Scholarlink

<p>The deployment of Facial Expression Recognition (FER) systems in resource-constrained edge computing environments is hindered by the high computational demands that often impede their operational efficacy. This paper introduces the Lightweight Attentional Convolutional Capsule Network (LAC-Capsnet), which has been carefully designed to achieve competitive classification performance while requiring a significantly reduced cardinality of trainable parameters. Its inherent novelty is the synergistic combination of an attentional mechanism (STN for geometric invariance and SE block for channel recalibration) with effective microarchitectures (Inception, Depthwise Separable Convolutions). To leverage dynamic routing and achieve a more refined spatial hierarchy modeling, this convolutional backbone is seamlessly integrated with a capsule network. Based on comprehensive validation, LAC-Capsnet achieves classification accuracy of 97.15% on CK + and 66.70% on FER2013, while requiring only 0.97&#xa0;million parameters. As a result, it was demonstrated to be a cost-effective and lightweight FER solution that showed efficacy and computational parsimony.</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

Lightweight attentional convolutional capsule network for facial expression recognition

  • 期刊论文

  • 发表日期：

    2026年2月26日

查看全文
  • Saeed Najafi Khanbebin,
  • Vahid Mehrdad

摘要

The deployment of Facial Expression Recognition (FER) systems in resource-constrained edge computing environments is hindered by the high computational demands that often impede their operational efficacy. This paper introduces the Lightweight Attentional Convolutional Capsule Network (LAC-Capsnet), which has been carefully designed to achieve competitive classification performance while requiring a significantly reduced cardinality of trainable parameters. Its inherent novelty is the synergistic combination of an attentional mechanism (STN for geometric invariance and SE block for channel recalibration) with effective microarchitectures (Inception, Depthwise Separable Convolutions). To leverage dynamic routing and achieve a more refined spatial hierarchy modeling, this convolutional backbone is seamlessly integrated with a capsule network. Based on comprehensive validation, LAC-Capsnet achieves classification accuracy of 97.15% on CK + and 66.70% on FER2013, while requiring only 0.97 million parameters. As a result, it was demonstrated to be a cost-effective and lightweight FER solution that showed efficacy and computational parsimony.