Automatic Gait Gender Classification Using Convolutional Neural Networks

Proceedings of the 2023 5th International Conference on Image Processing and Machine Vision Pub Date : 2023-01-13 DOI:10.1145/3582177.3582184

L. Srinivasan

引用次数: 2

Abstract

In this study, automatic gait gender classification using convolutional neural networks includes three phases: i) human gait signature generation, ii) which convolves the gait energy images with filters for feature extraction and iii) classified using feed-forward convolutional neural networks. Analysed performance of Gabor and Log Gabor features using classification accuracy. The Log Gabor filter's accuracy was 92.11% for the Normal vs Normal dataset, 74.14% for the Normal vs Bag dataset, 46.55% for the Normal vs Coat dataset, 72.41% for the Normal vs Case dataset and whiles Gabor filter's accuracy was 75% for the Normal vs Normal dataset, 60.34% for the Normal vs Bag dataset 65.52% for the Normal vs Coat dataset and 55.17% for the Normal vs Case dataset.

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基于卷积神经网络的步态性别自动分类

在本研究中，基于卷积神经网络的步态性别自动分类包括三个阶段:1)人体步态特征生成;2)对步态能量图像进行卷积滤波进行特征提取;3)利用前馈卷积神经网络进行分类。利用分类精度分析了Gabor和Log Gabor特征的性能。Log Gabor过滤器对于Normal vs Normal数据集的准确率为92.11%，对于Normal vs Bag数据集的准确率为74.14%，对于Normal vs Coat数据集的准确率为46.55%，对于Normal vs Case数据集的准确率为72.41%，而对于Normal vs Normal数据集的准确率为75%，对于Normal vs Bag数据集的准确率为60.34%，对于Normal vs Coat数据集的准确率为65.52%，对于Normal vs Case数据集的准确率为55.17%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Proceedings of the 2023 5th International Conference on Image Processing and Machine Vision

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