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.
Highlights
Face recognition is one of the most representative applications of image analysis
In the course of studying papers which are referenced [2] through [7] in existing state-of-art literature, the concepts such as face image variations caused by different poses, inter-person differences used in distinguishing identities, related potentials in many applications dealing with uncooperative subjects, full power of face recognition as a passive biometric technique, problems due to varying illumination, generalizability problems, problems related to image splicing, performance limitations caused due to vast datasets have been understood
This paper shows that, if the same importance is given to image preprocessing steps such as cropping and illumination normalization, a better performance can be achieved for any given face recognition system
Summary
Face recognition is one of the most representative applications of image analysis. It has received significant attention in both security applications and research fields. The performance of face recognition system is limited due to various challenges such as modelling and normalization of illumination, pose variance / pose estimation, accurate landmark detection, feature extraction and model building, matching methodology / determination of distance measures, vast database to be handled (in most applications), subspace learning and so on.
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