Skip to main content
main-content

Tipp

Weitere Artikel dieser Ausgabe durch Wischen aufrufen

Erschienen in: Neural Processing Letters 4/2022

15.03.2022

Iris Recognition using Multi Objective Artificial Bee Colony Optimization Algorithm with Autoencoder Classifier

verfasst von: Sheela S V, Radhika K R

Erschienen in: Neural Processing Letters | Ausgabe 4/2022

Einloggen, um Zugang zu erhalten
share
TEILEN

Abstract

In recent decades, iris recognition is a trustworthy and important biometric model for human recognition. Criminal to commercial products, citizen confirmation and border control are few application areas. The research work is a deep learning based integrated model for accurate iris detection and recognition. Initially, eye images are considered from two datasets, the Chinese Academy of Sciences Institute of Automation (CASIA) and the Indian Institute of Technology (IIT) Delhi v1.0. Iris region is accurately segmented using Daugman’s algorithm and Circular Hough Transform (CHT). Feature extraction is hybrid that is performed using Dual Tree Complex Wavelet Transform (DTCWT), Gabor filter, Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) from the segmented iris regions. A Multiobjective Artificial Bee Colony (MABC) algorithm is proposed to eliminate noisy and redundant feature vectors by estimating consistent information. In MABC algorithm, two multi-objective functions are formulated as reduction in number of features and classification error rate. The selected active feature vectors are given as input to autoencoder classification for iris recognition. The experimental outcome shows that MABC-autoencoder model obtained 99.67% and 98.73% accuracy on CASIA-Iris, and IIT Delhi v1.0 iris datasets. Performance evaluation is based on accuracy, specificity, Critical Success Index (CSI), sensitivity, Fowlkes Mallows (FM) index, and Mathews Correlation Coefficient (MCC).
Literatur
1.
Zurück zum Zitat Raja J, Gunasekaran K, Pitchai R (2019) Prognostic evaluation of multimodal biometric traits recognition based human face, finger print and iris images using ensembled SVM classifier. Cluster Comput 22(1):215–228 CrossRef Raja J, Gunasekaran K, Pitchai R (2019) Prognostic evaluation of multimodal biometric traits recognition based human face, finger print and iris images using ensembled SVM classifier. Cluster Comput 22(1):215–228 CrossRef
2.
Zurück zum Zitat Nguyen K, Fookes C, Ross A, Sridharan S (2017) Iris recognition with off-the-shelf CNN features: A deep learning perspective. IEEE Access 6:18848–18855 CrossRef Nguyen K, Fookes C, Ross A, Sridharan S (2017) Iris recognition with off-the-shelf CNN features: A deep learning perspective. IEEE Access 6:18848–18855 CrossRef
3.
Zurück zum Zitat Wang K, Kumar A (2019) Cross-spectral iris recognition using CNN and supervised discrete hashing. Pattern Recogn 86:85–98 CrossRef Wang K, Kumar A (2019) Cross-spectral iris recognition using CNN and supervised discrete hashing. Pattern Recogn 86:85–98 CrossRef
4.
Zurück zum Zitat Liu M, Zhou Z, Shang P, Xu D (2019) Fuzzified image enhancement for deep learning in iris recognition. IEEE Trans Fuzzy Syst 28(1):92–99 CrossRef Liu M, Zhou Z, Shang P, Xu D (2019) Fuzzified image enhancement for deep learning in iris recognition. IEEE Trans Fuzzy Syst 28(1):92–99 CrossRef
5.
Zurück zum Zitat Jenadeleh M, Pedersen M, Saupe D (2020) Blind quality assessment of iris images acquired in visible light for biometric recognition, Sensors, vol.20, no.5, pp. 1308 Jenadeleh M, Pedersen M, Saupe D (2020) Blind quality assessment of iris images acquired in visible light for biometric recognition, Sensors, vol.20, no.5, pp. 1308
6.
