Facial Images Improvement in the LBPH Algorithm Using the Histogram Equalization Method

Aditya Salman; Mardhiya Hayaty; Ika Nur Fajri

doi:10.30595/juita.v10i2.13223

Authors

Aditya Salman Universitas Amikom Yogyakarta
Mardhiya Hayaty Universitas Amikom Yogyakarta
Ika Nur Fajri Universitas Amikom Yogyakarta

DOI:

https://doi.org/10.30595/juita.v10i2.13223

Keywords:

LBPH, Histogram Equalization

Abstract

In face recognition research, detecting several parts of the face becomes a necessary part of the study. The main factor in this work is lighting; some obstacles emerge when the low light's intensity falls in the process of face detection because of some conditions, such as weather, season, and sunlight. This study focuses on detecting faces in dim lighting using the Local Binary Pattern Histogram (LBPH) algorithm assisted by the Classifier Method, which is often used in face detection, namely the Haar Cascade Classifier. Furthermore, It will employ the image enhancement method, namely Histogram Equalization (HE), to improve the image source from the webcam. In the evaluation, different light intensities and various head poses affect the accuracy of the method. As a result, The research reaches 88% accuracy for successful face detection. Some factors such as head accessories, hair covering the face, and several parts of the face, like the eye, mouth, and nose that are invisible, should not be extreme.

Author Biographies

Aditya Salman, Universitas Amikom Yogyakarta

Faculty of Computer Science

Mardhiya Hayaty, Universitas Amikom Yogyakarta

Faculty of Computer Science

Ika Nur Fajri, Universitas Amikom Yogyakarta

Faculty of Computer Science

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