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Düşük Çözünürlüklü Resimlerden Göz Merkezi Belirleme ve İris Yarıçapı Kestirimi için İyileştirilmiş Bir Algoritma

Year 2022, Volume: 9 Issue: 3, 948 - 962, 30.09.2022

Fatih Dursun Gökhan Gelen

https://doi.org/10.31202/ecjse.1011443

Abstract

Göz merkezinin doğru bir şekilde belirlenmesi problemi insan-bilgisayar etkileşimi, yüz tanıma, iris tanıma, bakış noktası tahmini gibi birçok bilgisayarla görü uygulamasında önemli bir yere sahiptir. Bu uygulamalarda elde edilen doğruluk değerleri, göz merkezi belirleme algoritmasının performansına doğrudan bağlıdır. Göz merkezi belirlenmesi amacıyla önerilen metotların çoğunluğu laboratuvar şartlarında alınan yüksek çözünürlüklü resimlere uygulanmaktadır. Yüksek çözünürlüklü ve kontrollü şartlarda alınan resimlerde göz merkezi oldukça doğru bir şekilde belirlebilse de düşük çözünürlüklü resimlerde belirleme işlemi çok daha zordur. Bu çalışmada göz merkezi ve iris yarıçapının düşük çözünürlüklü resimlerde belirlenmesine yönelik bir metot önerilmiştir.
Önerilen metot, yüz ve göz bölgeleri algılama, göz merkezini kabaca belirleme ve iyileştirme aşamalarından oluşan çok aşamalı bir yapıya sahiptir. Göz merkezi konumunun kabaca belirlenmesi amacıyla göz bölgesinin gradyan verisine ve irisin dairesel şekline dayanan bir metot kullanılmıştır. Kabaca belirleme algoritması ile iris sınırları içerisinde bir nokta belirlenmiştir. İris içerisinde belirlenen nokta etrafındaki bölgeye, bu çalışmada önerilen iyileştirme algoritması uygulanarak göz merkezinin tam konumu belirlenmiştir. Önerilen yaklaşımının performansı, bu alanda yaygın olarak kullanılan veri seti üzerinde test edilerek ve bulunan sonuçların diğer metotlarla karşılaştırılması sunulmuştur.

Keywords

Göz merkezi belirleme göz merkezi takibi Göz takibi iris yarıçapı tahmini iris merkezi belirleme.

