Abstract
The use of biometric features for the surveillance and recognition of certain classes, such as gender, age, and race, is widespread and popular among researchers. Various studies have focused on gender recognition using facial, gait, or audial features. This study aimed to recognize people's gender by analyzing their hand images using a deep learning model. Before training, the images were subjected to several preprocessing stages. In the first stage, the joint points on either side of the hand were detected using the MediaPipe framework. Using the detected points, the orientation of the hands was corrected and rotated so that the fingers pointed upwards. In the last preprocessing stage, the images were smoothened while the edges were preserved by a guided filter. The processed images were used to train and test different versions of the ResNet model. The results were compared with those of some other studies on the same dataset. The proposed method achieved 96.67% recognition accuracy.
Published Version
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