A Robust Spatially Invariant Model for Latent Fingerprint Authentication Approach.

Biometric fingerprints are one of the most broadly used form of biometric identification. Everyone is known to have unique, immutable fingerprints. In this area, the most challenging task is fingerprint recognition and identification system. This filed of the biometric data is significantly depends on the major quality data (input and tested images). A latent intelligent model is proposed in this paper. Our approach relies on develop an alternative approach to solve the localization problem based on Swarm Intelligence (SI) methodology for a robustness rotational and spatially invariant fingerprint recognition and verification model. In our latent model, a group of partial local features is extracted from fingerprint based on swarm-intelligence methodology such as Particle Swarm Optimization Algorithm (PSO) as a first one, and Firefly Optimized Algorithm (FOA) Algorithms as a second algorithm. The search strategy in swarm-intelligence methodology is an iteratively process which is guided by a fitness function that is been defined to maximize the class distribution separation. This methodology allows the latent model to detect and extract new features instead of using the typical model (Minutiae based feature extraction). Two main contributions have assumed in this paper to achieve the efficacity and solve the spatially invariant of the fingerprint recognition problems. The first contribution of this paper is proposing a new formulation method for finding a new feature selection approach fashion, which is based on Discrete Wavelet Transform. This formulation feature selection solves the locality problem of the fingerprint feature selection by applying swarms separately to four sub-bands fingerprint image, that to diversify the feature selection, and to improve the recognition/identification rate in additional to speed up the feat an invariant moment matching algorithm to address the potentially misclassified features to improve the matching accuracy, and to solve the miss-sequentially features tracking for the swarm-intelligent approach. The proposed approach (PSO) and (FOA) are found to generate significant improvement for recognition results by admitted about 99.1-100% accuracy. The proposed system has been applied on large scale dataset. However, it admits accuracy that ranged (96.35-99.00%), when it has been applied for fingerprints with a significant rotation issues of 0°-360°.