References

  • [1]. Noor N. M. M, Bin Kamarudinn M. Q., Study The Different Level Of Eye Movement Based On Electrooculography (EOG) Technique, IEEE-EMBS Conference on Biomedical Engineering and Sciences, 2016, Kuala Lumpur, Malaysia.
  • [2]. Zheng W, Gao K, Li G, Liu W, Liu C, Liu J, Wang G, Lu B., Vigilance Estimation Using a Wearable EOG Device in Real Driving Environment, IEEE Transactions on Intelligent Transportation Systems, 2020, 21(1): 170-184.
  • [3]. Cognolato M, Atzori M, Müller H., Head-Mounted Eye Gaze Tracking Devices: An Overview Of Modern Devices And Recent Advances, Journal of Rehabilitation and Assistive Technologies Engineering, 2018, 5: 1-13.
  • [4]. Franchak M. J, Kretch K. S, Soska K. C, Adolph K. E., Head-Mounted Eye-Tracking: A New Method To Describe İnfant Looking, Child development, 2011, 82(6): 1738-1750.
  • [5]. Yahya A.E, Nordin M. J., A New Technique for Iris Localization in Iris Recognition Systems, Information Technology Journal,2008, 7: 924-929.
  • [6]. Mahlouji M, Noruzi A., Human Iris Segmentation for Iris Recognition in Unconstrained Environments, International Journal of Computer Science Issues, 2012, 9(1).
  • [7]. Soliman N. F, Mohamed E, Magdi F, Abd El-Samie F. E, AbdElnaby M., Efficient İris Localization And Recognition, Optik-International Journal for Light and Electron Optics, 2017,140: 469-475.
  • [8]. Wood E, Bulling A., Eyetab: Model-Based Gaze Estimation On Unmodified Tablet Computers, Eye Tracking Research and Applications Symposium (ETRA), 2014,Florida, USA.
  • [9]. Fridman L, Langhans P, Lee J, Reimer B., Driver Gaze Region Estimation without Use of Eye Movement, IEEE Intelligent Systems, 2016, 31(3): 49-56.
  • [10]. Shin Y. G, Choi K. A, Kim S. T, Yoo C. H, Ko S. J., A Novel 2-D Mapping-Based Remote Eye Gaze Tracking Method Using Two IR Light Sources, IEEE 2015 International Conference on Consumer Electronics, 2015, Las Vegas, USA.
  • [11]. Boumbarov O, Panev S, Sokolov S, Kanchev V., IR Based Pupil Tracking Using Optimized Particle Filter, IEEE 2009 International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 2009, Rende, Italy.
  • [12]. Timm F, Barth E., Accurate Eye Centre Localisation By Means of Gradients, International Conference on Computer Vision Theory and Applications, 2011, Algarve, Portugal.
  • [13]. Valenti R, Gevers T., Accurate Eye Center Location and Tracking Using Isophote Curvature, 26th IEEE Conference on Computer Vision and Pattern Recognition, 2008, Anchorage, USA.
  • [14]. Garg S, Tripathi A, Cutrell E., Accurate Eye Center Localization Using Snakuscule, IEEE 2016 Winter Conference on Applications of Computer Vision, 2016, New York, USA.
  • [15]. Cai H, Liu B, Zhang J, Chen S, Liu H., Visual Focus of Attention Estimation Using Eye Center Localization, IEEE Systems Journal, 2017,11(3): 1320-1325.
  • [16]. Toennies K, Behrens F, Aurnhammer M., Feasibility of Hough-Transform-Based Iris Localization for Real-Time-Application, Object Recognition Supported By User İnteraction For Service Robots, 2002, 16(2):1053–1056.
  • [17]. George A, Routray A., Fast and Accurate Algorithm for Eye Localization for Gaze Tracking in Low Resolution Images, IET Computer Vision, 2016,10(7): 660-669.
  • [18]. Xia Y, Lou J, Dong J, Li G, Yu H., SDM-based Means of Gradient for Eye Center Localization, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress, 2018, Athens, Greece.
  • [19]. Daugman J., How Iris Recognition Works, IEEE Transactions on Circuits and Systems for Video Technology , 2009, 4(1): 21-30.
  • [20]. Viola P, Jones M., Rapid Object Detection using a Boosted Cascade of Simple Features, IEEE 2001 Computer Society Conference on Computer Vision and Pattern Recognition, 2001, Hawaii, USA.
  • [21]. Özuysal M., Artırılmış Gerçeklik İçin BRIEF Betimleyicileri Ve Yerelliğe Duyarlı Karma Yöntemi İle Nesne Arama, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 2017, 23(5): 588-596.
  • [22]. Bayrakdar S, Akgün D, Yücedağ İ., Video Dosyaları Üzerinde Yüz İfade Analizi İçin Hızlandırılmış Bir Yaklaşım, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi,2017, 23(5): 602-613.
  • [23]. Cristinacce D, Cootes T, Scott I., A Multi-Stage Approach to Facial Feature Detection, 15th British Machine Vision Conference, 2004, Londra, England.
  • [24]. BioID Company, The BioID Face Database, https://www.bioid.com/facedb(14.10.2021).
  • [25]. Soelistio Y. E, Postma E, Maes A., Circle-based Eye Center Localization (CECL), 14th IAPR International Conference on Machine Vision Applications (MVA), 2015, Tokyo, Japan.
  • [26]. Chen S, Liu C., Clustering-Based Discriminant Analysis for Eye Detection, IEEE Transactıons on Image Processing, 2014, 23(4):1629-1638.

