A Study of Gender Classification Techniques Based on Iris Images: A Deep Survey and Analysis
Keywords:Gender Classification, Machine Vision, Iris Biometrics, Machine Learning, Deep Learning
Gender classification is attractive in a range of applications, including surveillance and monitoring, corporate profiling, and human-computer interaction. Individuals' identities may be gleaned from information about their gender, which is a kind of soft biometric. Over the years, several methods for determining a person's gender have been devised. Some of the most well-known ones are based on physical characteristics like face, fingerprint, palmprint, DNA, ears, gait, and iris. On the other hand, facial features account for the vast majority of gender classification methods. Also, the iris is a significant biometric trait, because the iris, according to research, remains basically constant during an individual's life. Besides that, the iris is externally visible and is non-invasive to the user, which is important for practical applications. Furthermore, there are already high-quality methods for segmenting and encoding iris images, and the current methods facilitate selecting and extracting attribute vectors from iris textures. This study discusses several approaches to determining gender. The previous works of literature are briefly reviewed. Additionally, there are a variety of methodologies for different steps of gender classification. This study provides researchers with knowledge and analysis of the existing gender classification approaches. Also, it will assist researchers who are interested in this specific area, as well as highlight the gaps and challenges in the field, and finally provide suggestions and future paths for improvement.
Abdelwhab, A., & Viriri, S. (2018). A Survey on Soft Biometrics for Human Identification. Machine Learning and Biometrics. https://doi.org/10.5772/INTECHOPEN.76021
ACAR, E. (2016). EXTRACTION OF TEXTURE FEATURES FROM LOCAL IRIS AREAS BY GLCM AND IRIS RECOGNITION SYSTEM BASED ON KNN. European Journal of Technic, 6(1), 44–52.
Adekunle, A., A, Y., Aiyeniko, A., O, O., Eze, E., O, M., & O.D, A. (2020). Feature Extraction Techniques for Iris Recognition System: A Survey. International Journal of Innovative Research in Computer Science & Technology, 8(2), 37–42. https://doi.org/10.21276/ijircst.2020.8.2.5
Agbo-Ajala, O., & Viriri, S. (2021). Deep learning approach for facial age classification: a survey of the state-of-the-art. In Artificial Intelligence Review (Vol. 54, Issue 1). Springer Netherlands. https://doi.org/10.1007/s10462-020-09855-0
Alghaili, M., Li, Z., & Ali, H. A. R. (2020). Deep feature learning for gender classification with covered/camouflaged faces. IET Image Processing, 14(15), 3957–3964. https://doi.org/10.1049/iet-ipr.2020.0199
Angée, C., Nedelec, B., Erjavec, E., Rozet, J. M., & Taie, L. F. (2021). Congenital Microcoria: Clinical Features and Molecular Genetics. Genes 2021, Vol. 12, Page 624, 12(5), 624. https://doi.org/10.3390/GENES12050624
Aryanmehr, S., Karimi, M., & Boroujeni, F. Z. (2018). CVBL IRIS Gender Classification Database Image Processing and Biometric Research, Computer Vision and Biometric Laboratory (CVBL). 2018 3rd IEEE International Conference on Image, Vision and Computing, ICIVC 2018, 433–438. https://doi.org/10.1109/ICIVC.2018.8492757
Banerjee, S., & Mery, D. (2015). Iris Segmentation Using Geodesic Active Contours and GrabCut. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9555, 48–60. https://doi.org/10.1007/978-3-319-30285-0_5
Bansal, A., Agarwal, R., & Sharma, R. K. (2012). SVM based gender classification using iris images. Proceedings - 4th International Conference on Computational Intelligence and Communication Networks, CICN 2012, 425–429. https://doi.org/10.1109/CICN.2012.192
Bartfai, A., Levander, S. E., Nybäck, H., Berggren, B. M., & Schalling, D. (1985). Smooth pursuit eye tracking, neuropsychological test performance, and computed tomography in schizophrenia. Psychiatry Research, 15(1), 49–62. https://doi.org/10.1016/0165-1781(85)90039-3
Batchelor, B. G. (1980). Book Review: Digital Image Processing. In The International Journal of Electrical Engineering & Education (Vol. 17, Issue 3). https://doi.org/10.1177/002072098001700324
Bhanu, B., & Kumar, A. (n.d.). Advances in Computer Vision and Pattern Recognition Deep Learning for Biometrics.
