Jangala. Sasi Kiran

Work place: University of Mysore, Mysore & Associate Professor, MNR College of Engineering and Technology, Hyderabad, A.P, India.

E-mail: sasikiranjangala@gmail.com


Research Interests: Image Processing, Network Security, Information Security, Image Manipulation, Image Compression, Pattern Recognition


Sasi Kiran Graduated in B.Tech. from JNTU University in 2002. He received Masters Degree in M.Tech. from Bharath University, Chennai, in 2005 and pursuing Ph.D from University of Mysore, Mysore in Computer Science under the guidance of Dr V. Vijaya Kumar. He served as an Associate Professor in Vidya Vikas Institute of Technology, Hyderabad from 2004 to 2013 and working as Associate Professor in CSE Dept. in MNR College of Engg. & Tech, Sangareddy, Medak Dt, A.P, India. His research interests include Network Security, Digital Watermarking, and Pattern Recognition & Image Analysis. He has published research papers in various National, International conferences, proceedings and Journals. He is a life member of ISTE, ISC and management committee member of CSI. He has received significant contribution award from CSI India.

Author Articles
An Effective Age Classification Using Topological Features Based on Compressed and Reduced Grey Level Model of The Facial Skin

By V.Vijayakumar Jangala. Sasi Kiran V.V. Hari Chandana

DOI: https://doi.org/10.5815/ijigsp.2014.01.02, Pub. Date: 8 Nov. 2013

The present paper proposes an innovative technique that classifies human age group in to five categories i.e 0 to 12, 13 to 25, 26 to 45, 46 to 60, and above 60 based on the Topological Texture Features (TTF) of the facial skin.  Most of the existing age classification problems in the literature usually derive various facial features on entire image and with large range of gray level values in order to achieve efficient and precise classification and recognition. This leads to lot of complexity in evaluating feature parameters. To address this, the present paper derives TTF’s on Second Order image Compressed and Fuzzy Reduced Grey level (SICFRG) model, which reduces the image dimension from 5 x 5 into 2 x 2 and grey level range without any loss of significant feature information. The present paper assumes that bone structural changes do not occur after the person is fully grown that is the geometric relationships of primary features do not vary. That is the reason secondary features i.e TTF’s are identified and exploited. In the literature few researchers worked on TTF for classification of age, but so far no research is implemented on reduced dimensionality model.  The proposed Second order Image Compressed and Fuzzy Reduced Grey level (SICFRG) model reduces overall complexity in recognizing and finding histogram of the TTF on the facial skin.  The experimental evidence on FG-NET aging database and Google Images clearly indicates the high classification rate of the proposed method.

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