An Improved Algorithm for Eye Center Localization and Iris Radius Estimation from Low-Resolution Images

Year 2022, Volume: 9 Issue: 3, 948 - 962, 30.09.2022

Fatih Dursun Gökhan Gelen

https://doi.org/10.31202/ecjse.1011443

Abstract

The accurate eye center localization problem is of great importance for many computer vision applications such as human-computer interaction, face recognition, iris recognition, and gaze estimation. The achieved accuracy in these applications is directly affected by the eye center localization process. The majority of the proposed approach for eye center localization achieve satisfactory results in high-resolution images taken in laboratory conditions. Despite success in controlled environments, the eye center determination process is much more challenging for low-resolution images. Given the need for localization in low-resolution images, this study proposes a method to tackle this problem.

The proposed method introduces a multi-stage framework consisting of face and eye–area detection, coarse eye center localization, and precise localization. To estimate coarse eye center location, a method that leverages the gradient field in the eye region and exploits the iris's circular shape was adapted. Having been applied this method, a point in iris boundary was determined. After which, a refinement algorithm developed in our study was employed to find the precise location of the eye center in the region around the already found rough point. The performance of the proposed approach was evaluated on a data set widely used in this field, and the results were compared with other methods.

Keywords

Eye center localization Eye center tracking Eye tracking Iris radius estimation Eye center tracking Eye tracking Iris radius estimation Iris center localization

