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Yüz İmgelerinden Göz Bölgelerinin Tespitinde ESA Tabanlı Alternatif Bir Yaklaşım

Year 2021, Volume: 33 Issue: 2, 735 - 743, 15.09.2021
https://doi.org/10.35234/fumbd.956120

Abstract

Artan işlemci hızlarıyla beraber kullanımı yaygınlaşan derin sinir ağları birçok farklı alanda gösterdiği başarılarla etkinliğini ispatlamıştır. Bu çalışmada bir imge seçici yardımıyla yüz üzerinden örnekler toplanarak elde edilen imgelerin derin sinir ağlarının örüntü tanımada etkili bir algoritması olan evrişimsel sinir ağı modeline verilmesiyle yüz resimlerinden gözlerin tespiti gerçekleştirilmiştir. Geçmişten günümüze önerilen birçok göz algılama yöntemi mevcuttur. Fakat bu yöntemlerin birçoğunda aydınlatma koşulları, duruş pozisyonları, düşük çözünürlükteki görüntüler, kapalı göz, gözlük, gözlerin algılanmasında önemli bir sorun olmuştur. Önerilen sistemin evrişimsel sinir ağı modeli ile göz tespitinde zorluk çıkaran durumların model tarafından birçok örnek veri ile öğrenilmesiyle üstesinden gelinmiştir. Önerilen sistemin performansı günümüzde göz tespitinde yaygın olarak kullanılan Viola-Jones algoritmasının XML tabanlı yüz ve göz tanıma uygulaması ile karşılaştırılmıştır. Karşılaştırma sonunda önerilen sistemin gözlerin algılanmasında zorluk çıkaran yüz resimlerinde daha iyi sonuçlar verdiği görülmüştür. Doğruluk (%98,99), F1-skor (%98,99), Matthews korelasyon katsayısı (%97,99) ve R-kare (%95,98) gibi yaygın kullanılan ölçütler ile önerilen sistemin başarısı gösterilmiştir.

References

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Year 2021, Volume: 33 Issue: 2, 735 - 743, 15.09.2021
https://doi.org/10.35234/fumbd.956120

Abstract

References

  • Noble AM, Miles M, Perez MA, Guo F, Klauer SG. Evaluating driver eye glance behavior and secondary task engagement while using driving automation systems. Accident Analysis and Prevention 2021; 151.
  • Rakhmatulin I, Duchowski AT. Deep neural networks for low-cost eye tracking. Procedia Computer Science 2020; 176: 685–694.
  • Alghamdi J, Alharthi R, Alghamdi R, Alsubaie W, Alsubaie R, Alqahtani D, Ramadan RA, Alqarni L, Alshammari R. A Survey on Face Recognition Algorithms. ICCAIS 2020 - 3rd International Conference on Computer Applications and Information Security 2020; 1–5.
  • Tarnowski P, Kołodziej M, Majkowski A, Rak RJ. Eye-Tracking Analysis for Emotion Recognition. Computational Intelligence and Neuroscience 2020; 1–13.
  • Thiyaneswaran B, Padma S. Iris Recognition using Left and Right Iris Feature of the Human Eye for Biometric Security System. International Journal of Computer Applications 2012; 50(12): 37–41.
  • Viola P, Jones M. Rapid object detection using a boosted cascade of simple features. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2001; 1: 511–518.
  • Zhang K, Zhang Z, Li Z, Qiao Y. Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks. IEEE Signal Processing Letters 2016; 23(10): 1499–1503.
  • Kazemi V, Sullivan J. One millisecond face alignment with an ensemble of regression trees. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2014.
  • Soetedjo A. Eye Detection Based-on Color and Shape Features. International Journal of Advanced Computer Science and Applications 2012; 3(5).
  • Majumder G, Bhowmik MK, Bhatacharjee D. Automatic Eye Detection Using Fast Corner Detector of North East Indian (NEI) Face Images. Procedia Technology 2013; 10:646–653.
  • Knapik M, Cyganek B. Fast eyes detection in thermal images. Multimedia Tools and Applications 2020; 80(3):3601–3621.
  • Gou C, Wu Y, Wang K, Wang K, Wang FY, Ji Q. A joint cascaded framework for simultaneous eye detection and eye state estimation. Pattern Recognition 2017; 67:23–31.
  • Yu M, Lin Y, Wang X. An efficient hybrid eye detection method. Turkish Journal of Electrical Engineering and Computer Sciences 2016; 24(3):1586–1603.
  • Ghazali KH, Jadin MS, Jie M, Xiao R. Novel automatic eye detection and tracking algorithm. Optics and Lasers in Engineering 2015; 67: 49–56.
  • Liu Z, Luo P, Wang X, Tang X. Deep learning face attributes in the wild. In ICCV 2015; 3730–3738.
  • Song F, Tan X, Liu X, Chen S. Eyes closeness detection from still images with multi-scale histograms of principal oriented gradients. Pattern Recognition 2014; 47(9): 2825–2838.
  • Cun YL, Guyon I, Jackel LD, Henderson D, Boser B, Howard RE, Denker JS, Hubbard W, Graf HP. Handwritten Digit Recognition: Applications of Neural Network Chips and Automatic Learning. IEEE Communications Magazine 1989; 27(11): 41–46.
  • Vincent P, Larochelle H, Bengio Y, Manzagol PA. Extracting and composing robust features with denoising autoencoders. Proceedings of the 25th International Conference on Machine Learning 2008; 1096–1103.
  • Hochreiter S, Schmidhuber J. Long Short-Term Memory. Neural Computation 1997; 9(8): 1735–1780.
  • Salakhutdinov R, Hinton G. Deep Boltzmann Machines. 2009;5(2).
  • Martens J, Ca IU. Learning Recurrent Neural Networks with Hessian-Free Optimization Ilya Sutskever. Proceedings of the 28th International Conference on Machine Learning (ICML) 2011.
  • Hubel DH, Wiesel TN. Receptive fields and functional architecture of monkey striate cortex. The Journal of Physiology 1968; 195(1): 215–243.
  • LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE 1998; 86(11): 2278–2323.
  • Ajit A, Acharya K, Samanta A. A Review of Convolutional Neural Networks. International Conference on Emerging Trends in Information Technology and Engineering (Ic-ETITE) 2020; 1-5.
  • Li W, Liu K, Yan L, Cheng F, Lv YQ, Zhang LZ. FRD-CNN: Object detection based on small-scale convolutional neural networks and feature reuse. Scientific Reports 2019; 9(1).
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  • Sahu M, Dash R. A survey on deep learning: Convolution neural network (cnn). Smart Innovation, Systems and Technologies 2021; 153: 317–325.
  • Rachapudi V, Lavanya Devi G. Improved convolutional neural network based histopathological image classification. Evolutionary Intelligence 2020; 1–7.
  • Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov RR. Improving neural networks by preventing co-adaptation of feature detectors, 2012.
  • Hahn S, Choi H. Understanding dropout as an optimization trick. Neurocomputing 2020; 398: 64–70.
  • Home-OpenCV. https://opencv.org/, (30.12.2020).
  • Pedregosa Fabianpedregosa F, Michel V, Grisel Oliviergrisel O, Blondel M, Prettenhofer P, Weiss R, Vanderplas J, Cournapeau D, ve diğerleri. Scikit-learn: Machine learning in Python. In Journal of Machine Learning Research 2011; 12(85): 2825-2830.
There are 33 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section MBD
Authors

