ISSN 22773061 1330 | Page Aug 10, 2013 Performance Improvement of Face Recognition System using Selective Local Feature Vectors Vasudha S, Research Scholar and Member, IEEE Neelamma K. Patil, Lecturer and Member, IEEE Dr. Lokesh R. Boregowda, Fellow and CD Leader GC, Senior Member, IEEE Abstract 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 of prime concern. Although the existing automated machine recognition systems have certain level of maturity but their accomplishments are limited due to real time challenges. 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. Proposed method uses a non-statistical procedure which avoids training step for face samples thereby avoiding generalizability problem which is caused due to statistical learning procedure. 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 of feature vectors for actual recognition. Index Terms— Discrete Cosine Transforms, False Acceptance Ratio, False Rejection Ratio, Gabor filter, Image splicing, Local Binary Pattern, Performance modeling. Council for Innovative Research Peer Review Research Publishing System Journal: INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY Vol 10, No 2 editor@cirworld.com www.cirworld.com , member.cirworld.com