A Fuzzy based Classification Approach for Efficient Fake and Real Fingerprint Classification with Intelligent Feature Selection

Fake and real fingerprint classification has become an attractive research area in the last decade. A number of research works have been carried out to classify fake and real fingerprints. But, most of the existing techniques did not utilize swarm intelligence techniques in their fingerprint classification system. Swarm intelligence has been widely used in various applications due to its robustness and potential in solving a complex optimization problem. This paper aims to develop a new and efficient fingerprint classification approach which overcomes the limitations of the existing classification approaches based on swarm intelligence and fuzzy based neural network techniques. The proposed classification methodology comprises of four steps, namely image preprocessing, feature extraction, feature selection and classification. This work uses efficient min-max normalization and median filtering for preprocessing, and multiple static features are extracted from Gabor filtering. Then, from the multiple static features obtained from 2D Gabor filtering, best features are selected using Artificial Bee Colony (ABC) optimization based on certain fitness values. This optimization based feature selection selects only the optimal set of features which is used for classification. This would lessen the complexity and the time taken by the classifier. This approach uses Fuzzy Feed Forward Neural Network (FFFNN) for classification and its performance is compared with the SVM classifier. The performance and evaluations is performed for real and fake fingerprint images obtained from LivDet2015 database. It shows that proposed work provides better results in terms of sensitivity, specificity, and precision and classification accuracy. Key-Words: Fake and real Fingerprint classification, multiple static features, normalization, median filtering, Gabor filtering, Artificial Bee Colony (ABC) optimization, Fuzzy Feed Forward Neural network (FFFNN)