Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3684 2019 2019 Assistant Professor, Dept. Of CSE, Koneru Lakshmaiah Education Foundation, (Deemed to be University) Vaddeswaram, Guntur, A.P., India Krishna Krishna Assistant Professor, Dept. Of ECE, Anil Neerukonda Institute of Technology & Sciences, Visakhapatnam, AP, India Bylapudi Rama Devi Principal, MIT Muzaffarpur, Bihar, India Chandra Bhushan Mahato Enterprise Architect, R&D - Engineering, ThoughtSpot Inc, Franklin, TN, USA Lakshmi Chandrakanth Kasireddy Associate Professor, Department of CSE, St. Martin's Engineering College, Secunderabad, Telangana, India M. Vadivukarassi Professor, Department of Electrical and Electronics Engineering, Bannari Amman Institute of Technology, Erode, TN, India P. Sivaraman In this research, we provide a CNN-based system that can reliably identify the dorsal veins of the hand. In order to get better results on different picture quality datasets, the suggested model makes use of refined variants of the pre-trained VGG Net-16 and VGG Net-19 designs. We use the BOSPHORUS dataset, which provides medium-quality photos, in addition to two self-constructed datasets that provide good- and low-quality images. By using state-of-the-art augmenting image methods, streamlined pre-processing procedures, and meticulously designed CNN designs, the fine-tuned VGG Net-16 model achieves superior performance in comparison to all other models. Using ROI pictures with a resolution of 224×224 pixels, a multi-class technique is employed for arranging the vein patterns. Improving data quality during training makes the approach more broad, which helps prevent over fitting. On every dataset, the proposed method achieves better results than standard ML models like K-NN and SVM, and the experimental outcomes demonstrate significant improvements in accuracy. The modifying process led to a considerable decrease in the equal error rates (EER) when compared to benchmark methods. The structure enhances efficiency in computing with GPU-accelerated studying. It was built with the help of Python extensions like as OpenCV, Keras, and TensorFlow. Results from extensive testing of the proposed method show an accuracy of 99.98%, a precision of 98.98%, and a recall of 98.8%. From what we can see, the technique is both adaptable and dependable; making it well suited for use in practical biometrics vein recognition applications. 2025 2025 26 41 10.54216/JISIoT.160203 https://www.americaspg.com/articleinfo/18/show/3684