Boyd, K., & Turbert, D. (2021). Eye Anatomy: Parts of the Eye and How We See - American Academy of Ophthalmology. American Academy of Opthalmology Website, 1–4.
Brifcani, A. M. A., & Al-Bamerny, J. N. (2010). Image compression analysis using multistage vector quantization based on discrete wavelet transform. Proceedings of 2010 International Conference on Methods and Models in Computer Science, ICM2CS-2010, 46–53. https://doi.org/10.1109/ICM2CS.2010.5706717
By, E. (2018). Deep Learning in Biometrics. In Deep Learning in Biometrics. https://doi.org/10.1201/b22524
Cantoni, V., Cascone, L., Nappi, M., & Porta, M. (2020). Demographic classification through pupil analysis. Image and Vision Computing, 102, 103980. https://doi.org/10.1016/J.IMAVIS.2020.103980
Cascone, L., Medaglia, C., Nappi, M., & Narducci, F. (2020). Pupil size as a soft biometrics for age and gender classification. Pattern Recognition Letters, 140, 238–244. https://doi.org/10.1016/j.patrec.2020.10.009
Czajka, A., & Becker, B. (2019). Application of dynamic features of the pupil for iris presentation attack detection. Advances in Computer Vision and Pattern Recognition, 151–168. https://doi.org/10.1007/978-3-319-92627-8_7
Daugman, J. (2009). How Iris Recognition Works. The Essential Guide to Image Processing, 14(1), 715–739. https://doi.org/10.1016/B978-0-12-374457-9.00025-1
Duan, M., Li, K., Yang, C., & Li, K. (2018). A hybrid deep learning CNN–ELM for age and gender classification. Neurocomputing, 275, 448–461. https://doi.org/10.1016/j.neucom.2017.08.062
Eskandari, M., & Sharifi, O. (2019). Effect of face and ocular multimodal biometric systems on gender classification. IET Biometrics, 8(4), 243–248. https://doi.org/10.1049/iet-bmt.2018.5134
Fang, B., Lu, Y., Zhou, Z., Li, Z., Yan, Y., Yang, L., Jiao, G., & Li, G. (2019). Classification of genetically identical left and right irises using a convolutional neural network. Electronics (Switzerland), 8(10), 2–11. https://doi.org/10.3390/electronics8101109
Feng, G., & Wu, Y. (2008). An iris recognition algorithm based on DCT and GLCM. Optical and Digital Image Processing, 7000, 70001H. https://doi.org/10.1117/12.780158
Gu, S., & Ding, L. (2019). A Complex-Valued VGG Network Based Deep Learing Algorithm for Image Recognition. 9th International Conference on Intelligent Control and Information Processing, ICICIP 2018, 340–343. https://doi.org/10.1109/ICICIP.2018.8606702
Hara, K., Saito, D., & Shouno, H. (2015). Analysis of function of rectified linear unit used in deep learning. Proceedings of the International Joint Conference on Neural Networks, 2015-Septe. https://doi.org/10.1109/IJCNN.2015.7280578
Hassan, M. M., Hussein, H. I., Eesa, A. S., & Mstafa, R. J. (2021). Face recognition based on gabor feature extraction followed by fastica and lda. Computers, Materials and Continua, 68(2), 1637–1659. https://doi.org/10.32604/CMC.2021.016467
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012). Improving neural networks by preventing co-adaptation of feature detectors.
Hollingsworth, K., Bowyer, K. W., & Flynn, P. J. (2009). Pupil dilation degrades iris biometric performance. Computer Vision and Image Understanding, 113(1), 150–157. https://doi.org/10.1016/J.CVIU.2008.08.001
JG Daugman - US Patent 5, 291,560, & 1994, undefined. (n.d.). Biometric personal identification system based on iris analysis. Google Patents.
Juefei-Xu, F., & Savvides, M. (2012). Unconstrained periocular biometric acquisition and recognition using COTS PTZ camera for uncooperative and non-cooperative subjects. Proceedings of IEEE Workshop on Applications of Computer Vision, 201–208. https://doi.org/10.1109/WACV.2012.6163051
Kazakov, T. (2011). Iris Detection and Normalization.
