TY - GEN
T1 - Accuracy Comparison of Home Security Face Recognition Model in the Several Lighting Condition Using Some Kinect Produced Image
AU - Perdana, Octgi Ristya
AU - Tjahyanto, Aris
AU - Samopa, Febriliyan
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/4/9
Y1 - 2021/4/9
N2 - As technology advances, security systems and the development of artificial intelligence systems around the world continue to be developed. Artificial intelligence is very often used to automate parts of a system. In terms of data security and physical security, the use of artificial intelligence to be able to recognize a person's face plays a very important role in increasing the efficiency and management of knowledge. This paper was written with the aim of proposing a comprehensive comparison of accuracy, in recognizing faces using the features found on the Kinect camera (depth images, 3-dimensional images, and infrared images), compared to features found on ordinary cameras (RGB images), using techniques digital image processing in several different light conditions (dark room conditions and bright / standard room lighting). This research was conducted by comparing how reliably the facial recognition system classifies images that are part of the data class, as well as fake class images in the home security system. From this research, the Support Vector Machine (SVM) algorithm is used for classification, resulting in 20% better accuracy for images produced by Kinect cameras than for standard RGB cameras.
AB - As technology advances, security systems and the development of artificial intelligence systems around the world continue to be developed. Artificial intelligence is very often used to automate parts of a system. In terms of data security and physical security, the use of artificial intelligence to be able to recognize a person's face plays a very important role in increasing the efficiency and management of knowledge. This paper was written with the aim of proposing a comprehensive comparison of accuracy, in recognizing faces using the features found on the Kinect camera (depth images, 3-dimensional images, and infrared images), compared to features found on ordinary cameras (RGB images), using techniques digital image processing in several different light conditions (dark room conditions and bright / standard room lighting). This research was conducted by comparing how reliably the facial recognition system classifies images that are part of the data class, as well as fake class images in the home security system. From this research, the Support Vector Machine (SVM) algorithm is used for classification, resulting in 20% better accuracy for images produced by Kinect cameras than for standard RGB cameras.
KW - 3D Image
KW - Dark Image
KW - Face recognition
UR - http://www.scopus.com/inward/record.url?scp=85107290248&partnerID=8YFLogxK
U2 - 10.1109/EIConCIT50028.2021.9431860
DO - 10.1109/EIConCIT50028.2021.9431860
M3 - Conference contribution
AN - SCOPUS:85107290248
T3 - 3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021
SP - 105
EP - 110
BT - 3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021
A2 - Alfred, Rayner
A2 - Haviluddin, Haviluddin
A2 - Wibawa, Aji Prasetya
A2 - Santoso, Joan
A2 - Kurniawan, Fachrul
A2 - Junaedi, Hartarto
A2 - Purnawansyah, Purnawansyah
A2 - Setyati, Endang
A2 - Saurik, Herman Thuan To
A2 - Setiawan, Esther Irawati
A2 - Setyaningsih, Eka Rahayu
A2 - Pramana, Edwin
A2 - Kristian, Yosi
A2 - Kelvin, Kelvin
A2 - Purwanto, Devi Dwi
A2 - Kardinata, Eunike
A2 - Anugrah, Prananda
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd East Indonesia Conference on Computer and Information Technology, EIConCIT 2021
Y2 - 9 April 2021 through 11 April 2021
ER -