Facial Hair Area in Face Recognition Across Demographics: Small Size, Big Effect.

Haiyu Wu, Sicong Tian, Aman Bhatta, Kagan Öztürk, Karl Ricanek, Kevin W. Bowyer

IEEE/CVF Winter Conference on Applications of Computer Vision(2024)

引用 0|浏览5
暂无评分
摘要
Observed variations in face recognition accuracy across demographics, often viewed as “bias”, have motivated re-search into the causes of such variations. Variations in facial hairstyle are an important potential cause of accu-racy differences for males. In this work, we first explore how face recognition accuracy is affected by the facial hair region - clean-shaven, mustache, chin-area beard, side-to-side beard. Results show that mustache area facial hair has a greater effect on accuracy than either chin-area beard or side-to-side beard. We then employ a synthetic facial hair method to verify the consistency of the observation across five synthetic facial hair colors and three face matchers. Re-sults of these experiments indicate that, the larger the dif-ference in brightness between facial hair region and skin region, the larger impact of the mustache area. To reduce accuracy differences caused by facial hairstyle, quantified by $\Delta d^{\prime}$ , we adjust the training dataset distribution to have increased representation of facial hair, resulting in an over 40% reduction in accuracy difference.
更多
查看译文
关键词
Face Recognition,Face Area,Training Dataset,Bigotry,Facial Skin,Skin Regions,Difference In Brightness,Face Matching,Training Set,Training Data,African American,Similarity Score,Skin Color,Number Of Images,Size Of Region,Image Pairs,Generative Adversarial Networks,Variational Autoencoder,Inconsistent Patterns,Distribution Of Pixels,African American Males,Face Recognition Model,Caucasian Males,Identity Preservation
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要