Gender and Ethnicity Classification based on Palmprint and Palmar Hand Images from Uncontrolled Environment

TL;DR

This study explores gender and ethnicity classification from palm and hand images captured in uncontrolled environments, using deep learning models on a new public dataset, highlighting the effectiveness of full and segmented hand images over palmprints.

Contribution

It introduces a new public dataset with hand images from the internet and evaluates deep learning models for soft biometric classification in uncontrolled settings.

Findings

Full and segmented hand images outperform palmprints for classification.

Deep learning models achieve high accuracy in uncontrolled environments.

Hand images from the internet can be effectively used for soft biometric recognition.

Abstract

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.