ICTACT Journal on Image and Video Processing

Pullela S. V. V. S. R. Kumar ¹, P. Kiran Kumar Reddy ², V. Vijaya Kumar ³

Affiliations
1 Department of Computer Science and Engineering, Sri Aditya Engineering College, IN
2 Department of Computer Science and Engineering, Rajeev Gandhi Memorial College of Engineering and Technology, IN
3 Department of Computer Science and Engineering, Anurag Group of Institutions, IN

Abstract

The present paper extended the work carried out by Kumar et. al. [10] on Third order Neighbourhood LBP (TN-LBP) and derived an approach that estimates pattern trends on the outer cell of TN-LBP. The present paper observed and noted that the TN-LBP forms two types of V-patterns on the outer cell of TN-LBP i.e. Outer Right V Patterns (ORVP) and Outer Left V Patterns (OLVP). The ORLP and OLVP of TN-LBP consist of 5 pixels each. The present paper derived Grey Level Co-occurrence Matrix (GLCM) features based on LBP values of ORVP and OLVP. This GLCM is named as ORLVP-GLCM (Outer cell Right and Left V-Patterns of GLCM) and on this four features are evaluated to classify human into child (0 to 12 years), young (13 to 30 years), middle aged (31 to 50 years) and senior adult (above 60 years). The proposed method is experimented on FGNET, GOOGLE and Scanned facial images and the results are compared with the existing methods. The results demonstrate the efficiency of the proposed method over the existing methods.

Keywords

GLCM, LBP-Code, Outer Layer, Size of GLCM.

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P. Chandra Sekhar Reddy ¹, B. Eswara Reddy ², V. Vijaya Kumar ³

Affiliations
1 Deprtment of Computer Science and Engineering, Nalla Narasimha Reddy Education Society’s Group of Institutions, IN
2 Deprtment of Computer Science and Engineering, JNTUA College of Engineering, IN
3 Deprtment of Computer Science and Engineering, Anurag Group of Institutions, IN

Abstract

Today face recognition capability of the human visual system plays a significant role in day to day life due to numerous important applications for automatic face recognition. One of the problems with the recent image classification and recognition approaches are they have to extract features on the entire image and on the large grey level range of the image. The present paper overcomes this by deriving an approach that reduces the dimensionality of the image using Shape primitives and reducing the grey level range by using a fuzzy logic while preserving the significant attributes of the texture. The present paper proposed an Image Dimensionality Reduction using shape Primitives (IDRSP) model for efficient face recognition. Fuzzy logic is applied on IDRSP facial model to reduce the grey level range from 0 to 4. This makes the proposed fuzzy based IDRSP (FIDRSP) model suitable to Grey level co-occurrence matrices. The proposed FIDRSP model with GLCM features are compared with existing face recognition algorithm. The results indicate the efficacy of the proposed method.

Keywords

GLCM Features, Preprocessing, Grey Level Range, Significant Image Features, Dimensionality Reduction.

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