Deep Neural Networks With Distance Distributions for Gender Recognition of 3D Human Shapes

Hui Wang,Junjie Cao,Nannan Li,Xiaoyang Lin,Fan Meng,Xiuping Liu

doi:10.1109/access.2020.3042299

Abstract

Automatic human gender recognition is an important and classical problem in artificial intelligence. Most of the previous gender recognition works are based on vision appearance and biometric characteristics. However, there are fewer gender recognition approaches for 3D human shapes. In this article, we propose a novel deep neural network learning method for gender recognition of 3D human shapes. Firstly, we introduce effective descriptors to distinguish male and female of 3D human shapes via probability distributions of biharmonic distances among points. Secondly, the above distances-based low-level descriptors are fed into a fully connected neural network for gender recognition. Furthermore, we construct a larger 3D human shape dataset for evaluation of the proposed gender recognition method by collecting and labeling human shape models. Compared with previous works, our method obtains higher recognition accuracy and has more advantages, such as posture invariant, robust to noises, and no need of landmarks or pre-alignment process.

Highlights

G ENDER is an important physiological and demographic attribute of people
Based on types of features that differentiate between male and female, previous gender recognition works can be mainly divided into vision appearances and biometric characteristics approaches [2]
Pairwise geodesic distances between the 73 manually placed anthropometric landmarks on shapes from the 3D human dataset CAESAR are used as features for gender recognition, where the classifiers are the traditional linear discriminant function, bayesian decision boundary, and Support Vector Machine (SVM) [3]

Summary

INTRODUCTION

G ENDER is an important physiological and demographic attribute of people. Many real-life applications utilize gender information such as human-computer interaction, surveillance system, commercial development, video game, and social security. Geodesic distances between some manually placed anthropometric landmarks for each of the scanned subject from the 3D human dataset CAESAR (Civilian American and European Surface Anthropometry Resource) [7] are used for gender classification [3]. We give a conclusion and point out some possible future works

RELATED WORK

METHODS

DEEP NEURAL NETWORK

Findings

EXPERIMENTS