The Use of QLRBP and MLLPQ as Feature Extractors Combined with SVM and kNN Classifiers for Gender Recognition

Septian Abednego (Faculty of Electronic and Computer Engineering, Satya Wacana Christian University Jalan Diponegoro No. 52-60, Salatiga 50711, Indonesia)
Iwan Setyawan (Faculty of Electronic and Computer Engineering, Satya Wacana Christian University Jalan Diponegoro No. 52-60, Salatiga 50711, Indonesia)
Gunawan Dewantoro (Faculty of Electronic and Computer Engineering, Satya Wacana Christian University Jalan Diponegoro No. 52-60, Salatiga 50711, Indonesia)

Journal of ICT Research and Applications
Vol. 15 No. 3 (2021)

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%.