用人脸语义特征提升人脸识别模型的可解释性。
Found in Translation: semantic approaches for enhancing AI interpretability in face verification
- 结合用户选定的人脸关键点生成语义相似图与文本解释。
- 用户研究显示语义解释比传统热力图更易理解。
- 适合需要高可信度的人脸识别应用场景。
计算机视觉中机器学习模型的复杂性日益增加，尤其是在人脸识别领域，亟需可解释人工智能（XAI）以提升透明度和可理解性。本研究通过将人类认知过程中的语义概念融入XAI框架，弥补模型输出与人类理解之间的差距。提出一种结合全局与局部解释的新方法，利用用户选定的人脸关键点定义语义特征，通过大语言模型（LLMs）生成相似性图和文本解释。通过定量实验和用户反馈验证，结果表明基于语义的方法，尤其是最详细版本，能提供比传统方法更细致的模型决策理解。用户研究显示，参与者更偏好我们的语义解释而非传统的像素级热力图，凸显了以人为本的可解释性在关键应用中的优势。该工作推动了与人类认知对齐的XAI框架发展，有助于增强公众对人工智能的信任与接受度。
原文摘要 · Abstract (English)
The increasing complexity of machine learning models in computer vision, particularly in face verification, requires the development of explainable artificial intelligence (XAI) to enhance interpretability and transparency. This study extends previous work by integrating semantic concepts derived from human cognitive processes into XAI frameworks to bridge the comprehension gap between model outputs and human understanding. We propose a novel approach combining global and local explanations, using semantic features defined by user-selected facial landmarks to generate similarity maps and textual explanations via large language models (LLMs). The methodology was validated through quantitative experiments and user feedback, demonstrating improved interpretability. Results indicate that our semantic-based approach, particularly the most detailed set, offers a more nuanced understanding of model decisions than traditional methods. User studies highlight a preference for our semantic explanations over traditional pixelbased heatmaps, emphasizing the benefits of human-centric interpretability in AI. This work contributes to the ongoing efforts to create XAI frameworks that align AI models behaviour with human cognitive processes, fostering trust and acceptance in critical applications.
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