论文标题
基于掌刻和掌上手掌图像的性别和种族分类来自不受控制的环境
Gender and Ethnicity Classification based on Palmprint and Palmar Hand Images from Uncontrolled Environment
论文作者
论文摘要
性别，种族或年龄等软生物特征属性可能会为生物识别技术和取证应用提供有用的信息。研究人员使用了例如面部，步态，虹膜和手等来对此类属性进行分类。即使对生物识别识别的手已经广泛研究，但从手上对软生物识别技术的关注相对较少。先前对基于手图像的软生物识别技术的研究集中在性别和控制良好的成像环境上。在本文中，考虑了不受控制的环境中的性别和种族分类。收集性别和种族标签并为公开数据库中的主题提供，其中包含来自Internet的手动图像。基于手掌的性别和种族分类方案，对五个深度学习模型进行了微调和评估。实验结果表明，对于不受控制的环境中的性别和种族分类，完整和分段的手图像比棕榈印刷图像更合适。
Soft biometric attributes such as gender, ethnicity or age may provide useful information for biometrics and forensics applications. Researchers used, e.g., face, gait, iris, and hand, etc. to classify such attributes. Even though hand has been widely studied for biometric recognition, relatively less attention has been given to soft biometrics from hand. Previous studies of soft biometrics based on hand images focused on gender and well-controlled imaging environment. In this paper, the gender and ethnicity classification in uncontrolled environment are considered. Gender and ethnicity labels are collected and provided for subjects in a publicly available database, which contains hand images from the Internet. Five deep learning models are fine-tuned and evaluated in gender and ethnicity classification scenarios based on palmar 1) full hand, 2) segmented hand and 3) palmprint images. The experimental results indicate that for gender and ethnicity classification in uncontrolled environment, full and segmented hand images are more suitable than palmprint images.
