Journal of Scientific and Industrial Research

Open Access Open Access  Restricted Access Subscription Access

A Hybrid Classification Approach for Iris Recognition System for Security of Industrial Applications


Affiliations
1 Dept. of Electronics and Communication Engg., University College of Engineering, Osmania University, Hyderabad 500 007, India
2 Dept. of Electronics & Communication Engg., Chaithanya Bharathi Institute of Technology, Osmania University, Hyderabad 500 075, India
 

The biometric authentication system is demanded to identify a particular person from the set of persons. Even though many biometric authentication methods are available such as fingerprint, palm, face, and iris, the iris-based recognition system is effective due to its simplified process. This article proposes an iris recognition system using a hybrid classification approach for security applications. The proposed method includes three modules: preprocessing, augmentation, and classifier. The preprocessing module converts the color iris images into grey scale images and also resizes the image into 256 × 256. The preprocessed iris images are now data augmented to construct the larger dataset. The data augmented images are classified into either genuine or imposter images using a hybrid classification approach. The hybrid classification approach functions in two modes as training and testing. In this article, the Convolutional Neural Networks (CNN) is integrated with the Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier to enhance the recognition rate of the iris recognition system. The performance analysis of the proposed approach is shown in terms of sensitivity, accuracy, recognition rate, specificity, false-positive rate, and false-negative rate. The experimental results of the proposed iris recognition system stated in this article significantly outweigh other design methods.

Keywords

ANFIS, CNN, Data Augmentation, Feature Map, Genuine, Imposter.
User
Notifications
Font Size

  • Li X, Wang K, Shen J, Saru K, Fan W & Yonghua H,An enhanced biometrics-based user authentication scheme for multi-server environments in critical systems, J Ambient Intell Humaniz Comput 7 (2016) 427–443.

  • Srivastava V, Tripathi B K & Pathak V K, Biometric recognition by hybridization of evolutionary fuzzy clustering with functional neural networks, J Ambient Intell Humaniz Comput, 5(4) (2014) 525–537.

  • Sallam A, Amery H A, Al-Qudasi S, Al-Ghorbani S, RassemT H & Makbol N M, Iris recognition system using convolutional neural network, Int Conf Softw Eng Comput Syst 4th Int Conf Comput Sci Info Manag (ICSECS-ICOCSIM) (Virtual mode) 24th – 26th Aug, 2021, 109–114.

  • Aro T, Jibrin M, Matiluko O, Abdulkadir I & Oluwaseyi I, Dual feature extraction techniques for iris recognition system, Int J Softw Eng Comput Syst, 5(1) (2019) 1–15.

  • Minaee S, Abdolrashidiy A & Wang Y, An experimental study of deep convolutional features for iris recognition, IEEE Signal Process Med Biol Symp (SPMB) (2016) 1–6.

  • Zanlorensi L A, Luz E, Laroca R, Britto A S, Oliveira L S & Menotti D, The impact of preprocessing on deep representations for iris recognition on unconstrained environments, 31st,/sup> SIBGRAPI Conf Graphics Patterns Images (SIBGRAPI)( Parana, Brazil) 29th Oct – 1st Nov 2018, 289–296.

  • Nguyen K, Fookes C, Ross A & Sridharan S, Iris recognition with off-the-shelf CNN features: A deep learning perspective, IEEE Access, 6 (2017) 18848–18855.

  • Liu M, Zhou Z, Shang P & Xu D, Fuzzified image enhancement for deep learning in iris recognition, IEEE Trans Fuzzy Syst 28(1) (2019) 92–99.

  • Chuang C W & Fan C P, Deep-learning based joint iris and sclera recognition with yolo network for identity identification, J Adv Inf Technol, 12(1) (2021) 60–65.

  • Al-Shoukry S, Rassem T H & Makbol N M, Alzheimer’s diseases detection by using deep learning algorithms: a mini-review, IEEE Access, 8 (2020) 77131–77141.

  • Ghosh U B, Sharma R & Kesharwani A, Symptoms-based biometric pattern detection and recognition, augmented intelligence in healthcare: A pragmatic and integrated analysis, in Studies in Computational Intelligence (Springer) 2022, 371–399.

  • Jayanthi J, Lydia E L, Krishnaraj N, Jayasankar T, Babu R L & Suji R, An effective deep learning features based integrated framework for iris detection and recognition. J Ambient Intell Humaniz Comput, 12 (2021) 3271–3281.

  • Galla D K K, Mukamalla B R & Chegireddy R P R, Support vector machine based feature extraction for gender recognition from objects using lasso classifier, J Big Data, 7(1) (2020) 1–16.

  • Lin Y N, Hsieh T Y & Huang J J, Fast Iris localization using HAAR-like features and AdaBoost algorithm, Multimed Tools Appl, 79 (2020) 34339–34362.

  • Chaturvedi R & Thakur Y S, Iris Recognition using Daugman’s Algorithm and ANN, Int J Appl Eng Res, 14(21) (2019) 3987–3995.

  • Hassanzadeh Y, Jafari-Bavil-Olyaei A, Aalami M T & Kardan N, Experimental and numerical investigation of bridge pier scour estimation using ANFIS and teaching–learning-based optimization methods, Eng Comput, 35 (2019) 1103–1120.

  • https://www.kaggle.com/datasets/naureenmohammad/mmu-iris-dataset (13/03/2022)


Abstract Views: 83

PDF Views: 73




  • A Hybrid Classification Approach for Iris Recognition System for Security of Industrial Applications

Abstract Views: 83  |  PDF Views: 73

Authors

P Jyothi
Dept. of Electronics and Communication Engg., University College of Engineering, Osmania University, Hyderabad 500 007, India
D Krishna Reddy
Dept. of Electronics & Communication Engg., Chaithanya Bharathi Institute of Technology, Osmania University, Hyderabad 500 075, India
P Naveen Kumar
Dept. of Electronics and Communication Engg., University College of Engineering, Osmania University, Hyderabad 500 007, India

Abstract


The biometric authentication system is demanded to identify a particular person from the set of persons. Even though many biometric authentication methods are available such as fingerprint, palm, face, and iris, the iris-based recognition system is effective due to its simplified process. This article proposes an iris recognition system using a hybrid classification approach for security applications. The proposed method includes three modules: preprocessing, augmentation, and classifier. The preprocessing module converts the color iris images into grey scale images and also resizes the image into 256 × 256. The preprocessed iris images are now data augmented to construct the larger dataset. The data augmented images are classified into either genuine or imposter images using a hybrid classification approach. The hybrid classification approach functions in two modes as training and testing. In this article, the Convolutional Neural Networks (CNN) is integrated with the Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier to enhance the recognition rate of the iris recognition system. The performance analysis of the proposed approach is shown in terms of sensitivity, accuracy, recognition rate, specificity, false-positive rate, and false-negative rate. The experimental results of the proposed iris recognition system stated in this article significantly outweigh other design methods.

Keywords


ANFIS, CNN, Data Augmentation, Feature Map, Genuine, Imposter.

References