Zurück zum Zitat Ahmadi N, Akbarizadeh G (2020) Iris tissue recognition based on GLDM feature extraction and hybrid MLPNN-ICA classifier. Neural Comput Appl 32(7):2267–2281 CrossRef Ahmadi N, Akbarizadeh G (2020) Iris tissue recognition based on GLDM feature extraction and hybrid MLPNN-ICA classifier. Neural Comput Appl 32(7):2267–2281 CrossRef
7.
Zurück zum Zitat Arsalan M, Naqvi RA, Kim DS, Nguyen PH, Owais M, Park KR (2018) IrisDenseNet: Robust iris segmentation using densely connected fully convolutional networks in the images by visible light and near-infrared light camera sensors. Sensors 18(5):1501 CrossRef Arsalan M, Naqvi RA, Kim DS, Nguyen PH, Owais M, Park KR (2018) IrisDenseNet: Robust iris segmentation using densely connected fully convolutional networks in the images by visible light and near-infrared light camera sensors. Sensors 18(5):1501 CrossRef
8.
Zurück zum Zitat Ohmaid H, Eddarouich S, Bourouhou A, Timouyas M (2020) Iris segmentation using a new unsupervised neural approach, IAES International Journal of Artificial Intelligence, vol. 9, no.1, pp. 58 Ohmaid H, Eddarouich S, Bourouhou A, Timouyas M (2020) Iris segmentation using a new unsupervised neural approach, IAES International Journal of Artificial Intelligence, vol. 9, no.1, pp. 58
9.
Zurück zum Zitat Lin YN, Hsieh TY, Huang JJ, Yang CY, Shen VR, Bui HH (2020) Fast Iris localization using Haar-like features and AdaBoost algorithm. Multimedia Tools and Applications 79(45):34339–34362 CrossRef Lin YN, Hsieh TY, Huang JJ, Yang CY, Shen VR, Bui HH (2020) Fast Iris localization using Haar-like features and AdaBoost algorithm. Multimedia Tools and Applications 79(45):34339–34362 CrossRef
10.
Zurück zum Zitat Alam MM, Khan MAR, Salehin ZU, Uddin M, Soheli SJ, Khan TZ (2020) Combined PCA-Daugman Method: An Efficient Technique for Face and Iris Recognition, Journal of Advances in Mathematics and Computer Science, pp. 34–44 Alam MM, Khan MAR, Salehin ZU, Uddin M, Soheli SJ, Khan TZ (2020) Combined PCA-Daugman Method: An Efficient Technique for Face and Iris Recognition, Journal of Advances in Mathematics and Computer Science, pp. 34–44
11.
Zurück zum Zitat Ismail S, Ali FHM, Aljunid SA (2020) Reducing intra-class variations of deformed iris recognition system, International Journal of Advanced Trends in Computer Science and Engineering, vol. 9, no.1.3 Ismail S, Ali FHM, Aljunid SA (2020) Reducing intra-class variations of deformed iris recognition system, International Journal of Advanced Trends in Computer Science and Engineering, vol. 9, no.1.3
12.
Zurück zum Zitat Dua M, Gupta R, Khari M, Crespo RG (2019) Biometric iris recognition using radial basis function neural network. Soft Comput 23(22):11801–11815 CrossRef Dua M, Gupta R, Khari M, Crespo RG (2019) Biometric iris recognition using radial basis function neural network. Soft Comput 23(22):11801–11815 CrossRef
13.
Zurück zum Zitat Nithya AA, Lakshmi C (2019) Enhancing iris recognition framework using feature selection and BPNN. Cluster Comput 22(5):12363–12372 CrossRef Nithya AA, Lakshmi C (2019) Enhancing iris recognition framework using feature selection and BPNN. Cluster Comput 22(5):12363–12372 CrossRef
14.
Zurück zum Zitat Ahmadi N, Nilashi M, Samad S, Rashid TA, Ahmadi H (2019) An intelligent method for iris recognition using supervised machine learning techniques, Optics & Laser Technology, vol. 120, pp. 105701 Ahmadi N, Nilashi M, Samad S, Rashid TA, Ahmadi H (2019) An intelligent method for iris recognition using supervised machine learning techniques, Optics & Laser Technology, vol. 120, pp. 105701
15.
Zurück zum Zitat Ahmadi N, Akbarizadeh G (2018) Hybrid robust iris recognition approach using iris image pre-processing, two‐dimensional Gabor features and multi‐layer perceptron neural network/PSO. IET Biom 7(2):153–162 CrossRef Ahmadi N, Akbarizadeh G (2018) Hybrid robust iris recognition approach using iris image pre-processing, two‐dimensional Gabor features and multi‐layer perceptron neural network/PSO. IET Biom 7(2):153–162 CrossRef
