Latent Fingerprint Recognition using Hybrid Ant Colony Optimization and Cuckoo Search

Latent fingerprints are adapted as prominent evidence for the identification of crime suspects from ages. The unavailability of complete minutiae information, poor quality of impressions, and overlapping of multi-impressions make the latent fingerprint recognition process a challenging task. Although the contributions in the field are efficient for determining the match, there is a requirement to ameliorate the existing techniques as false identification can put the benign behind bars. This research work has amalgamated the Cuckoo Search (CS) algorithm with Ant Colony Optimization (ACO) for the recognition of latent fingerprints. It reduces the demerits of the individual cuckoo search algorithm, such as the probability of falling into local optima, the inefficient creation of nests at the boundary due to random walk and Levy flight attributes. The positive feedback mechanism of ant colony optimization makes it easy to combine with other techniques, reducing the risk of local failure and evaluating the global best solution. Prior to the evaluation of the proposed amalgamated technique on the latent fingerprint dataset of NIST SD-27, it is tested with the benchmark functions for different shapes and physical attributes. The benchmark testing and latent fingerprint evaluation result in the betterment of the amalgamated technique over the individual cuckoo search algorithm. The state-of-the-art comparison indicates that the amalgamation technique outperformed the other fingerprint matching techniques.