OPTIMISED FINGERPRINT FEATURE REPRESENTATION FOR RELIABLE IDENTIFICATION

The fingerprint recognition has become a widely used procedure of biometric authentication but its functionality is typically affected by noise, mis-alignment and partial impression of the fingerprint making the existing feature extraction and classification scheme ineffective. In an attempt to address these issues, the research shall recommend an adaptive model of fingerprint recognition that shall incorporate the application of enhanced feature extraction and effective classification methods. Specifically, it introduces the proposed algorithm Selective Spatio-Temporal Residual Feature Framework (STRFF-Net) of discriminative spatio-temporal features extraction and Temporal Residual Flow Recognition Network (TRFRN) of the accurate fingerprint identification. STRFF-Net uses the residual flow modeling and attention to highlight salient ridge and minutiae patterns and discard the irrelevant or noisy regions to provide enriched feature representation. TRFRN uses the stream of attention-based classification model to investigate the spatial and temporal changes patterns of the fingerprints that enables reliable recognition of the prints of fingers in the event of smudged and partial fingerprints. Comparison of the experimental results with publicly available fingerprint data shows that experimental analysis with reference to feature improvement, strength of the activation as well as accuracy of classification is highly improved when compared to existing and machine-based learning techniques. The robustness and precision of the suggested framework makes it a suitable instrument in the realistic application of biometric authentication, forensic identification or application in areas of serious security concern.