Hiper kişiselleştirilmiş pazarlama için evrişimsel sinir ağını kullanarak yürüyüş biçimi tabanlı cinsiyet tanıma

Yıl 2025, , 603 - 614, 16.08.2024

Murat Berksan , Selay Ilgaz Sümer , Emre Sümer

https://doi.org/10.17341/gazimmfd.1308742

Öz

Teknolojideki gelişmeler, işletmelerin pazarlama faaliyetlerini önemli ölçüde etkilemiştir. Teknolojinin pazarlama faaliyetlerine entegre edilmesi ile birlikte satışlar artmış ve hedef pazarlarda daha fazla dikkat çekilmeye başlanmıştır. İşletmeler, teknolojinin getirdiği fırsatlar sayesinde, tüketicilerin kişisel ihtiyaçlarını ve beklentilerini daha kolay anlama imkânına sahip olmuştur. Böylelikle, pazarlamada kişiselleştirme, pazarlamanın merkezinde yer almaya başlamıştır. Ancak son yıllarda yaşanan gelişmeler, işletmelerin kişiselleştirilmesinin bir adım ötesinde olan hiper kişiselleştirme konusunu gündeme getirmiştir. Bunda yapay zekânın, makine öğrenmesinin ve nesnelerin internetinin büyük ve önemli bir etkisi vardır. Bu makalede, hiper kişiselleştirilmiş pazarlama faaliyetleri için önemli bir örnek olan yürüyüş biçimi tabanlı cinsiyet tanıma sorununa, Evrişimsel Sinir Ağları ile çözüm getirilmeye çalışılmıştır. Bu amaçla farklı ağlar değerlendirilmiş ve bir temel ağ seçilmiştir. Mimari seçenekler ve üst değişkenler üzerinde deneyler yapılarak temel ağ üzerinde ek ayarlamalar yapılmıştır. Sonuçlar mevcut çalışmalarla karşılaştırıldığında umut verici bir performans göstermekte olup deneysel sonuçlar, ağ yapısının ve üst değişkenlerin performansı nasıl etkilediğine dair bir içgörü sağlamaktadır. Deneyler, girdi olarak bir öznitelik tanımlayıcısı olan yürüyüş biçimi silüeti kullanılarak gerçekleştirilmiştir. Önerilen ESA mimarisi kullanıldığında, genel doğruluk düzeyinin %97,45 olduğu hesaplanmıştır. Bu dikkate alındığında, elde edilen sonuç, sorun alanımız olan yürüyüş biçimi öznitelik tanımlamasının sınıflandırma amacıyla kullanılması konusunda bir anlayış kazanmaya imkân tanımaktadır.

Anahtar Kelimeler

Pazarlama, cinsiyet tanıma, evrişimsel sinir ağı, yürüyüş biçimi silüeti, hiper kişiselleştirme

