Scholarlink

Age estimation from facial images plays a vital role in applications ranging from access control to advanced facial analysis systems. While this task presents significant challenges due to the subtle evolution of facial features over time, this paper introduces a graph-based framework to address these complexities. The proposed architecture integrates an Adaptive Facial Key Point Estimation Module that efficiently handles redundancy and self-occlusion challenges, alongside a Node Feature Extraction Module that processes key point patches to enhance age-specific characteristics. At its core, a Graph Convolution Network captures intricate relationships between different facial features. Tested on “in the wild” images—photos taken in uncontrolled environments with varying lighting, poses, and quality conditions–this system showcases remarkable robustness and reliability.

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

Integrating Adaptive Key Points and CNN Features for Graph-Based Age Estimation from Facial Image

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Sadhvik Bathini,
  • P. Elamukilan,
  • Santhoshkumar Peddi,
  • Debasis Samanta

摘要

Age estimation from facial images plays a vital role in applications ranging from access control to advanced facial analysis systems. While this task presents significant challenges due to the subtle evolution of facial features over time, this paper introduces a graph-based framework to address these complexities. The proposed architecture integrates an Adaptive Facial Key Point Estimation Module that efficiently handles redundancy and self-occlusion challenges, alongside a Node Feature Extraction Module that processes key point patches to enhance age-specific characteristics. At its core, a Graph Convolution Network captures intricate relationships between different facial features. Tested on “in the wild” images—photos taken in uncontrolled environments with varying lighting, poses, and quality conditions–this system showcases remarkable robustness and reliability.