16.
Zurück zum Zitat Adamović S, Miškovic V, Maček N, Milosavljević M, Šarac M, Saračević M, Gnjatović M (2020) An efficient novel approach for iris recognition based on stylometric features and machine learning techniques. Future Generation Computer Systems 107:144–157 CrossRef Adamović S, Miškovic V, Maček N, Milosavljević M, Šarac M, Saračević M, Gnjatović M (2020) An efficient novel approach for iris recognition based on stylometric features and machine learning techniques. Future Generation Computer Systems 107:144–157 CrossRef
17.
Zurück zum Zitat Shuai L, Yuanning L, Xiaodong Z, Guang H, Jingwei C, Qixian Z, Zukang W, Xinlong L, Chaoqun W (2020) Multi-source feature fusion and entropy feature lightweight neural network for constrained multi-state heterogeneous iris recognition. IEEE Access 8:53321–53345 CrossRef Shuai L, Yuanning L, Xiaodong Z, Guang H, Jingwei C, Qixian Z, Zukang W, Xinlong L, Chaoqun W (2020) Multi-source feature fusion and entropy feature lightweight neural network for constrained multi-state heterogeneous iris recognition. IEEE Access 8:53321–53345 CrossRef
18.
Zurück zum Zitat Chen Y, Wu C, Wang Y (2020) T-center: A novel feature extraction approach towards large-scale iris recognition. IEEE Access 8:32365–32375 CrossRef Chen Y, Wu C, Wang Y (2020) T-center: A novel feature extraction approach towards large-scale iris recognition. IEEE Access 8:32365–32375 CrossRef
19.
Zurück zum Zitat Tobji R, Di W, Ayoub N (2019) FMnet: iris segmentation and recognition by using fully and multi-scale CNN for biometric security, Applied Sciences, vol.9, no.10, pp. 2042 Tobji R, Di W, Ayoub N (2019) FMnet: iris segmentation and recognition by using fully and multi-scale CNN for biometric security, Applied Sciences, vol.9, no.10, pp. 2042
20.
Zurück zum Zitat Jayanthi J, Lydia EL, Krishnaraj N, Jayasankar T, Babu RL, Suji RA (2020) An effective deep learning features based integrated framework for iris detection and recognition, Journal of Ambient Intelligence and Humanized Computing, pp. 1–11 Jayanthi J, Lydia EL, Krishnaraj N, Jayasankar T, Babu RL, Suji RA (2020) An effective deep learning features based integrated framework for iris detection and recognition, Journal of Ambient Intelligence and Humanized Computing, pp. 1–11
21.
Zurück zum Zitat Vyas R, Kanumuri T, Sheoran G, Dubey P (2019) Efficient iris recognition through curvelet transform and polynomial fitting, Optik, vol.185, pp. 859–867 Vyas R, Kanumuri T, Sheoran G, Dubey P (2019) Efficient iris recognition through curvelet transform and polynomial fitting, Optik, vol.185, pp. 859–867
22.
Zurück zum Zitat Juneja K, Rana C (2021) Compression-Robust and Fuzzy-Based Feature-Fusion Model for Optimizing the Iris Recognition. Wireless Pers Commun 116(1):267–300 CrossRef Juneja K, Rana C (2021) Compression-Robust and Fuzzy-Based Feature-Fusion Model for Optimizing the Iris Recognition. Wireless Pers Commun 116(1):267–300 CrossRef
23.
Zurück zum Zitat Kumar MR, Arthi K (2020) An effective non-cooperative iris recognition system using hierarchical collaborative representation-based classification. J Supercomputing 76(8):5835–5848 CrossRef Kumar MR, Arthi K (2020) An effective non-cooperative iris recognition system using hierarchical collaborative representation-based classification. J Supercomputing 76(8):5835–5848 CrossRef
24.
Zurück zum Zitat Jan F, Min-Allah N (2020) An effective iris segmentation scheme for noisy images. Biocybernetics and Biomedical Engineering 40(3):1064–1080 CrossRef Jan F, Min-Allah N (2020) An effective iris segmentation scheme for noisy images. Biocybernetics and Biomedical Engineering 40(3):1064–1080 CrossRef
25.
