Enhanced Iris Recognition for Person Identification
Sherin Antonny1, Ashwini P2, Kanchana V3
1Sherin Antonny: Department of Computer Science, Amrita Vishwa Vidyapeetham, Amrita School of Arts and Sciences, Mysuru, India.
2Ashwini P: Department of Computer Science, Amrita Vishwa Vidyapeetham, Amrita School of Arts and Sciences, Mysuru, India.
3Kanchana V: Department of Computer Science, Amrita Vishwa Vidyapeetham, Amrita School of Arts and Sciences, Mysuru, India.
Manuscript received on November 20, 2019. | Revised Manuscript received on November 28, 2019. | Manuscript published on 30 November, 2019. | PP: 7400-7405 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9502118419/2019©BEIESP | DOI: 10.35940/ijrte.D9502.118419
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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: Iris recognition is a secure biometric for personal identification. Commonly used biometric are voice, face, fingerprint, iris etc. Among this iris recognition is considered as more accurate, because iris is externally visible and the texture patterns are unique and stable throughout a person’s whole life. The main steps involved in iris recognition are pre-processing, feature extraction and feature matching. Unique preprocessing methods are mentioned in this work. The feature extraction phase is imperative in iris recognition task. Here in this work feature extraction utilizing Discrete Wavelet Transform (DWT) and matching of iris pictures utilizing Euclidean distance.
Keywords: Unsharp Mask, Adaptive Threshold, Gradient Magnitude, Canny Edge Detection, DWT, Euclidean Distance.
Scope of the Article: Pattern Recognition and Analysis.