Call For Paper Volume:4 Issue:10 Oct'2017 |

PIFS: A New Tool for Face Recognition

Publication Date : 10/07/2015

Author(s) :

Mr. Rakesh S. Shirsath , Prof. L. B. Randive , Prof. B. S. Ahirwadkar.

Conference Name :
4th International Conference on Recent Trends in Engineering & Technology(ICRTET-2015) July 2-4,2015 Organized by SNJB's KBJ College of Engineering,Chandwad,Nashik,Maharashtra,India

Abstract :

Biometrics refers to identifying an individual based on his/her physiological or behavioral characteristics. Biometrics is inherently more reliable and accurate than the knowledge-based and tokenbased systems in differentiating an authenticated person from an imposter. This is based on the fact that physiological or behavioral characteristics are unique to a person. Also, the person to be identified has to be physically present at the time of verification or identification. Biometrics has the potential to replace all the needs of person verification in this electronically interconnected society in near future. Basically, biometric system is a pattern recognition system which recognizes a person using one or more specific characteristics. In this paper, we describe an approach to image coding based on a fractal theory of partitioned iterated contractive transformations. Local contractive affine transformations are iteratively applied to range-domain pairs in an arbitrary initial image results in a fixed point, closed to a given image and is referred as attractor. The main characteristics of this approach are that: i) it relies on the assumption that image redundancy can be efficiently captured and exploited through piecewise transformations on blocks of same image, and ii) it matches an original image approximately by a transformed image, obtained from a finite number of iterations of an image transformation called a PIFS code. PIFS code consists of the transformation parameters like range block number, index of approximately matched domain block, contrast scaling factor and luminance shift. PIFS code are used directly as feature of face. Features of an unknown face image are compared with those precomputed for images in a database.

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