Gender and Ethnicity Classification based on Palmprint and Palmar Hand Images from Uncontrolled Environment
This work addresses a practical problem for biometrics and forensics applications by extending soft biometric classification to less controlled settings, though it is incremental as it builds on existing deep learning methods.
The paper tackled gender and ethnicity classification from hand images in uncontrolled environments, finding that full and segmented hand images outperformed palmprint images, with specific accuracy improvements reported (e.g., up to 95% for gender and 85% for ethnicity on certain datasets).
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.