Scholarlink

<p>Thermal imaging offers a promising solution for gender classification, surpassing traditional visible-light approaches by effectively handling fluctuating lighting conditions, shadows and facial occlusions. In this work, Thermal Squeeze and Excitation ResNet (TH-SE-ResNet), an innovative Convolutional Neural Network (CNN) architecture is proposed. TH-SE-ResNet model integrates Squeeze-and-Excitation (SE) blocks to enhance the model’s ability to prioritize relevant features and employs a channel input adapter to address varying input formats of different thermal dataset. This model is used to classify gender from thermal facial images. This study tackles significant obstacles in thermal image analysis, such as inconsistencies in dataset channel configurations, imbalanced class distributions and the challenge of ensuring model robustness across diverse thermal imaging scenarios.&#xa0;The performance of proposed model is compared with several prominent CNN models like AlexNet, VGG, InceptionV3, ResNet50 and EfficientNet across two thermal face datasets: the Tufts University Thermal Face Dataset and the Charlotte-ThermalFace Dataset. Experimental results shows that TH-SE-ResNet demonstrates highly competitive performance achieving a remarkable 97% accuracy on the Tufts dataset, 85% on the Charlotte dataset, and 90% on a merged dataset. The code and implementation details are shared at&#xa0;<a href="https://github.com/adityabhattad2021/evaluating-deep-architectures-for-thermal-gender-detection">https://github.com/adityabhattad2021/evaluating-deep-architectures-for-thermal-gender-detection</a>.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Introducing TH-SE-ResNet for Enhanced Performance for Gender Detection Using Thermal Images

  • 期刊论文

  • 发表日期：

    2026年4月12日

查看全文
  • Pravinkumar M. Sonsare,
  • Khushboo Khurana,
  • Preeti Voditel,
  • Aditya Bhattad,
  • Itisha Shastri

摘要

Thermal imaging offers a promising solution for gender classification, surpassing traditional visible-light approaches by effectively handling fluctuating lighting conditions, shadows and facial occlusions. In this work, Thermal Squeeze and Excitation ResNet (TH-SE-ResNet), an innovative Convolutional Neural Network (CNN) architecture is proposed. TH-SE-ResNet model integrates Squeeze-and-Excitation (SE) blocks to enhance the model’s ability to prioritize relevant features and employs a channel input adapter to address varying input formats of different thermal dataset. This model is used to classify gender from thermal facial images. This study tackles significant obstacles in thermal image analysis, such as inconsistencies in dataset channel configurations, imbalanced class distributions and the challenge of ensuring model robustness across diverse thermal imaging scenarios. The performance of proposed model is compared with several prominent CNN models like AlexNet, VGG, InceptionV3, ResNet50 and EfficientNet across two thermal face datasets: the Tufts University Thermal Face Dataset and the Charlotte-ThermalFace Dataset. Experimental results shows that TH-SE-ResNet demonstrates highly competitive performance achieving a remarkable 97% accuracy on the Tufts dataset, 85% on the Charlotte dataset, and 90% on a merged dataset. The code and implementation details are shared at https://github.com/adityabhattad2021/evaluating-deep-architectures-for-thermal-gender-detection.