Fingerprint Feature Extraction Using Convolution and Particle Swarm Optimization Algorithms

Most of the existing fingerprint extraction systems are based on the global features and detailed characteristics of fingerprints, which have a weak performance in cases of poor quality fingerprint images, such as the fingerprint image is incomplete. In order to improve recognition accuracy, reliability and quickness to identify the fingerprints a new trend has been opened by using swarm intelligence techniques in biometric field. Therefore, particle swarm optimization techniques (PSO) are used in this paper to build fingerprints authentication system. A fast fingerprint identification method based on the convolution transformation and Particle Swarm Optimization algorithms proposed. The convolution algorithm was used to extract the convolved feature and then found the optimal solution from this feature by using Particle Swarm Optimization algorithm. Experimental results show that, the proposed method has a high efficiency in extracting features from fingerprints, strong strength, and good accuracy for recognition.