Gender Classification Based on Multiscale Facial Fusion Feature

Chunyu Zhang,Hui Ding,Zhuhong Shao,Xiaoyan Fu,Yuanyuan Shang

doi:10.1155/2018/1924151

Abstract

For gender classification, we present a new approach based on Multiscale facial fusion feature (MS3F) to classify gender from face images. Fusion feature is extracted by the combination of Local Binary Pattern (LBP) and Local Phase Quantization (LPQ) descriptors, and a multiscale feature is generated through Multiblock (MB) and Multilevel (ML) methods. Support Vector Machine (SVM) is employed as the classifier to conduct gender classification. All the experiments are performed based on the Images of Groups (IoG) dataset. The results demonstrate that the application of Multiscale fusion feature greatly improves the performance of gender classification, and our approach outperforms the state-of-the-art techniques.

Highlights

Gender classification plays an important role in many scenarios
We propose a new approach, Multiscale facial fusion feature (MS3F), to classify gender for the face images which are captured in uncontrolled conditions
Local Binary Pattern (LBP) characterizes the spatial structure of a local image texture pattern, and Local Phase Quantization (LPQ) is based on computing short-term Fourier transform (STFT) on the local image window

Summary

Introduction

As one of the demographic classification attributes, gender information belongs to soft biometrics that provides ancillary information of an individual’s identity information It can improve the performance of face recognition. Golomb et al [4] trained a fully connected three-layer neural network to discriminate gender for a set of 90 face images in the early 1990s. Only the single facial feature could be extracted from the images, which are captured in controlled conditions in above-mentioned methods. We propose a new approach, Multiscale facial fusion feature (MS3F), to classify gender for the face images which are captured in uncontrolled conditions. Each face image is divided into two blocks and LBP is applied to the top block to extract feature, while LPQ is applied to the bottom block.

Our Approach

Experimental Results and Discussion

Conclusion

Conflicts of Interest