Fingerprint Recognition using Combined Feature Vector

Biometrics offers greater security and convenience than traditional methods of personal recognition. In some applications, biometrics can replace or supplement the existing technology. A biometric system is essentially a pattern-recognition system that recognizes a person based on a feature vector derived from a specific physiological or behavioral characteristic that the person possesses. Fingerprint recognition has been successfully used in law enforcement and forensics to identify suspects and victims for over a century. The probability of fingerprints two be same are 1 in 1.9*10¹⁵. In this paper, Fingerprint biometric system is considered to develop a recognition model. Various texture features are extracted using Local Binary Pattern (LBP), Local Gabor XOR Pattern, Gray Level Co-occurrence matrix, Gabor features to form a combined feature vector in order to train the Neural Network using Bat algorithm. The performance of recognition model is analyzed by using sample database.