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The objective of this research is to design an efficient algorithm that can successfully enhance a targeted latent fingerprint from various complex backgrounds under an uncontrolled environment. Most algorithms in literature exploited dictionary learning schemes and deep learning architectures to capture latent fingerprints from complicated backgrounds and noise. However, an algorithm learned from other high-quality fingerprint images may not solve all possible cases within a given unseen image. We propose a new feedback framework to distinguish latent fingerprints from complex backgrounds and gradually improve friction-ridge quality using the information provided inside the given unseen image. We combine two efficient mechanisms. The first mechanism enhances high-quality areas in priority and feeds the enhanced areas back to improve the quality of latent fingerprints in the nearby area. The second mechanism is to verify that the first mechanism works correctly by detecting anomalously enhanced fingerprint patterns. The second mechanism employs a spectral autoencoder that learns from good fingerprint spectra in the frequency domain. The anomalous fingerprint area is sent back to the first mechanism for further improving the enhanced result. We benchmark the proposed algorithm against available state-of-the-art algorithms using two fingerprint matching systems (one commercial off-the-shelf and one open-source) on two public latent fingerprint databases. The experimental results show that the proposed algorithm outperforms most state-of-the-art algorithms in the literature.

Progressive and Corrective Feedback for Latent Fingerprint Enhancement Using Boosted Spectral Filtering and Spectral Autoencoder