On the Performance of Pretrained CNN Aimed at Palm Vein Recognition Application

Meirista Wulandari; Dadang Gunawan

doi:10.1109/ICITEED.2019.8929938

On the Performance of Pretrained CNN Aimed at Palm Vein Recognition Application

Meirista Wulandari, Basari, Dadang Gunawan

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

Abstract

Biometric technology has been very highly developed as a recognition system as personal identity. Because biometric is attached to human body such as physical or behavioral. Many applications adopt biometric recognition as their security and access system such as smart house or smart building, banking access system, cellular phones and many more. Vascular pattern include vein pattern is being a very fast-growing research. Vein pattern identifies an individual from his vein features. The quality of infrared vein images need to be enhanced by increasing the contrast to extract the object from the background Many methodologies has been developed to create a robust system of recognition from feature extraction to classification method. And high developed algorithm for classification which is rapidly being developed is deep learning, Convolutional Neural Network (CNN). There are four pretrained structure of CNN that discussed in this paper, AlexNet, VGG-16, VGG-19 and GoogLeNet. AlexNet seems to be the simplest in depth. The accuracy of AlexNet is better among others with 93.92% ±0.98334.

Original language	English
Title of host publication	2019 11th International Conference on Information Technology and Electrical Engineering, ICITEE 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728140551
DOIs	https://doi.org/10.1109/ICITEED.2019.8929938
Publication status	Published - Oct 2019
Event	11th International Conference on Information Technology and Electrical Engineering, ICITEE 2019 - Pattaya, Thailand Duration: 10 Oct 2019 → 11 Oct 2019

Publication series

Name	2019 11th International Conference on Information Technology and Electrical Engineering, ICITEE 2019

Conference

Conference	11th International Conference on Information Technology and Electrical Engineering, ICITEE 2019
Country/Territory	Thailand
City	Pattaya
Period	10/10/19 → 11/10/19

Keywords

AlexNet
GoogLeNet
palm vein
pretrained CNN
recognition
VGG-16
VGG-19

Access to Document

10.1109/ICITEED.2019.8929938

Cite this

Wulandari, M., Basari , & Gunawan, D. (2019). On the Performance of Pretrained CNN Aimed at Palm Vein Recognition Application. In 2019 11th International Conference on Information Technology and Electrical Engineering, ICITEE 2019 Article 8929938 (2019 11th International Conference on Information Technology and Electrical Engineering, ICITEE 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICITEED.2019.8929938

Wulandari, Meirista ; Basari, ; Gunawan, Dadang. / On the Performance of Pretrained CNN Aimed at Palm Vein Recognition Application. 2019 11th International Conference on Information Technology and Electrical Engineering, ICITEE 2019. Institute of Electrical and Electronics Engineers Inc., 2019. (2019 11th International Conference on Information Technology and Electrical Engineering, ICITEE 2019).

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abstract = "Biometric technology has been very highly developed as a recognition system as personal identity. Because biometric is attached to human body such as physical or behavioral. Many applications adopt biometric recognition as their security and access system such as smart house or smart building, banking access system, cellular phones and many more. Vascular pattern include vein pattern is being a very fast-growing research. Vein pattern identifies an individual from his vein features. The quality of infrared vein images need to be enhanced by increasing the contrast to extract the object from the background Many methodologies has been developed to create a robust system of recognition from feature extraction to classification method. And high developed algorithm for classification which is rapidly being developed is deep learning, Convolutional Neural Network (CNN). There are four pretrained structure of CNN that discussed in this paper, AlexNet, VGG-16, VGG-19 and GoogLeNet. AlexNet seems to be the simplest in depth. The accuracy of AlexNet is better among others with 93.92% ±0.98334.",

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Wulandari, M, Basari, & Gunawan, D 2019, On the Performance of Pretrained CNN Aimed at Palm Vein Recognition Application. in 2019 11th International Conference on Information Technology and Electrical Engineering, ICITEE 2019., 8929938, 2019 11th International Conference on Information Technology and Electrical Engineering, ICITEE 2019, Institute of Electrical and Electronics Engineers Inc., 11th International Conference on Information Technology and Electrical Engineering, ICITEE 2019, Pattaya, Thailand, 10/10/19. https://doi.org/10.1109/ICITEED.2019.8929938

On the Performance of Pretrained CNN Aimed at Palm Vein Recognition Application. / Wulandari, Meirista; Basari, ; Gunawan, Dadang.
2019 11th International Conference on Information Technology and Electrical Engineering, ICITEE 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8929938 (2019 11th International Conference on Information Technology and Electrical Engineering, ICITEE 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Basari,

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AB - Biometric technology has been very highly developed as a recognition system as personal identity. Because biometric is attached to human body such as physical or behavioral. Many applications adopt biometric recognition as their security and access system such as smart house or smart building, banking access system, cellular phones and many more. Vascular pattern include vein pattern is being a very fast-growing research. Vein pattern identifies an individual from his vein features. The quality of infrared vein images need to be enhanced by increasing the contrast to extract the object from the background Many methodologies has been developed to create a robust system of recognition from feature extraction to classification method. And high developed algorithm for classification which is rapidly being developed is deep learning, Convolutional Neural Network (CNN). There are four pretrained structure of CNN that discussed in this paper, AlexNet, VGG-16, VGG-19 and GoogLeNet. AlexNet seems to be the simplest in depth. The accuracy of AlexNet is better among others with 93.92% ±0.98334.

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Wulandari M, Basari , Gunawan D. On the Performance of Pretrained CNN Aimed at Palm Vein Recognition Application. In 2019 11th International Conference on Information Technology and Electrical Engineering, ICITEE 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8929938. (2019 11th International Conference on Information Technology and Electrical Engineering, ICITEE 2019). doi: 10.1109/ICITEED.2019.8929938

On the Performance of Pretrained CNN Aimed at Palm Vein Recognition Application

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