An Improved Low Contrast Image in Normalization Process for Iris Recognition System

Iris recognition system is one of the most predominant methods used for personal identification in the modern days. Low quality iris image such as low contrast and poor illumination presents a setback for iris recognition as the acceptance or rejection rates of verified user depend solely on the image quality. This paper presents a new method for improving histogram equalization technique to obtained high contrast in normalization process thereby reducing False Rejection Rate (FRR) and False Acceptance Rate (FAR). The proposed technique is developed using C++ and tested using four datasets CASIA, UBIRIS, MMU and ICE 2005. The experimental results show that the proposed technique has an accuracy of 95%, as compared to the existing techniques: CLAHE, AHE, MAHE and HE which have an accuracy of a 93.0, 85.7, 92.8 and 90.71% respectively. Hence it can be concluded that the proposed technique is a better enhancement technique compared to the existing techniques for image enhancement.

[1]  Nadia Othman.  Fusion techniques for iris recognition in degraded sequences. (Techniques de fusion pour la reconnaissance de personne par l'iris dans des séquences dégradées) , 2016 .

[2]  S. Sanderson,et al.  Authentication for secure environments based on iris scanning technology , 2000 .

[3]  Miguel García-Silvente,et al.  A keypoints-based feature extraction method for iris recognition under variable image quality conditions , 2016, Knowl. Based Syst..

[4]  Richa Singh,et al.  Comparison of iris recognition algorithms , 2004, International Conference on Intelligent Sensing and Information Processing, 2004. Proceedings of.

[5]  Tieniu Tan,et al.  Biometric personal identification based on iris patterns , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[6]  Zahraddeen Abubakar Pindar,et al.  Reducing Error Rates for Iris Image using higher Contrast in Normalization process , 2017 .

[7]  John Daugman,et al.  High Confidence Visual Recognition of Persons by a Test of Statistical Independence , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  R. S. D. Wahida Banu,et al.  Adaptive contrast enhancement using modified histogram equalization , 2015 .

[9]  John Daugman.  How iris recognition works , 2004 .

[10]  Peihua Li,et al.  Iris recognition in non-ideal imaging conditions , 2012, Pattern Recognit. Lett..

[11]  J. Daugman,et al.  Epigenetic randomness, complexity and singularity of human iris patterns , 2001, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[12]  M. Taib,et al.  An Analysis of Image Enhancement Techniques for Dental X-ray Image Interpretation , 2012 .

[13]  Bernadette Dorizzi,et al.  OSIRIS: An open source iris recognition software , 2016, Pattern Recognit. Lett..

[14]  Apurba Das,et al.  Image Enhancement in Spatial Domain , 2018, Understanding Digital Image Processing.

[15]  Palaiahnakote Shivakumara,et al.  Accurate video text detection through classification of low and high contrast images , 2010, Pattern Recognit..

[16]  Himanshu Aggarwal,et al.  A Comprehensive Review of Image Enhancement Techniques , 2010, ArXiv.

[17]  Dexin Zhang,et al.  Personal Identification Based on Iris Texture Analysis , 2003, IEEE Trans. Pattern Anal. Mach. Intell..