Kadhim M.Hashem

Work place: Department of computer science University of Thi-Qar, Thi-Qar, Iraq

E-mail: Kadhimmehdi63@gmail.com


Research Interests: Image Processing, Pattern Recognition, Artificial Intelligence


Kadhim M.Hashem received his BS degree in computer science from Al- Basra University, lraq, in 1985, received his MS degree in computer Science from Al- Technology University, lraq, in 1991, and received his PHD degree from Al- Basra University, lraq, in 2006.His interesting area in image processing, Biometric, Artificial intelligent, pattern recognition.

Author Articles
Human Identification Using Foot Features

By Kadhim M.Hashem Fatima Ghali

DOI: https://doi.org/10.5815/ijem.2016.04.03, Pub. Date: 8 Jul. 2016

The goal of this paper is to investigate a new technique for human identification using foot features. This work can be mainly decomposed into image preprocessing, feature extraction and pattern recognition by Artificial Neural Network (ANN).
Foot images are rarely of perfect quality. To obtain good minutiae extraction in foot with varying quality, we conducted preprocessing in form of image enhancement and binarization .To extract features from human foot based on shape geometry of foot boundaries by extracting 16 geometric features from a human foot image. The foot center has been determined, and then the distances between the center point and outer points are measured with different angles .The angles are from 30˚ to 360˚ by increment with 30˚ gradual. The 13th feature that can be extracted is the length of a foot which is defined as the distance between the top point of the foot and the bottom point. The 14th, 15th and 16th are three major features the width of the foot. The first width is passing through center point, therefore, the second widths of foot is measured from the upper part above the center point and third width from the region the center point under the center point at the bottom of the foot. Euclidean distance is used in the proposed system. Artificial Neural network used for recognition.
MATLAB version 8.1(R2013a) and windows 7 with 32 bit is used to build the application and performed on pc of core i3 processor, and our test system on 40 persons, results were satisfactory up to more 92.5%.

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