Fingerprint Image Segmentation using Deep Features and SVM
Reji C. Joy1, S. Hemalatha2
1Reji C Joy, Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore, India.
2S Hemalatha, Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore, India.
Manuscript received on 21 March 2019 | Revised Manuscript received on 26 March 2019 | Manuscript published on 30 July 2019 | PP: 1633-1638 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2371078219/19©BEIESP | DOI: 10.35940/ijrte.B2371.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The task of fingerprint segmentation is the most important step in an automated fingerprint identification system. It is essential to separate the fingerprint foreground with ridge and valley structure from the background, which usually contains unwanted data hindering an accurate feature extraction. In the proposed method, fingerprint segmentation is treated as a classification problem by classifying the given input image into foreground class or background class. Here, we have used an unsupervised learning algorithm by using Stacked Sparse Autoencoder (SSAE) to learn the deep features which can very well distinguish the background region from foreground one. Finally, these deep features are given to the SVM classifier. The experimental results prove that the proposed method meets the state-of-the-art results in a wide range of applications.
Index Terms: Autoencoder, Fingerprint, Morphology, Segmentation.
Scope of the Article: Deep Learning