Indian Journal of Science and Technology

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An Approach to Improve Palmprint Recognition Accuracy by using Different Region of Interest Methods with Local Binary Pattern Technique


Affiliations
1 Department of CS&IT, Vision and Intelligent System Lab, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad − 431001, Maharashtra, India
2 Institute of Management Studies and Information Technology, Aurangabad − 431001, Maharashtra, India
 

Objective: To extract the Region of Interest (ROI) of palmprint image by using appropriate methods and to improve the accuracy of palmprint recognition system. Methods/Statistical Analysis: This piece of work is primarily addressing the different mechanisms for extracting ROI area. The techniques like Competitive Hand Valley Detection (CHVD), and Euclidean Distance (ED) were applied as the part of pre-processing, while the Feature Extraction mechanism LBP was utilized to extract the texture feature from different type of ROIs of palmprint image. Findings: The experimental results showed that CHVD with LBP gave best result with high accuracy reached to 96.10534% and Equal Error Rate (EER) of 3.894661%, while in ED the best result showed accuracy reached to 88.23611% and EER of 11.76389%. Application/Improvements: The study mainly concentrated on developing palmprint authentication system with less EER and high accuracy.
User

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  • An Approach to Improve Palmprint Recognition Accuracy by using Different Region of Interest Methods with Local Binary Pattern Technique

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Authors

Mouad M. H. Ali
Department of CS&IT, Vision and Intelligent System Lab, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad − 431001, Maharashtra, India
A. T. Gaikwad
Institute of Management Studies and Information Technology, Aurangabad − 431001, Maharashtra, India
Pravin Yannawar
Department of CS&IT, Vision and Intelligent System Lab, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad − 431001, Maharashtra, India

Abstract


Objective: To extract the Region of Interest (ROI) of palmprint image by using appropriate methods and to improve the accuracy of palmprint recognition system. Methods/Statistical Analysis: This piece of work is primarily addressing the different mechanisms for extracting ROI area. The techniques like Competitive Hand Valley Detection (CHVD), and Euclidean Distance (ED) were applied as the part of pre-processing, while the Feature Extraction mechanism LBP was utilized to extract the texture feature from different type of ROIs of palmprint image. Findings: The experimental results showed that CHVD with LBP gave best result with high accuracy reached to 96.10534% and Equal Error Rate (EER) of 3.894661%, while in ED the best result showed accuracy reached to 88.23611% and EER of 11.76389%. Application/Improvements: The study mainly concentrated on developing palmprint authentication system with less EER and high accuracy.

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DOI: https://doi.org/10.17485/ijst%2F2018%2Fv11i22%2F122752