Face Gender Classification using Combination of LPQ-Self PCA

Abstract

The age factor had a significant impact on human faces, potentially influencing the performance of existing gender classification systems. This research proposed a new method that combined local descriptors such as Local Binary Patterns (LBP) and Local Phase Quantization (LPQ) with Self-Principal Component Analysis (Self-PCA) as a feature extraction technique. The use of Self-PCA was chosen for its ability to address the age factor in human facial images, while also leveraging local descriptors to capture features from these images. The primary focus was to compare the performance of Self-PCA with LPQ+Self-PCA, along with the additional comparison of LBP+Self-PCA, in the task of gender classification using facial images. Euclidean distance served as the classifier, and the evaluation was conducted using the FG-Net and ORL datasets. The combination of LPQ+Self-PCA showed an improvement in accuracy by 57.85% compared to the combination of LBP+Self-PCA, which provided an accuracy of 56.47%. Meanwhile, using Self-PCA alone gave an accuracy of 55.37% on the FG-Net. In contrast, on the ORL dataset, both combinations gave the same accuracy result as Self-PCA, which was 90.14%, for images without blurring.

Keywords

• self-principal component analisys, local binary patterns, local phase quantization, face gender classification