Optimized Algorithms Based Iris Recognition System

Amanpreet Kaur, Anujpreet Singh

Abstract


Human Being’s eyes are recognized as the most efficient biometric trait. Because of its great accuracy, reliability as well as originality iris recognition is included in numerous fields associated with access control as well as security. For efficient working of the iris recognition system the researchers have to face many challenges in designing an effective system. In this article the author discusses the new way of individual identification depending upon the iris recognition developed by many researchers. It includes various techniques used by them and their effectiveness in iris recognition process.


Keywords


Artificial neural, Gabor filter, Genetic algorithm, K-Mean clustering, Log Gabor filter, Network

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References


John D. How iris recognition works. Transactions on Circuits and Systems for Video Technology 2004; 14(1): 21-30.

Araghi LF, Shahhosseini H, Setoudeh F. IRIS recognition using neural network. International Multiconference of Engineers and Computer Scientists, Hong Kong. 2010; 1.

Sibai FN, Hosani HI, Naqbi IM, et al. Iris recognition using artificial neural networks. Expert Systems with Applications 2011; 38(5): 5940-6.

Raja SV, Rajagopalan SP. IRIS recognition system using neural network and Genetic Algorithm. International Journal of Computer Applications 2013; 68(20): 49-53.

Galbally J, Ross A, Gomez-Barrero M, et al. Iris image reconstruction from binary templates: An efficient probabilistic approach based on genetic algorithms. Computer Vision and Image Understanding 2013; 117(10): 1512-25.

Rai H, Yadav A. Iris recognition using combined support vector machine and Hamming distance approach. Expert Systems with Applications 2014; 41(2): 588-93.

De Marsico M, Galdi C, Nappi M, et al. FIRME: Face and iris recognition for mobile engagement. Image and Vision Computing 2014; 32(12): 1161-72.

Raja KB, Raghavendra R, Vemuri VK, et al. Smartphone based visible iris recognition using deep sparse filtering. Pattern Recognition Letters 2015; 57: 33-42.

Sánchez D, Melin P, Castillo O. Optimization of modular granular neural networks using a hierarchical genetic algorithm based on the database complexity applied to human recognition. Information Sciences 2015; 309: 73-101.

Bhateja AK, Sharma S, Chaudhury S, et al. Iris recognition based on sparse0 representation and k-nearest subspace with genetic algorithm. Pattern Recognition Letters 2016; 73: 13-8.

Ibrahim AA, Khalaf TA, Ahmed BM. Design and implementation of iris pattern recognition using wireless network system. Journal of Computer and Communications 2016; 4(7): 15.

Kaur R, Kaur P. Optimizing the genetic algorithm to resynthesize iris patterns from iris Code Template. International Journal of Current Engineering and Technology 2016; 6.

Canny J. A computational approach to edge detection. Transactions on Pattern Analysis and Machine Intelligence 1986; 8(6): 679-86.

Chen Y, Adjouadi M, Han C, et al. A highly accurate

and computationally efficient approach for unconstrained iris segmentation. Image and Vision Computing 2010; 28(2): 261-9.

Fatt Ng RY, Tay YH, Mok KM. A review of iris recognition algorithms. Transactions Information Technology 2008; 2: 1-7.

Sahmoud SA, Abuhaiba IS. Efficient iris segmentation method in unconstrained environments. Pattern Recognition Letters 2013; 46(12): 3174-85.

Rankin DM, Scotney BW, Morrow PJ, et al. Iris recognition failure over time: the effects of texture. Pattern Recognition Letters 2012; 45(1): 145-50.

Chen R, Lin X, Ding T. Liveness detection for iris recognition using multispectral images. Pattern Recognition Letters 2012; 33(12): 1514-9.

Zuo J, Schmid N, Chen X. On generation and analysis of synthetic iris images. Transactions on Information Forensics and Security 2007; 2(1): 77-90.

Cui J, Wang Y, Huang J, et al. An iris image synthesis method based on PCA and super-resolution. International Conference on Pattern Recognition, UK. 2004. DOI: 10.1109/ICPR.2004.1333804


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