International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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K-Means Clustering with Deep Learning for Fingerprint Class Type Prediction

  • Mukoya, Esther (School of Computing, Jomo Kenyatta University of Agriculture and Technology) ;
  • Rimiru, Richard (School of Computing, Jomo Kenyatta University of Agriculture and Technology) ;
  • Kimwele, Michael (School of Computing, Jomo Kenyatta University of Agriculture and Technology) ;
  • Mashava, Destine (Pan African University institute for basic sciences Technology and innovation (PAUSTI))
  • Received : 2022.03.05
  • Published : 2022.03.30
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Abstract

In deep learning classification tasks, most models frequently assume that all labels are available for the training datasets. As such strategies to learn new concepts from unlabeled datasets are scarce. In fingerprint classification tasks, most of the fingerprint datasets are labelled using the subject/individual and fingerprint datasets labelled with finger type classes are scarce. In this paper, authors have developed approaches of classifying fingerprint images using the majorly known fingerprint classes. Our study provides a flexible method to learn new classes of fingerprints. Our classifier model combines both the clustering technique and use of deep learning to cluster and hence label the fingerprint images into appropriate classes. The K means clustering strategy explores the label uncertainty and high-density regions from unlabeled data to be clustered. Using similarity index, five clusters are created. Deep learning is then used to train a model using a publicly known fingerprint dataset with known finger class types. A prediction technique is then employed to predict the classes of the clusters from the trained model. Our proposed model is better and has less computational costs in learning new classes and hence significantly saving on labelling costs of fingerprint images.

Keywords

Acknowledgement

The authors of this paper would like to acknowledge the public hospital that allowed the researcher to collect data from the children visiting the clinics. We also thank the parents and caregivers of children who gave consent on behalf of their children to help achieve the goal of this research.

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