References

  • [1]. Noor N. M. M, Bin Kamarudinn M. Q., Study The Different Level Of Eye Movement Based On Electrooculography (EOG) Technique, IEEE-EMBS Conference on Biomedical Engineering and Sciences, 2016, Kuala Lumpur, Malaysia.
  • [2]. Zheng W, Gao K, Li G, Liu W, Liu C, Liu J, Wang G, Lu B., Vigilance Estimation Using a Wearable EOG Device in Real Driving Environment, IEEE Transactions on Intelligent Transportation Systems, 2020, 21(1): 170-184.
  • [3]. Cognolato M, Atzori M, Müller H., Head-Mounted Eye Gaze Tracking Devices: An Overview Of Modern Devices And Recent Advances, Journal of Rehabilitation and Assistive Technologies Engineering, 2018, 5: 1-13.
  • [4]. Franchak M. J, Kretch K. S, Soska K. C, Adolph K. E., Head-Mounted Eye-Tracking: A New Method To Describe İnfant Looking, Child development, 2011, 82(6): 1738-1750.
  • [5]. Yahya A.E, Nordin M. J., A New Technique for Iris Localization in Iris Recognition Systems, Information Technology Journal,2008, 7: 924-929.
  • [6]. Mahlouji M, Noruzi A., Human Iris Segmentation for Iris Recognition in Unconstrained Environments, International Journal of Computer Science Issues, 2012, 9(1).
  • [7]. Soliman N. F, Mohamed E, Magdi F, Abd El-Samie F. E, AbdElnaby M., Efficient İris Localization And Recognition, Optik-International Journal for Light and Electron Optics, 2017,140: 469-475.
  • [8]. Wood E, Bulling A., Eyetab: Model-Based Gaze Estimation On Unmodified Tablet Computers, Eye Tracking Research and Applications Symposium (ETRA), 2014,Florida, USA.
  • [9]. Fridman L, Langhans P, Lee J, Reimer B., Driver Gaze Region Estimation without Use of Eye Movement, IEEE Intelligent Systems, 2016, 31(3): 49-56.
  • [10]. Shin Y. G, Choi K. A, Kim S. T, Yoo C. H, Ko S. J., A Novel 2-D Mapping-Based Remote Eye Gaze Tracking Method Using Two IR Light Sources, IEEE 2015 International Conference on Consumer Electronics, 2015, Las Vegas, USA.
  • [11]. Boumbarov O, Panev S, Sokolov S, Kanchev V., IR Based Pupil Tracking Using Optimized Particle Filter, IEEE 2009 International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, 2009, Rende, Italy.
  • [12]. Timm F, Barth E., Accurate Eye Centre Localisation By Means of Gradients, International Conference on Computer Vision Theory and Applications, 2011, Algarve, Portugal.
  • [13]. Valenti R, Gevers T., Accurate Eye Center Location and Tracking Using Isophote Curvature, 26th IEEE Conference on Computer Vision and Pattern Recognition, 2008, Anchorage, USA.
  • [14]. Garg S, Tripathi A, Cutrell E., Accurate Eye Center Localization Using Snakuscule, IEEE 2016 Winter Conference on Applications of Computer Vision, 2016, New York, USA.
  • [15]. Cai H, Liu B, Zhang J, Chen S, Liu H., Visual Focus of Attention Estimation Using Eye Center Localization, IEEE Systems Journal, 2017,11(3): 1320-1325.
  • [16]. Toennies K, Behrens F, Aurnhammer M., Feasibility of Hough-Transform-Based Iris Localization for Real-Time-Application, Object Recognition Supported By User İnteraction For Service Robots, 2002, 16(2):1053–1056.
  • [17]. George A, Routray A., Fast and Accurate Algorithm for Eye Localization for Gaze Tracking in Low Resolution Images, IET Computer Vision, 2016,10(7): 660-669.
  • [18]. Xia Y, Lou J, Dong J, Li G, Yu H., SDM-based Means of Gradient for Eye Center Localization, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress, 2018, Athens, Greece.
  • [19]. Daugman J., How Iris Recognition Works, IEEE Transactions on Circuits and Systems for Video Technology , 2009, 4(1): 21-30.
  • [20]. Viola P, Jones M., Rapid Object Detection using a Boosted Cascade of Simple Features, IEEE 2001 Computer Society Conference on Computer Vision and Pattern Recognition, 2001, Hawaii, USA.
  • [21]. Özuysal M., Artırılmış Gerçeklik İçin BRIEF Betimleyicileri Ve Yerelliğe Duyarlı Karma Yöntemi İle Nesne Arama, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 2017, 23(5): 588-596.
  • [22]. Bayrakdar S, Akgün D, Yücedağ İ., Video Dosyaları Üzerinde Yüz İfade Analizi İçin Hızlandırılmış Bir Yaklaşım, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi,2017, 23(5): 602-613.
  • [23]. Cristinacce D, Cootes T, Scott I., A Multi-Stage Approach to Facial Feature Detection, 15th British Machine Vision Conference, 2004, Londra, England.
  • [24]. BioID Company, The BioID Face Database, https://www.bioid.com/facedb(14.10.2021).
  • [25]. Soelistio Y. E, Postma E, Maes A., Circle-based Eye Center Localization (CECL), 14th IAPR International Conference on Machine Vision Applications (MVA), 2015, Tokyo, Japan.
  • [26]. Chen S, Liu C., Clustering-Based Discriminant Analysis for Eye Detection, IEEE Transactıons on Image Processing, 2014, 23(4):1629-1638.
There are 26 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler
Authors

Fatih Dursun 0000-0002-7472-3717

Gökhan Gelen 0000-0002-2780-3386

Publication Date September 30, 2022
Submission Date October 19, 2021
Acceptance Date January 14, 2022
Published in Issue Year 2022 Volume: 9 Issue: 3

Cite

IEEE F. Dursun and G. Gelen, “Düşük Çözünürlüklü Resimlerden Göz Merkezi Belirleme ve İris Yarıçapı Kestirimi için İyileştirilmiş Bir Algoritma”, ECJSE, vol. 9, no. 3, pp. 948–962, 2022, doi: 10.31202/ecjse.1011443.
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