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Palmprint Recognition Based on Edge Detection Features and Convolutional Neural Network

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@article{IJASEIT11664,
   author = {I Ketut Gede Darma Putra and Deden Witarsyah and Muhardi Saputra and Putu Jhonarendra},
   title = {Palmprint Recognition Based on Edge Detection Features and Convolutional Neural Network},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {11},
   number = {1},
   year = {2021},
   pages = {380--387},
   keywords = {Palmprint recognition; edge detection; convolutional neural network.},
   abstract = {

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.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11664},    doi = {10.18517/ijaseit.11.1.11664} }

EndNote

%A Darma Putra, I Ketut Gede
%A Witarsyah, Deden
%A Saputra, Muhardi
%A Jhonarendra, Putu
%D 2021
%T Palmprint Recognition Based on Edge Detection Features and Convolutional Neural Network
%B 2021
%9 Palmprint recognition; edge detection; convolutional neural network.
%! Palmprint Recognition Based on Edge Detection Features and Convolutional Neural Network
%K Palmprint recognition; edge detection; convolutional neural network.
%X 

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.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11664 %R doi:10.18517/ijaseit.11.1.11664 %J International Journal on Advanced Science, Engineering and Information Technology %V 11 %N 1 %@ 2088-5334

IEEE

I Ketut Gede Darma Putra,Deden Witarsyah,Muhardi Saputra and Putu Jhonarendra,"Palmprint Recognition Based on Edge Detection Features and Convolutional Neural Network," International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 1, pp. 380-387, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.1.11664.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Darma Putra, I Ketut Gede
AU  - Witarsyah, Deden
AU  - Saputra, Muhardi
AU  - Jhonarendra, Putu
PY  - 2021
TI  - Palmprint Recognition Based on Edge Detection Features and Convolutional Neural Network
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 1
Y2  - 2021
SP  - 380
EP  - 387
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - Palmprint recognition; edge detection; convolutional neural network.
N2  - 

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.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11664 DO - 10.18517/ijaseit.11.1.11664

RefWorks

RT Journal Article
ID 11664
A1 Darma Putra, I Ketut Gede
A1 Witarsyah, Deden
A1 Saputra, Muhardi
A1 Jhonarendra, Putu
T1 Palmprint Recognition Based on Edge Detection Features and Convolutional Neural Network
JF International Journal on Advanced Science, Engineering and Information Technology
VO 11
IS 1
YR 2021
SP 380
OP 387
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 Palmprint recognition; edge detection; convolutional neural network.
AB 

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.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=11664 DO - 10.18517/ijaseit.11.1.11664