Rule based features selection for the performance improvement of face recognition system

Face recognition is one of the important applications of image processing and it has gained significant attention in wide range of law enforcement areas in which security is the prime concern. Face image is most popular non-intrusive and non-invasive biometrics whose image can easily be taken without user co-operation. Although the existing automated machine recognition systems have certain level of maturity but their accomplishments are limited due to real time challenges and face recognition systems are impressively sensitive to appearance variations due to lighting, expression and aging. The major metric in modeling the performance of a face recognition system is its accuracy of recognition. This paper proposes a novel method which improves the recognition accuracy as well as avoids face datasets being tampered through image splicing techniques. It also avoids generalizability problem which is caused due to subspace discriminant analysis or statistical learning procedure by using a non-statistical procedure which avoids training step for face samples. This proposed method performs well with images with partial occlusion and images with lighting variations as the local patch of the face is divided into several different patches. The performance improvement is shown considerably high in terms of recognition rate and storage space by storing train images in compressed domain and selecting significant features from superset if feature vectors for actual recognition.