A CLAHE and Gaussian Filter Based Framework for Enhancement of Latent Fingerprints

Biometric identification systems face difficulties because latent fingerprint images contain deficient qualities due to background clutter and noise in addition to inferior contrast. Latent fingerprint images require preprocessing to increase their usability through sequential processes of enhancing ridge clarity and background noise suppression. Such images often generate unsuccessful results in essential tasks involving feature extraction and matching. This research work develops a preprocessing strategy utilizing contrast-limited adaptive Histogram Equalization (CLAHE) and Gaussian filters, which enhances the visual and Signal-to-Noise Ratio (SNR) qualities of latent fingerprint images. Enhancements of image clarity and SNR levels have been confirmed through experimental results, which increase the reliability of fingerprint recognition systems. Local contrast becomes more apparent through CLAHE application and Gaussian filtering produces a denoising effect on unwanted high-frequency artifacts. The selected preprocessing techniques provide both an efficient and impactful solution that operates effectively as a base for following sophisticated fingerprint detection methods. SNR value assessments together with visual examination show that the method delivers beneficial outcomes.