Palmprint Recognition Based on Edge Detection Features and Convolutional Neural Network

I Ketut Gede Darma Putra (1), Deden Witarsyah (2), Muhardi Saputra (3), Putu Jhonarendra (4)
(1) Information Technology Department, Udayana University, Bukit Jimbaran, Badung, 80361, Indonesia
(2) Department of Information System, Faculty of Industrial Engineering, Telkom University, Bandung, 40257, Indonesia
(3) Department of Information System, Faculty of Industrial Engineering, Telkom University, Bandung, 40257, Indonesia
(4) Information Technology Department, Udayana University, Bukit Jimbaran, Badung, 80361, Indonesia
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How to cite (IJASEIT) :
Darma Putra, I Ketut Gede, et al. “Palmprint Recognition Based on Edge Detection Features and Convolutional Neural Network”. International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 1, Feb. 2021, pp. 380-7, doi:10.18517/ijaseit.11.1.11664.
Research on biometric technology get much attention from researchers who interest in the recognition system. One of the biometric objects that will continue to be developed is the palmprint. The hand palm line has a unique characteristic in each person or may not be the same. The palmprint image is easy to capture because clearly visible, so it does not require a specific sensor. This paper presents the automatic extraction feature with Convolutional Neural Network (CNN) technique to get a unique characteristic of palmprint image and identify a person. CNN will get easier to classify the image database if it has many data. CNN belongs to Supervised Learning, which requires training data to create a knowledge base. In the dataset with little training data, the system must increase the training data using augmentation methods like zoom, shear, and rotate. Still, in the palmprint, that augmentation method can change the original character of the palmprint. Our proposed method is adding training data with an edge detection image from the original image. Edge detection used in our method is Canny and Sobel. The addition of Canny and Sobel edge detection for training data is the best combination scenario for palmprint recognition. The experiment results showed that palmprint recognition using Convolution Neural Network with Canny and Sobel edge detection for training data resulted in an accuracy rate of 96.5% for 200 classes, and the Equal Error Rate (ERR) value is 3.5%. This method has been able to recognize 193 palms of 200 people.

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