SAX2SEX: Gender Classification on 3D Faces using Symbolic Aggregate ApproXimation
Samia Bentaieb, Abdelaziz Ouamri, Mokhtar Keche · 2019
Gender classification is a demographic attribute that found an increasing amount of applications particularly in human-computer interaction, security access control and biometrics. The purpose of this paper is to investigate the feasibility of using time series for gender classification. After transforming input depth images of faces into one-dimensional vector using Peano-Hilbert space filling curve, a dimensionality reduction step is performed to convert this vector to a sequence of alphabets using Symbolic Aggregate approXimation (SAX). With FRGCv2 dataset, standard 10-fold cross-validation experiments are performed using quadratic kernel for SVM classifier. The results have shown that the proposed approach can reach a correct classification rate higher than 96%.