Khalifa, I. A., Zeebaree, S. R. M., Ataş, M., & Khalifa, F. M. (2019). Image Steganalysis in Frequency Domain Using Co-Occurrence Matrix and Bpnn. Science Journal of University of Zakho, 7(1), 27–32. https://doi.org/10.25271/SJUOZ.2019.7.1.574
Khalifa, N. E. M., Taha, M. H. N., Hassanien, A. E., & Mohamed, H. N. E. T. (2019). Deep iris: Deep learning for gender classification through iris patterns. Acta Informatica Medica, 27(2), 96–102. https://doi.org/10.5455/aim.2019.27.96-102
Khan, A. R., Doosti, F., Karimi, M., Harouni, M., Tariq, U., Fati, S. M., & Ali Bahaj, S. (2021). Authentication through gender classification from iris images using support vector machine. Microscopy Research and Technique, 84(11), 2666–2676. https://doi.org/10.1002/jemt.23816
Khan, M. T., Arora, D., & Shukla, S. (2013). Feature extraction through iris images using 1-D Gabor filter on different iris datasets. 2013 6th International Conference on Contemporary Computing, IC3 2013, 445–450. https://doi.org/10.1109/IC3.2013.6612236
Kim, D. H. (2020). Deep Convolutional GANs for Car Image Generation.
Koklu, M., & Ozkan, I. A. (2020). Multiclass classification of dry beans using computer vision and machine learning techniques. Computers and Electronics in Agriculture, 174(June 2019), 105507. https://doi.org/10.1016/j.compag.2020.105507
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 2, 1097–1105.
Kumar, D. R. S., Raja, K. B., Nuthan, N., Sindhuja, B., Supriya, P., Chhotaray, R. K., & Pattnaik, S. (2011). Iris recognition based on DWT and PCA. Proceedings - 2011 International Conference on Computational Intelligence and Communication Systems, CICN 2011, 489–493. https://doi.org/10.1109/CICN.2011.102
Kumar, S., Singh, S. K., Abidi, A. I., Datta, D., & Sangaiah, A. K. (2017). Group Sparse Representation Approach for Recognition of Cattle on Muzzle Point Images. International Journal of Parallel Programming 2017 46:5, 46(5), 812–837. https://doi.org/10.1007/S10766-017-0550-X
Kumari, S., Bakshi, S., & Majhi, B. (2012). Periocular Gender Classification using Global ICA Features for Poor Quality Images. Procedia Engineering, 38, 945–951. https://doi.org/10.1016/J.PROENG.2012.06.119
Li, H., Yue, X., Wang, Z., Wang, W., Tomiyama, H., & Meng, L. (2021). A survey of Convolutional Neural Networks —From software to hardware and the applications in measurement. Measurement: Sensors, 18, 100080. https://doi.org/10.1016/J.MEASEN.2021.100080
Luo, Z. (2012). Iris Feature Extraction and Recognition Based on Wavelet-Based Contourlet Transform. Procedia Engineering, 29, 3578–3582. https://doi.org/10.1016/J.PROENG.2012.01.534
Mabuza-Hocquet, G., Ngejane, C. H., & Lefophane, S. (2018). Predicting and Classifying Gender from the Human Iris: A Survey on Recent Advances. 2018 International Conference on Advances in Big Data, Computing and Data Communication Systems, IcABCD 2018, 1–5. https://doi.org/10.1109/ICABCD.2018.8465471
Manyala, A., Cholakkal, H., Anand, V., Kanhangad, V., & Rajan, D. (2019). CNN-based gender classification in near-infrared periocular images. Pattern Analysis and Applications, 22(4), 1493–1504. https://doi.org/10.1007/s10044-018-0722-3
Patil, A., R, K., & Gornale, S. (2019). Analysis of Multi-modal Biometrics System for Gender Classification Using Face, Iris and Fingerprint Images. International Journal of Image, Graphics and Signal Processing, 11(5), 34–43. https://doi.org/10.5815/ijigsp.2019.05.04
Payasi, M., & Cecil, K. (2021). LBP and Iris Features based Human Gender Classification using radial Support Vector Machine. 2021 4th International Conference on Electrical, Computer and Communication Technologies, ICECCT 2021. https://doi.org/10.1109/ICECCT52121.2021.9616923
Podder, P., Rubaiyat Hossain Mondal, M., & Kamruzzaman, J. (2022). Iris feature extraction using three-level Haar wavelet transform and modified local binary pattern. Applications of Computational Intelligence in Multi-Disciplinary Research, 1–15. https://doi.org/10.1016/B978-0-12-823978-0.00005-8
Rahim, Z., Kadhim, H., & Salih, M. (2021). Survey of Iris Recognition using Deep Learning Techniques. Journal of Al-Qadisiyah for Computer Science and Mathematics, 13(3), 47–56.