Kenan Donuk 0000-0002-7421-5587

Ali Arı 0000-0002-5071-6790

Davut Hanbay 0000-0003-2271-7865

Publication Date September 15, 2021
Submission Date June 22, 2021
Published in Issue Year 2021 Volume: 33 Issue: 2

Cite

APA Donuk, K., Arı, A., & Hanbay, D. (2021). Yüz İmgelerinden Göz Bölgelerinin Tespitinde ESA Tabanlı Alternatif Bir Yaklaşım. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 33(2), 735-743. https://doi.org/10.35234/fumbd.956120
AMA Donuk K, Arı A, Hanbay D. Yüz İmgelerinden Göz Bölgelerinin Tespitinde ESA Tabanlı Alternatif Bir Yaklaşım. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. September 2021;33(2):735-743. doi:10.35234/fumbd.956120
Chicago Donuk, Kenan, Ali Arı, and Davut Hanbay. “Yüz İmgelerinden Göz Bölgelerinin Tespitinde ESA Tabanlı Alternatif Bir Yaklaşım”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 33, no. 2 (September 2021): 735-43. https://doi.org/10.35234/fumbd.956120.
EndNote Donuk K, Arı A, Hanbay D (September 1, 2021) Yüz İmgelerinden Göz Bölgelerinin Tespitinde ESA Tabanlı Alternatif Bir Yaklaşım. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 33 2 735–743.
IEEE K. Donuk, A. Arı, and D. Hanbay, “Yüz İmgelerinden Göz Bölgelerinin Tespitinde ESA Tabanlı Alternatif Bir Yaklaşım”, Fırat Üniversitesi Mühendislik Bilimleri Dergisi, vol. 33, no. 2, pp. 735–743, 2021, doi: 10.35234/fumbd.956120.
ISNAD Donuk, Kenan et al. “Yüz İmgelerinden Göz Bölgelerinin Tespitinde ESA Tabanlı Alternatif Bir Yaklaşım”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi 33/2 (September 2021), 735-743. https://doi.org/10.35234/fumbd.956120.
JAMA Donuk K, Arı A, Hanbay D. Yüz İmgelerinden Göz Bölgelerinin Tespitinde ESA Tabanlı Alternatif Bir Yaklaşım. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 2021;33:735–743.
MLA Donuk, Kenan et al. “Yüz İmgelerinden Göz Bölgelerinin Tespitinde ESA Tabanlı Alternatif Bir Yaklaşım”. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, vol. 33, no. 2, 2021, pp. 735-43, doi:10.35234/fumbd.956120.
Vancouver Donuk K, Arı A, Hanbay D. Yüz İmgelerinden Göz Bölgelerinin Tespitinde ESA Tabanlı Alternatif Bir Yaklaşım. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 2021;33(2):735-43.