Efficient Finger Print Image Classification and Recognition using Neural Network Data Mining

This paper deals with the fingerprint classification and recognition system, which consists of extracting and matching of minutiae from the input image. The necessity for security in fields such as improving airport security, strengthening the national borders, in travel documents, in preventing ID theft has brought the need to develop an able and efficient method for correct classification of personnel authentication. Fingerprints are the first biometric science used widely for the validation and verifications of entry into specific task, which is more reliable, efficient and accurate. Despite finger print recognition being reliable it has disadvantages such as very low recognition rate, low accuracy rate, total time of recovery and data insufficiency. To address above problem a novel data mining technique, neuro nearest neighbour based fingerprint classification and recognition, is introduced which boosts the classification rate. The proposed method consists of different stages such as image enhancement, line detector based feature extraction, neural network classification using Learning vector quantization and Back propagation networks. The proposed system is trained and tested on Fingerprint Database obtained from the University of Bologna Italy, which consists of 900 samples. The exact image is recognized precisely from the classified database rather than the original set of database using Crisp K-nearest neighbour algorithm that increases recognition accuracy and reduction in time