Journal of ICT Research and Applications ITB Bandung Vol. 15 No. 3 2021
The Use of QLRBP and MLLPQ as Feature Extractors Combined with SVM and kNN Classifiers for Gender Recognition
Dublin Core
Title
Journal of ICT Research and Applications ITB Bandung Vol. 15 No. 3 2021
The Use of QLRBP and MLLPQ as Feature Extractors Combined with SVM and kNN Classifiers for Gender Recognition
The Use of QLRBP and MLLPQ as Feature Extractors Combined with SVM and kNN Classifiers for Gender Recognition
Subject
gender recognition; kNN; MLLPQ; QLRBP; SVM.
Description
Abstract. Security systems must be continuously developed in order to cope with new challenges. One example of such challenges is the proliferation of sexual harassment against women in public places, such as public toilets and public transportation. Although separately designated toilets or waiting and seating areas in public transports are provided, enforcing these restrictions need constant
manual surveillance. In this paper we propose an automatic gender classification system based on an individual’s facial characteristics. We evaluate the performance of QLRBP and MLLPQ as feature extractors combined with SVM or kNN as classifiers. Our experiments show that MLLPQ gives superior performance compared to QLRBP for either classifier. Furthermore, MLLPQ is
less computationally demanding compared to QLRBP. The best result we achieved in our experiments was the combination of MLLPQ and kNN classifier, yielding an accuracy rate of 92.11%.
manual surveillance. In this paper we propose an automatic gender classification system based on an individual’s facial characteristics. We evaluate the performance of QLRBP and MLLPQ as feature extractors combined with SVM or kNN as classifiers. Our experiments show that MLLPQ gives superior performance compared to QLRBP for either classifier. Furthermore, MLLPQ is
less computationally demanding compared to QLRBP. The best result we achieved in our experiments was the combination of MLLPQ and kNN classifier, yielding an accuracy rate of 92.11%.
Creator
Septian Abednego, Iwan Setyawan & Gunawan Dewantoro
Source
DOI: 10.5614/itbj.ict.res.appl.2021.15.3.4
Publisher
IRCS-ITB
Date
08 September 2021
Contributor
Sri Wahyuni
Rights
ISSN: 2337-5787
Format
PDF
Language
English
Type
Text
Coverage
Journal of ICT Research and Applications ITB Bandung Vol. 15 No. 3 2021
Files
Collection
Citation
Septian Abednego, Iwan Setyawan & Gunawan Dewantoro, “Journal of ICT Research and Applications ITB Bandung Vol. 15 No. 3 2021
The Use of QLRBP and MLLPQ as Feature Extractors Combined with SVM and kNN Classifiers for Gender Recognition,” Repository Horizon University Indonesia, accessed November 10, 2024, https://repository.horizon.ac.id/items/show/3432.
The Use of QLRBP and MLLPQ as Feature Extractors Combined with SVM and kNN Classifiers for Gender Recognition,” Repository Horizon University Indonesia, accessed November 10, 2024, https://repository.horizon.ac.id/items/show/3432.