[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"ssr-repo-dspace000002627":3},{"id":4,"title":5,"authors":6,"files":8,"advisors":35,"authors_detail":36,"advisors_detail":40,"publisher":41,"publish_place":42,"language":43,"type":44,"type_slug":45,"year":46,"department":47,"study_program":48,"keywords":49,"keywords_detail":55,"abstract":66,"abstract_html":67,"url":68},"repo-dspace000002627","Implementation Contactless Palmprint Recognition System",[7],"Zerlina Putri Wollwage",[9,13,16,19,23,26,29,32],{"chapter":10,"requires_login":11,"label":12,"available":11},"abstrak",false,"Abstrak",{"chapter":14,"requires_login":11,"label":15,"available":11},"bab1","Bab I",{"chapter":17,"requires_login":11,"label":18,"available":11},"bab2","Bab II",{"chapter":20,"requires_login":21,"label":22,"available":11},"bab3",true,"Bab III",{"chapter":24,"requires_login":21,"label":25,"available":11},"bab4","Bab IV",{"chapter":27,"requires_login":21,"label":28,"available":11},"bab5","Bab V",{"chapter":30,"requires_login":21,"label":31,"available":11},"full","Dokumen Lengkap",{"chapter":33,"requires_login":11,"label":34,"available":21},"open","Akses Terbuka",[],[37],{"name":7,"nim":38,"slug":39},"2141720146","zerlina-putri-wollwage",[],"Politeknik Negeri Malang","Malang","Inggris","Skripsi (D4)","skripsi-d4",2025,"D-IV TEKNIK INFORMATIKA",null,[50,51,52,53,54],"Palmprint Recognition","ResNet50","Transfer Learning","Deep Learning","Image Classification",[56,58,60,62,64],{"name":50,"slug":57},"palmprint-recognition",{"name":51,"slug":59},"resnet50",{"name":52,"slug":61},"transfer-learning",{"name":53,"slug":63},"deep-learning",{"name":54,"slug":65},"image-classification","Palmprint recognition is a promising biometric approach for personal identification due to its unique and stable features. This study presents a deep learning-based palmprint recognition system utilizing the ResNet50 convolutional neural network. A comparative analysis was conducted between two training strategies: training the model from scratch and performing fine-tuning on a pretrained model via transfer learning. The models were evaluated on a curated dataset comprising +11.200 contactless palmprint images from 30 distinct user classes. The fine-tuned ResNet50 model significantly outperformed the scratch-trained model, achieving an accuracy of 99.65% compared to 98.59%. The fine-tuned approach also demonstrated superior generalization and stability, yielding macro-averaged precision, recall, and F1-scores of 0.9979, 0.9987, and 0.9983, respectively. These results indicate a more robust performance with fewer misclassifications, particularly in challenging or less-represented classes. The research culminates in the successful deployment of the system through a real-time web-based interface, providing a complete end-to-end implementation for practical application.","\u003Cp>Palmprint recognition is a promising biometric approach for personal identification due to its unique and stable features. This study presents a deep learning-based palmprint recognition system utilizing the ResNet50 convolutional neural network. A comparative analysis was conducted between two training strategies: training the model from scratch and performing fine-tuning on a pretrained model via transfer learning. The models were evaluated on a curated dataset comprising +11.200 contactless palmprint images from 30 distinct user classes. The fine-tuned ResNet50 model significantly outperformed the scratch-trained model, achieving an accuracy of 99.65% compared to 98.59%. The fine-tuned approach also demonstrated superior generalization and stability, yielding macro-averaged precision, recall, and F1-scores of 0.9979, 0.9987, and 0.9983, respectively. These results indicate a more robust performance with fewer misclassifications, particularly in challenging or less-represented classes. The research culminates in the successful deployment of the system through a real-time web-based interface, providing a complete end-to-end implementation for practical application.\u003C\u002Fp>","https:\u002F\u002Frepository.polinema.ac.id\u002Fcollection\u002Fdetail?id=dspace000002627&year=2025&title=implementation-contactless-palmprint-recognition-system"]