in recent years due to their widespread application in video surveillance, human-machine interaction and virtual reality. As one of the most famous biometric systems, face recognition has attracted the attention of both industrial and academic communities in the past decades. The main elements of a face recognition system are the feature extraction [1] [3] and the classification method [2] [4]. However, the feature extractor is of great importance to the extent that if this feature is not efficient even the best classifier will not provide good recognition rate. In this topic, various approaches has been proposed to deal with this task; we cite among them LBP [5], HOG [6] [7], Gabor [8], EBGM [9]. HOG is a descriptor based on gradient measurement which has been used in different areas as the pedestrian detection [11] and the hand gesture recognition [10] and recently some researchers adopted it to face recognition task. In this paper we propose to improve the HOG descriptor by incorporating the fuzzy concept [12]. In our case introducing fuzzy concept conserves the same recognition rate as the original HOG with a lower feature dimension. Besides, more we increase the feature length more it outperforms the original HOG.
