2026-10-24T15:30:16Zhttps://gumushane-rdlopen.rawdatalibrary.net/oai/request

oai:gumushane-rdlopen.rawdatalibrary.net:gumushane/114732026-10-22T11:34:26Zopenairecom_gumushane_10000col_gumushane_10001com_gumushane_10007col_gumushane_10171

High Performance Automatic ROI Extraction from Hand Images with Convolutional Neural NetworksKosmaz Sunnetci, BetulBingol, OzkanGedikli, EyupEkinci, Muratbiometric recognitiondeep learningpalmprintROI localizationAutomatic ROI extraction from hand images is a critical step in palmprint recognition. Traditional methods involve steps such as identifying the region of the hand, finding the valleys between the fingers and marking the palm area. Some of these processes are performed manually and some are performed with image processing techniques. However, these methods are time-consuming, vulnerable to human error, and computationally redundant. In this study, a deep learning-based convolutional neural network is developed for high-performance automatic ROI extraction from hand images. The proposed method eliminates the hand segmentation process and provides a solution where the palm region can be automatically selected directly. This reduces processing time, minimizes human error and improves overall accuracy.Institute of Electrical and Electronics Engineers Inc.2024Conference Objecthttps://rdlopen.rawdatalibrary.net/r/gumushane/11473https://doi.org/10.1109/idap64064.2024.10710885979-833153149-2en8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024info:eu-repo/semantics/closedAccessinfo:eu-repo/semantics/conferenceObjecthttps://gumushane-rdlopen.rawdatalibrary.net/tr/items/gumushane/11473