Zurück zum Zitat Jan F, Min-Allah N, Agha S, Usman I, Khan I (2021) A robust iris localization scheme for the iris recognition. Multimedia Tools and Applications 80(3):4579–4605 CrossRef Jan F, Min-Allah N, Agha S, Usman I, Khan I (2021) A robust iris localization scheme for the iris recognition. Multimedia Tools and Applications 80(3):4579–4605 CrossRef
26.
Zurück zum Zitat Singh G, Singh RK, Saha R, Agarwal N (2020) IWT based iris recognition for image authentication. Procedia Comput Sci 171:1868–1876 CrossRef Singh G, Singh RK, Saha R, Agarwal N (2020) IWT based iris recognition for image authentication. Procedia Comput Sci 171:1868–1876 CrossRef
27.
Zurück zum Zitat Ignat A, Păvăloi I (2020) Experiments on iris recognition using SURF descriptors, texture and a repetitive method. Procedia Comput Sci 176:175–184 CrossRef Ignat A, Păvăloi I (2020) Experiments on iris recognition using SURF descriptors, texture and a repetitive method. Procedia Comput Sci 176:175–184 CrossRef
30.
Zurück zum Zitat Yang P, Zhang F, Yang G (2018) Fusing DTCWT and LBP based features for rotation, illumination and scale invariant texture classification. IEEE access 6:13336–13349 CrossRef Yang P, Zhang F, Yang G (2018) Fusing DTCWT and LBP based features for rotation, illumination and scale invariant texture classification. IEEE access 6:13336–13349 CrossRef
31.
Zurück zum Zitat Muthukumar A, Kavipriya A (2019) A biometric system based on Gabor feature extraction with SVM classifier for Finger-Knuckle-Print. Pattern Recognit Lett 125:150–156 CrossRef Muthukumar A, Kavipriya A (2019) A biometric system based on Gabor feature extraction with SVM classifier for Finger-Knuckle-Print. Pattern Recognit Lett 125:150–156 CrossRef
32.
Zurück zum Zitat Kaplan K, Kaya Y, Kuncan M, Ertunç HM (2020) Brain tumor classification using modified local binary patterns (LBP) feature extraction methods, Medical hypotheses, vol. 139, pp. 109696 Kaplan K, Kaya Y, Kuncan M, Ertunç HM (2020) Brain tumor classification using modified local binary patterns (LBP) feature extraction methods, Medical hypotheses, vol. 139, pp. 109696
33.
Zurück zum Zitat Garg M, Dhiman G (2020) A novel content-based image retrieval approach for classification using GLCM features and texture fused LBP variants, Neural Computing and Applications, pp. 1–18 Garg M, Dhiman G (2020) A novel content-based image retrieval approach for classification using GLCM features and texture fused LBP variants, Neural Computing and Applications, pp. 1–18
34.
Zurück zum Zitat Zhou J, Gao L, Yao X, Chan FT, Zhang J, Li X, Lin Y (2019) A decomposition and statistical learning based many-objective artificial bee colony optimizer. Inf Sci 496:82–108 MathSciNetCrossRef Zhou J, Gao L, Yao X, Chan FT, Zhang J, Li X, Lin Y (2019) A decomposition and statistical learning based many-objective artificial bee colony optimizer. Inf Sci 496:82–108 MathSciNetCrossRef
35.
Zurück zum Zitat Karaboga D, Ozturk C (2011) A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Appl Soft Comput 11(1):652–657 CrossRef Karaboga D, Ozturk C (2011) A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Appl Soft Comput 11(1):652–657 CrossRef
36.
Zurück zum Zitat Zhou P, Han J, Cheng G, Zhang B (2019) Learning compact and discriminative stacked autoencoder for hyperspectral image classification. IEEE Trans Geosci Remote Sens 57(7):4823–4833 CrossRef Zhou P, Han J, Cheng G, Zhang B (2019) Learning compact and discriminative stacked autoencoder for hyperspectral image classification. IEEE Trans Geosci Remote Sens 57(7):4823–4833 CrossRef
Metadaten
Titel
Iris Recognition using Multi Objective Artificial Bee Colony Optimization Algorithm with Autoencoder Classifier
verfasst von
Sheela S V
Radhika K R
Publikationsdatum
15.03.2022
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 4/2022
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-022-10775-z

Weitere Artikel der Ausgabe 4/2022

Neural Processing Letters 4/2022 Zur Ausgabe