A suggested system for palmprint recognition using curvelet transform and co-occurrence matrix

الملخص EN

The main purpose of this paper is to create a palmprint recognition system (PPRS) that uses the curvelet transform and co-occurrence matrix to recognize a hand's palmprint.

The suggested system is composed of several stages: in the first stage, the region of interest (ROI) was taken from a palmprint image, then in the second stage, the curvelet transform was applied to the (ROI) to get a blurred version of the image, and finally, unsharp masking process and sobel filtering were done for edge detection.

The third stage involves feature extraction using a co-occurrence matrix to obtain 16 features, while the fourth stage inclusion is the training and testing of the suggested approach.

The algorithm ACO (ant colony optimization) has been adopted to evaluate the shortest path to the goal.

CASIA PalmprintV dataset of 100 people (60 male and 40 female) was used in proposed work to rate the performance of the proposed system.

ARR and EER metrics have been adopted to assess the performance of the proposed system.

The experimental results showed a very high recognition rate (ARR) that reaches 100% for the right hand of a male and the left hand of a female.

The overall accuracy rate (ARR) reaches 98.5% and EER equals 0.015.

The main purpose of this paper is to create a palmprint recognition system (PPRS) that uses the curvelet transform and co-occurrence matrix to recognize a hand's palmprint.

The suggested system is composed of several stages: in the first stage, the region of interest (ROI) was taken from a palmprint image, then in the second stage, the curvelet transform was applied to the (ROI) to get a blurred version of the image, and finally, unsharp masking process and sobel filtering were done for edge detection.

The third stage involves feature extraction using a co-occurrence matrix to obtain 16 features, while the fourth stage inclusion is the training and testing of the suggested approach.

The algorithm ACO (ant colony optimization) has been adopted to evaluate the shortest path to the goal.

CASIA PalmprintV dataset of 100 people (60 male and 40 female) was used in proposed work to rate the performance of the proposed system.

ARR and EER metrics have been adopted to assess the performance of the proposed system.

The experimental results showed a very high recognition rate (ARR) that reaches 100% for the right hand of a male and the left hand of a female.

The overall accuracy rate (ARR) reaches 98.5% and EER equals 0.015.