An Adaptively Automated Five-Class Fingerprint Classification Scheme Using Kohonens Feature Map and fuzzy ant clustering by centroid positioning

In this paper we present a novel adaptively automated fingerprint classification scheme, which is computationally efficient and resolves both intra- class diversities and inter-class similarities. Initially, preprocessing of fingerprint images is carried out to enhance the image. As part of preprocessing scheme the denoising algorithm used endows better performance of system even in case of bad quality image. Directional image is computed to classify based on global shape. Principal component analysis is employed for dimensionality reduction and to get feature space that accounts for as much of the total variation as possible. Self-organizing maps are involved for further dimension reduction and data clustering. The learning process takes into account the large intra class diversity and the continuum of fingerprint pattern types. Finally a swarm intelligence based maps the class separated fingerprint images into their respective class resolving the inter-class similarities. The proposed approach achieves an accuracy of around 93% for five-class classification tested on NIST 4 without rejection.