Iris Image Error Correction Techniques
J. Jenkin Winston, Prashanthi, Olive Teresa, Shruti Sekar, Sarah · 2019
In today's world, security has gotten paramount. Many biometric methods like facial expression recognition system and Iris recognition system have been developed. Iris biometry helps in identifying an individual in a more intuitive and natural manner. Iris recognition focuses on recognizing the identity of individuals using the textural based characteristics. But, due to various reasons corruption of texture features of iris often occurs during denoising and deblurring. In this paper we propose maltlab algorithms to overcome these defects as well as extract the features to receive a defect less image. To verify the algorithm white noise is added to the iris dataset and the calculations are done as required. This process shows that denoising and deblurring can improve the quality of the iris image evidently.