A Comparative Study On Textures Descriptors In Facial Gender Classification
Fares Bougourzi, Salah Eddine Bekhouche, Mohammed En-nadhir Zighem, Azeddine Benlamoudi, Abdelkrim Ouafi, Abdelmalik Taleb‐Ahmed · 2017
The aim of this work is to investigate global and local image descriptors impact on facial gender classification by carrying out an independent comparative study among several texture descriptors algorithms. In this paper, we consider three global descriptors namely, Gray-Level Co-Occurrence Matrix (GLCM), Gabor Wavelet Transform (GWT) and Autocorrelation Function (ACF). On the other hand, we consider four local image descriptors called, Local Binary Patterns (LBP), Local Directional Pattern (LDP), Local Phase Quantization (LPQ) and Binarized Statistical Image Features (BSIF). The experimental comparison proofs that the local image descriptors are more efficient than the global ones in facial gender classification. All the experiments conducted on the Image of Groups (IoG) database.