2026-10-23T17:47:54Zhttps://gumushane-rdlopen.rawdatalibrary.net/oai/request

oai:gumushane-rdlopen.rawdatalibrary.net:gumushane/162312026-10-22T11:38:28Zopenairedrivercom_gumushane_10000col_gumushane_10001com_gumushane_10007col_gumushane_10171

Cross-view Gait Recognition Based on Fine-Tuned Deep NetworksYaprak, BusranurGedikli, EyupBingöl, OzkanDogan, Ramazan OzgurIEEEcross-view gait recognitiondeep CNN networksGEISilhouetteVGG-16Gait 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.IEEE2024Conference Objecthttps://rdlopen.rawdatalibrary.net/r/gumushane/16231https://doi.org/10.1109/siu61531.2024.10600941979-8-3503-8897-8979-8-3503-8896-12165-0608tr32ND IEEE SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE, SIU 2024info:eu-repo/semantics/openAccessinfo:eu-repo/semantics/conferenceObjecthttps://gumushane-rdlopen.rawdatalibrary.net/tr/items/gumushane/16231