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Sayeed Al-Aidid
Politeknik Negeri Batam
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Sistem Pengenalan Wajah dengan Algoritma Haar Cascade dan Local Binary Pattern Histogram Sayeed Al-Aidid; Daniel Pamungkas
Jurnal Rekayasa Elektrika Vol 14, No 1 (2018)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (708.205 KB) | DOI: 10.17529/jre.v14i1.9799

Abstract

Recently, the applications of face recognition are increasing significantly. Some methods have already been tried, but the results have not optimal yet. This paper tries to overcome this problem, using haar cascade as face detection algorithm, whereas face recognition uses local binary pattern histogram method. This system uses a webcam as a camera and programming exploit OpenCV library. This system enables to differentiate the face of the human with others objects with the best range from the camera to the object is 50 cm until 150 cm. In addition, this system is capable to recognize faces from the 6 subjects of faces listed in the database, alone and in a group as well in one frame.
Sistem Pengenalan Wajah dengan Algoritma Haar Cascade dan Local Binary Pattern Histogram Sayeed Al-Aidid; Daniel Pamungkas
Jurnal Rekayasa Elektrika Vol 14, No 1 (2018)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v14i1.9799

Abstract

Recently, the applications of face recognition are increasing significantly. Some methods have already been tried, but the results have not optimal yet. This paper tries to overcome this problem, using haar cascade as face detection algorithm, whereas face recognition uses local binary pattern histogram method. This system uses a webcam as a camera and programming exploit OpenCV library. This system enables to differentiate the face of the human with others objects with the best range from the camera to the object is 50 cm until 150 cm. In addition, this system is capable to recognize faces from the 6 subjects of faces listed in the database, alone and in a group as well in one frame.
Co-Authors Daniel Pamungkas