Gender Classification on Video Using FaceNet Algorithm and Supervised Machine Learning

Gender classification using human face data becomes a trending topic for researchers in the field of image processing and computer vision. The human face is biometric information that can be used to differentiate gender using a computer-aided system. Previous research utilised a local feature algori...

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Bibliographic Details
Published in:International Journal of Computing and Digital System (Jāmiʻat al-Baḥrayn. Markaz al-Nashr al-ʻIlmī) Vol. 11; no. 1; pp. 199 - 208
Main Authors: Adhinata, Faisal Dharma, Junaidi, Apri
Format: Journal Article
Language:English
Published: University of Bahrain, Deanship of Graduate Studies and Scientific Research 2022
Subjects:
Algorithms
Equipment and supplies
Image processing
Machine learning
Machine vision
ISSN:2210-142X
Online Access:Get full text
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Summary:Gender classification using human face data becomes a trending topic for researchers in the field of image processing and computer vision. The human face is biometric information that can be used to differentiate gender using a computer-aided system. Previous research utilised a local feature algorithm for extracting features on the face. However, the processing speed for one image was more than 2 seconds, making it unsuitable for real-time processing using video data. Processing video data requires a fast feature extraction algorithm because video data collects sequential images (frames). Moreover, the gender classification system's success is also measured by its accuracy, consequently it is necessary to choose the correct classification method to divide the two classes of men and women. In this research, we propose the FaceNet algorithm for feature extraction and explore several supervised machine learning methods (KNN, SVM, and Decision tree) appropriate for gender classification on video data. This study used 23,000 training data on each gender. From the experiment, combination of the FaceNet algorithm and KNN method resulted in the best accuracy of 95.75% with a processing speed of 0.059 seconds on each frame. Keywords: Gender Classification; Real-time Processing; FaceNet Algorithm; Supervised Machine Learning
ISSN:2210-142X
DOI:10.12785/ijcds/110116