Facial image retrieval based on local and regional features

Invention of the digital camera and also cell phones with powerful cameras with moderate and low pricing system has given the common man the privilege to capture his world in pictures anywhere, at any time, and conveniently share them with others. This has resulted the generation of volumes of images. These factors have created numerous possibilities and finally created interest among the researchers towards the design of an efficient and accurate Content Based Information Retrieval (CBIR) system. That's why new technological advances and growth in CBIR has been unquestionably rapid during the last five years. Various face recognition methods are derived using local features, and among them the Local Binary Pattern (LBP) approach is very famous. The basic disadvantage of these methods is they completely fail in representing features derived from large or macro structures or regions, which are very much essential for faces. To address this present paper proposes a median based multi region LBP. The proposed median based multi region LBP, initially divides the facial image in to non-overlapped regions of size 5 × 5. LBP values are evaluated by dividing the region in to sub regions of size 3 × 3. The 9 sub-region LBP values are arranged in the sorted manner and the median LBP code is considered as the feature vector for the region. The present paper also proposes the minimum and maximum based regional LBP methods for efficient image retrieval. To overcome the noise and illumination effect the proposed method initially applied DOG preprocessing method with gamma correction. The proposed method is applied on FG-NET and Goggle databases for efficient facial image retrieval. The experimental results indicate the efficiency of the proposed method.

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