Real-time Gender Classification From Human Gait for Arbitrary View Angles

本篇論文研究的課題是，如何透過人類步態來進行性別辨識。這是一個十分重要但仍未完全解決的問題。在過程中，我們證明了使用GEI(Gait Energy Image)可以有效地描述從不同角度所觀察到的人類步態。並且，以GEI為特徵，我們透過幾個不同方法，建構了人類步態的角度辨識法，以及性別辨識法。最後透過實驗，顯示了依照我們所提出的方法所建構的系統，可以有效地將即時性別辨識應用於實際狀況中。In this thesis, we investigate an important but understudied problem, gender classification from human gaits. And we have proved the ability of using GEI (Gait Energy Image) as a representation of human gait for arbitrary view angles. Using GEI as a discriminative feature, we constructed angle classifiers and gender classifiers from different approaches. Experiments have shown that our system achieved a good performance and is able to be applied to real-world application.中文摘要 iibstract iiihapter 1 Introduction 1.1 MOTIVATION 1.2 RELATED WORKS 3.2.1 Psychophysical Studies 3.2.2 Computational Approach to Gender Classification from Human Gait 5.2.3 Gait Energy Image 7hapter 2 Human Gait Modeling 13hapter 3 Angle Classification 19.1 ELEVEN-CLASS ANGLE CLASSIFICATION 19.2 FIVE-GROUP ANGLE CLASSIFICATION 26hapter 4 Gender Classification 28.1 FISHER-BOOSTING 28.2 ELEVEN-CLASS GENDER CLASSIFICATION 30.3 FIVE-GROUP GENDER CLASSIFICATION 33hapter 5 Experimental Results 35.1 SYSTEM OVERVIEW 35.2 ANGLE CLASSIFICATION + GENDER CLASSIFICATION 37.2.1 Eleven-Class Approach 37.2.2 Five-Group Approach 38.3 REAL-WORLD VIDEO TESTING 38hapter 6 Conclusion and Future Work 40eference 4