A Novel Fingerprint Recognition and Verification System Using Swish Activation Based Gated Recurrent Unit and Optimal Feature Selection Mechanism

Using fingerprints in biometric systems is a rapidly expanding and pervasive field. The advancement of fingerprint identification as a computer technology for applications is directly linked to the latest developments in computer science. A kind of fingerprint identification algorithm has been made possible by artificial intelligence technology; particularly imaging technology based on deep learning. This paper proposes a novel fingerprint recognition and verification system using a Swish activation-based gated recurrent unit (SWAGRU) with an efficient feature selection mechanism. The system mainly includes four phases: preprocessing, feature extraction, feature selection, and fingerprint recognition. To begin, the fingerprint samples are collected from the publicly available FVC2004 database. After that, Gaussian filtering is applied to the collected dataset to suppress the noise. Then, the feature extraction is carried out with the help of Self-Attention-Based Visual Geometry Group-16 (SAVGG16), and from that, the optimal features are selected based on Cuckoo Search Optimization (CSO). Finally, the fingerprint recognition and verification are done using SWAGRU. The experimental results showed that the system outperformed existing methods in recognition performance

Measuring diversity of socio-cognitively inspired ACO search

In our recent research, we implemented an enhancement of Ant Colony Optimization incorporating the socio-cognitive dimension of perspective taking. Our initial results suggested that increasing the diversity of ant population - introducing different pheromones, different species and dedicated inter-species relations - yielded better results. In this paper, we explore the diversity issue by introducing novel diversity measurement strategies for ACO. Based on these strategies we compare both classic ACO and its socio-cognitive variation

Socio-cognitively inspired ant colony optimization

Recently we proposed an application of ant colony optimization (ACO) to simulate socio-cognitive features of a population, incorporating perspective-taking ability to generate differently acting ant colonies. Although our main goal was simulation, we took advantage of the fact that the quality of the constructed system was evaluated based on selected traveling salesman problem instances, and the resulting computing system became a metaheuristic, which turned out to be a promising method for solving discrete problems. In this paper, we extend the initial sets of populations driven by different perspective-taking inspirations, seeking both optimal configuration for solving a number of TSP benchmarks, at the same time constituting a tool for analyzing socio-cognitive features of the individuals involved. The proposed algorithms are compared against classic ACO, and are found to prevail in most of the benchmark functions tested

A hierarchical heterogeneous ant colony optimization based fingerprint recognition system

Personal identification is crucial to secure data against cyber-attacks. With increasing identity theft, fingerprint recognition systems have a growing importance in enforcing security and reliable identification. Although, most fingerprint recognitions systems use minutiae features for fingerprint matching, they require extensive pre-processing of fingerprints when the image quality is poor. This may introduce false ridge patterns, degrading the performance of the system. Moreover, fingerprint matching over a large database can be inefficient due to high computation time of fingerprint matching algorithms. This demand for fingerprint recognition systems that are fast and reliable. This paper proposes a computationally intelligent fingerprint recognition system that extracts ridge patterns from the fingerprint for matching. Hierarchical Heterogeneous Ant Colony Optimization based Fingerprint Matching (HHACOFM) algorithm has ant agents at different levels in the hierarchy to find a match between the input and stored ridge patterns. The algorithm was evaluated over four databases: a synthetic database generated using SFinGe tool, an internal database, SOCOFing database and FVC2004 database. Experimental results indicate that the proposal achieves high recognition rate compared to the existing approaches. HHACOFM algorithm achieves less EER than the state-of-art approaches. The results were validated using statistical tests. HHACOFM enables parallelism and thus reduces the response time. The proposal is scalable and suitable for real time applications demanding fast fingerprint verification