A combined HOG-LPQ with Fuz-SVM classifier for Object face Liveness Detection

In day to day, the Object Liveness Detection for Genuine Face recognition becoming a challenging task in security systems which is important in many real time applications such as User Authentication, Object Identification, Object Recognition, Object live detection, genuine face identification and many more. From many decades, the existing systems and proposed systems for Genuine Object Face recognition and Anti-Spoofing Classifier is accomplished to detect liveness of the object with the help of object features and finds spoofing attacks on all subjects. However, by considering the various individual differences between several objects, the ordinary classifier cannot simplifies well to all objects. In order to overcome this problem, we proposed a Combined HOG-LPQ with Fuz-SVM classifier for Object Liveness Detection, allows to select specific object based on Region of Interest (ROI) and extract features of ROI, so that it will reduce the complexity in feature extraction and then recognises face, later on it check for spoofing attacks using Fuzzy logic based SVM classifier which is specifically trained for each object, and also avoids the interferences between several objects. In addition of all possible rare and uncommon fake samples, the proposed system also includes blur objects. The proposed Combined HOG-LPQ with Fuz-SVM classifier for Object Liveness Detection makes it practical to train well performed individual Object to its certain face with liveness detection. We performed experiments on various real time objects with exiting data base; the details are discussed in the prospect of the proposed approach.