Rai, P., & Khanna, P. (2012). Gender classification techniques: A review. Advances in Intelligent and Soft Computing, 166 AISC(VOL. 1), 51–59. https://doi.org/10.1007/978-3-642-30157-5_6
Rajput, M., & Sable, G. (2020). Deep Learning Based Gender and Age Estimation from Human Iris. SSRN Electronic Journal, 1–9. https://doi.org/10.2139/ssrn.3576471
Ramón-Balmaseda, E., Lorenzo-Navarro, J., & Castrillón-Santana, M. (2012). Gender classification in large databases. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7441 LNCS, 74–81. https://doi.org/10.1007/978-3-642-33275-3_9
Rattani, A., Reddy, N., & Derakhshani, R. (2018). Convolutional neural networks for gender prediction from smartphone-based ocular images. IET Biometrics, 7(5), 423–430. https://doi.org/10.1049/iet-bmt.2017.0171
Rekha, V., Gurupriya, S., Gayadhri, S., & Sowmya, S. (2019). Dactyloscopy based gender classification using machine learning. 2019 IEEE International Conference on System, Computation, Automation and Networking, ICSCAN 2019, 1–5. https://doi.org/10.1109/ICSCAN.2019.8878756
Reshma, P. A., Divya, K. V., Therattil, G. J., & Subair, T. B. (2018). A study of gender recognition from Iris: A literature survey. Proceedings of the International Conference on Intelligent Sustainable Systems, ICISS 2017, December 2017, 888–891. https://doi.org/10.1109/ISS1.2017.8389306
Rinky, B. P., Mondal, P., Manikantan, K., & Ramachandran, S. (2012). DWT based Feature Extraction using Edge Tracked Scale Normalization for Enhanced Face Recognition. Procedia Technology, 6, 344–353. https://doi.org/10.1016/j.protcy.2012.10.041
Sharanappa Gornale, S., Patil, A., & Ramchandra, K. (2020). Multimodal Biometrics Data Analysis for Gender Estimation Using Deep Learning. International Journal of Data Science and Analysis, 6(2), 64. https://doi.org/10.11648/j.ijdsa.20200602.11
Sharma, N., Jain, V., & Mishra, A. (2018). An Analysis Of Convolutional Neural Networks For Image Classification. Procedia Computer Science, 132, 377–384. https://doi.org/10.1016/J.PROCS.2018.05.198
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2014). Going Deeper with Convolutions. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07-12-June, 1–9. https://doi.org/10.48550/arxiv.1409.4842
Tapia, J., & Arellano, C. (2019). Gender Classification from Iris Texture Images Using a New Set of Binary Statistical Image Features. 2019 International Conference on Biometrics, ICB 2019. https://doi.org/10.1109/ICB45273.2019.8987245
Tapia, J., Arellano, C., & Viedma, I. (2019). Sex-classification from cellphones periocular iris images. Advances in Computer Vision and Pattern Recognition, 227–242. https://doi.org/10.1007/978-3-030-26972-2_11
Tapia, J. E., & Perez, C. A. (2019). Gender Classification from NIR Images by Using Quadrature Encoding Filters of the Most Relevant Features. IEEE Access, 7, 29114–29127. https://doi.org/10.1109/ACCESS.2019.2902470
Vijayalaxmi, & Rao, P. S. (2012). Eye detection using Gabor Filter and SVM. International Conference on Intelligent Systems Design and Applications, ISDA, 880–883. https://doi.org/10.1109/ISDA.2012.6416654
Wildes, R. P. (1997). Iris recognition: An emerging biometrie technology. Proceedings of the IEEE, 85(9), 1348–1363. https://doi.org/10.1109/5.628669
Willoughby, C. E., Ponzin, D., Ferrari, S., Lobo, A., Landau, K., & Omidi, Y. (2010). Anatomy and physiology of the human eye: effects of mucopolysaccharidoses disease on structure and function – a review. Clinical & Experimental Ophthalmology, 38(SUPPL. 1), 2–11. https://doi.org/10.1111/J.1442-9071.2010.02363.X
Wu, C., Wen, W., Afzal, T., Zhang, Y., Chen, Y., & Li, H. H. (2017). A compact DNN: Approaching GoogLeNet-level accuracy of classification and domain adaptation. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 2017-January, 761–770. https://doi.org/10.1109/CVPR.2017.88
How to Cite
Copyright (c) 2022 Basna Mohammed Salih Hasan, Ramadhan J. Mstafa
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License [CC BY-NC-SA 4.0] that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work, with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online.