Lukas Wirkestrand present his master’s thesis Training Deep Neural Network Embeddings for Contactless Palmprint Recognition Wednesday 5 June at 9:00 in MH:309A

Abstract:
This study delves into the potential of leveraging contactless palmprints within large-scale biometric frameworks, focusing on improving candidate narrowing through an encoder-based approach. Utilizing deep neural networks and trained via semi-hard triplet learning, the research transforms palm images into distinctive feature vectors for precise identification and candidate selection. Comprehensive analysis involving various architectures, datasets, and preprocessing techniques achieved a closed-set rank 10 retrieval rate of 99.4% on the HandID and Tongji datasets. Additionally, the Average Number of Hands (ANH) metric was introduced for model comparison, revealing that Model 62 outperformed others across multiple tests. Although the models are not yet sufficient as standalone end-to-end classifiers, they exhibit strong potential when combined with additional classifiers. Comparisons to previous studies underscore the promising performance of palmprint biometrics, highlighting their potential in specific domains like access security and payment.