P.Chandra Sekhar Reddy

Work place: CSE Dept, GIET COLLEGE OF ENGINEERING, RAJAHMUNDRY, A.P, INDIA.

E-mail: pchandureddy@yahoo.com

Website:

Research Interests: Image Processing, Pattern Recognition

Biography

Mr. P.Chandra Sekhar Reddy completed his B.Tech in Computer Science & Engineering from Sri Krishna Devaraya University in 1999.He Received the Masters Degree in M.Tech (Computer Science) from JNT University, Hyderabad, in 2004. He is pursuing his Ph.D in JNTUA University, Anantapur, India. He is currently working as Associate Professor in CSE Dept, GIET College of Engineering, Rajahmundry, India. He has having more than 11 years of Teaching Experience.His research areas are Image Processing and Pattern Recognition. He is a member of Professional bodies like IEEE,IAENG and SRRF.

Author Articles
Texton Based Shape Features on Local Binary Pattern for Age Classification

By B. Eswara Reddy P.Chandra Sekhar Reddy V.Vijayakumar

DOI: https://doi.org/10.5815/ijigsp.2012.07.06, Pub. Date: 28 Jul. 2012

Classification and recognition of objects is interest of many researchers. Shape is a significant feature of objects and it plays a crucial role in image classification and recognition. The present paper assumes that the features that drastically affect the adulthood classification system are the Shape features (SF) of face. Based on this, the present paper proposes a new technique of adulthood classification by extracting feature parameters of face on Integrated Texton based LBP (IT-LBP) images. The present paper evaluates LBP features on facial images. On LBP Texton Images complex shape features are evaluated on facial images for a precise age classification.LBP is a local texture operator with low computational complexity and low sensitivity to changes in illumination. Textons are considered as texture shape primitives which are located with certain placement rules. The proposed shape features represent emergent patterns showing a common property all over the image. The experimental evidence on FGnet aging database clearly indicates the significance and accuracy of the proposed classification method over the other existing methods.

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