Cross-view Gait Recognition Based on Fine-Tuned Deep Networks
32nd IEEE Signal Processing and Communications Applications Conference (SIU), Mersin, Turkey, 15 - 18 May 2024, (Full Text)
- Publication Type: Conference Paper / Full Text
- Doi Number: 10.1109/siu61531.2024.10600941
- City: Mersin
- Country: Turkey
- Karadeniz Technical University Affiliated: Yes
Abstract
Gait recognition is a biometrics-based computer vision process used to identify people based on their walking styles. Compared to other types of biometrics, gait offers a more advantageous recognition process as it does not require high-resolution and close-range images and obtains without contact. But besides this, gait biometrics is highly affected by cross-view variation, and under this variation recognition performance decreases significantly. In this study, performance evaluations of fine-tuned VGG-16 and ResNet-50 deep CNN networks on the cross-view gait recognition problem are performed. For this purpose, Gait energy images (GEI) and Silhouettes obtained from CASIA-B, the most comprehensive data set in gait recognition, are given as input to the networks. The experimental results showed that the VGG-16 network achieved higher recognition rates in cross-view gait recognition.