Finger Vein Recognition with Hybrid Deep Learning Approach

Finger vein biometrics is an identification technique based on the patterns in fingers, and it has benefit of being difficult to counterfeit. Due its high level security, durability, performance history, finger recognition captures our attention as one most significant authentication methods available today. Using a mixed deep learning approach, we investigate challenge identifying sensor model. Thus far, use Traditional LSTM architectures for this biometric modality. This work also suggests brand-new hybrid architecture that shines due compactness merging with LSMT layer be taught. In experiment, original samples well region interest data from eight freely FV-USM datasets are employed. The standard LSTM-based strategy preferable produced better outcomes, seen by comparison earlier approaches. Moreover, results show CNN networks may used improve detection performance.