对比多种人脸识别模型在印度多元人群中的表现，验证了现有系统在多样性上的不足。
Surveying Facial Recognition Models for Diverse Indian Demographics: A Comparative Analysis on LFW and Custom Dataset
- 用LFW和自建的印度理工学院人脸数据集进行多模型对比测试
- 传统方法如Eigenfaces在印度人群上准确率普遍低于75%，深度模型表现更优
- 提出需针对印度人群优化模型，适合关注公平性与跨文化应用的研究者
人脸识别技术虽已取得显著进展，但在特定印度人群中的适用性仍研究不足。本文对传统与深度学习模型在标准LFW数据集及新构建的IITJ Faces of Academia Dataset（JFAD）上进行了评估，该数据集包含来自IIT Jodhpur的学生图像，旨在反映印度的人口多样性。研究涵盖从Eigenfaces、SIFT等整体方法，到融合CNN、Gabor滤波器、拉普拉斯变换与分割技术的混合模型。结果表明，多数模型在印度人群中表现受限，尤其传统方法准确率普遍低于75%；而结合多模态特征的深度模型展现出更强适应性。研究强调需针对性优化模型以提升真实场景下的准确率与公平性，并指出JFAD可作为未来研究的重要资源。
原文摘要 · Abstract (English)
Facial recognition technology has made significant advances, yet its effectiveness across diverse ethnic backgrounds, particularly in specific Indian demographics, is less explored. This paper presents a detailed evaluation of both traditional and deep learning-based facial recognition models using the established LFW dataset and our newly developed IITJ Faces of Academia Dataset (JFAD), which comprises images of students from IIT Jodhpur. This unique dataset is designed to reflect the ethnic diversity of India, providing a critical test bed for assessing model performance in a focused academic environment. We analyze models ranging from holistic approaches like Eigenfaces and SIFT to advanced hybrid models that integrate CNNs with Gabor filters, Laplacian transforms, and segmentation techniques. Our findings reveal significant insights into the models' ability to adapt to the ethnic variability within Indian demographics and suggest modifications to enhance accuracy and inclusivity in real-world applications. The JFAD not only serves as a valuable resource for further research but also highlights the need for developing facial recognition systems that perform equitably across diverse populations.
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