面向印度多元人口群体的人脸识别模型调研：基于LFW与自定义数据集的对比分析
Surveying Facial Recognition Models for Diverse Indian Demographics: A Comparative Analysis on LFW and Custom Dataset
AI总结：
针对人脸识别在印度多元种族群体中有效性研究不足的问题，本文新建反映印度种族多样性的JFAD数据集，结合LFW数据集评估传统与深度学习人脸识别模型，得出模型适应结论并提出改进方向，为相关研究提供资源与参考。
摘要：
人脸识别技术已取得显著进展，但其在不同种族背景（尤其是特定印度人口群体）中的有效性仍缺乏充分研究。本文利用成熟的LFW数据集与我们新建的IITJ Faces of Academia Dataset（JFAD，学术界人脸数据集），对传统及基于深度学习的人脸识别模型开展详细评估；该数据集收录了印度理工学院焦特布尔分校学生的图像，旨在反映印度的种族多样性，为聚焦学术场景的模型性能评估提供关键测试基准。我们分析了从Eigenfaces（特征脸）、SIFT（尺度不变特征变换）等整体方法，到将CNN（卷积神经网络）与Gabor滤波器、拉普拉斯变换、分割技术相结合的先进混合模型。研究结果揭示了模型对印度人口群体内部种族差异的适应能力相关重要结论，并提出改进方案以提升实际应用中的准确率与包容性。JFAD不仅为后续研究提供了宝贵资源，也凸显了开发能在多元人群中公平运行的人脸识别系统的必要性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