Kaynakça

• 1. Zengin F., Dijital Pazarlama İletişiminde Yeni Yönelim: Hiper Kişiselleştirme, Uluslararası Halkla İlişkiler ve Reklam Çalışmaları Dergisi, 4 (1), 8-37, 2021.
• 2. Jain A.K., Bolle R., Pankanti S., Biometrics: Personal Identification in Networked Society. MA: Kluwer Academic Publishers, A.B.D., 1998.
• 3. Kozlowski L.T., Cutting J.E., Recognizing the sex of a walker from a dynamic point-light display, Percept. Psychophys., 21 (6), 575-580, 1977.
• 4. Hu M., Wang Y., Zhang Z., Wang Y., Combining Spatial and Temporal Information for Gait Based Gender Classification, Proceedings of the 20th International Conference on Pattern Recognition, 3679-3682, August 2010.
• 5. Makihara Y., Mannami H., Yagi Y., Gait Analysis of Gender and Age Using a Large-Scale Multi-view Gait Database, Proceedings of the 10th Asian Conference on Computer Vision - Volume Part II, 440-451, November 2010.
• 6. Chen L., Wang Y., Wang Y., Gender Classification Based on Fusion of Weighted Multi-View Gait Component Distance, Proceedings of the Chinese Conference on Pattern Recognition, 1-5, November 2009.
• 7. Lu J., Tan Y.-P., Uncorrelated discriminant simplex analysis for view-invariant gait signal computing, Pattern Recognit. Lett., 31 (5), 382-393, 2010.
• 8. Chang C.-Y., Wu T.-H., Using gait information for gender recognition, Proceedings of the 10th International Conference on Intelligent Systems Design and Applications, 1388-1393, November 2010.
• 9. Troje N. F., Decomposing biological motion: A framework for analysis and synthesis of human gait patterns, J. Vis., 2 (5), 371-387, 2002.
• 10. 10.Huang G., Wang Y., Gender Classification Based on Fusion of Multi-view Gait Sequences, Proceedings of the Computer Vision – ACCV 2007, 462-471, November 2007.
• 11. Zhang D., Wang Y., Investigating the separability of features from different views for gait based gender classification, Proceedings of the 19th International Conference on Pattern Recognition, 1-4, December 2008.
• 12. Zhang D., Wang Y., Using multiple views for gait-based gender classification. Proceedings of the 26th Chinese Control and Decision Conference, 2194-2197, May 2014.
• 13. Lu J., Wang G., Huang T.S., Gait-based gender classification in unconstrained environments, Proceedings of the 21st International Conference on Pattern Recognition, 3284-3287, November 2012.
• 14. Lu J., Wang G., Moulin P., Human Identity and Gender Recognition From Gait Sequences With Arbitrary Walking Directions, IEEE Trans. Inf. Forensics Secur., 9 (1), 51-61, 2014.
• 15. Zaki M.H., Sayed T., Using automated walking gait analysis for the identification of pedestrian attributes, Transp. Res. Part C Emerg. Technol., 48, 16-36, 2004.
• 16. Chang P.-C., Tien M.-C., Wu J.-L., Hu C.-S., Real-time Gender Classification from Human Gait for Arbitrary View Angles, Proceedings of the 11th IEEE International Symposium on Multimedia, 88-95, December 2009.
• 17. KalaiSelvan C., Raja A.S., Robust Gait-Based Gender Classification for Video Surveillance Applications, Appl. Math. Inf. Sci., 11 (4), 1207-1215, 2017.
• 18. Guan Y., Wei X., On the Generalization Power of Face and Gait Gender Recognition Methods, Int. J. Digit. Crime Forensics, 6 (1), 1-8, 2014.
• 19. Félez R.M., García V., Sánchez J.S., Gait-based Gender Classification Considering Resampling and Feature Selection, J. Image Graph, 1 (2), 85-89, 2013.
• 20. Martín-Félez R., Mollineda R.A., Sánchez J.S., Gender Classification from Pose-Based GEIs, Lecture Notes in Computer Science 7594, Editors: Bolc L., Tadeusiewicz R., Chmielewski L.J., Wojciechowski K., Springer-Verlag, 501-508, 2012.
• 21. Borràs R., Lapedriza À., Igual L., Depth Information in Human Gait Analysis: An Experimental Study on Gender Recognition, Lecture Notes in Computer Science 7325, Editors: Campilho A., Kamel M., Springer-Verlag, 98-105, 2012.
• 22. Igual L., Lapedriza À., Borràs R., Robust gait-based gender classification using depth cameras, EURASIP J. Image Video Process, 1, 2013.
• 23. Hofmann M., Geiger J., Bachmann S., Schuller B., Rigoll G., The TUM Gait from Audio, Image and Depth (GAID) database: Multimodal recognition of subjects and traits, J. Vis. Commun. Image Represent, 25 (1), 195-206, 2014.
• 24. Shan C., Gong S., McOwan P.W., Learning gender from human gaits and faces, Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance, 505-510, September 2007.
• 25. Zhang D., Wang Y.-H., Gender recognition based on fusion on face and gait information, Proceedings of the 2008 International Conference on Machine Learning and Cybernetics, 62-67, July 2008.
• 26. Davis J.W., Gao H., Gender Recognition from Walking Movements using Adaptive Three-Mode PCA, Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop, 9-9, June 2004.
• 27. DeCann B., Ross A., Culp M., On Clustering Human Gait Patterns, Proceedings of the 22nd International Conference on Pattern Recognition, 1794-1799, August 2014.
• 28. Juang L.-H., Wu M.-N., Lin S.-A., Gender recognition based on computer vision system, Intell. Autom. Soft Comput., 24 (2), 249-256, 2018.
• 29. Lee L., Grimson W.E.L., Gait analysis for recognition and classification, Proceedings of the 5th IEEE International Conference on Automatic Face Gesture Recognition, 155-162, May 2002.
• 30. Yu S., Tan T., Huang K., Jia K., Wu X., A Study on Gait-Based Gender Classification, IEEE Trans. Image Process., 18 (8), 1905-1910, 2009.
• 31. Li X., Maybank S.J., Yan S., Tao D., Xu D., Gait Components and Their Application to Gender Recognition, IEEE Trans. Syst. Man, Cybern. Part C (Applications Rev.), 38 (2), 145-155, 2008.
• 32. Yoo J.-H., Hwang D., Nixon M.S., Gender Classification in Human Gait Using Support Vector Machine, Lecture Notes in Computer Science 3708, Editors: Blanc-Talon J. et al., Springer-Verlag, 138-145, 2005.
• 33. Martin-Felez R., Mollineda R.A., Sanchez J.S., Towards a More Realistic Appearance-Based Gait Representation for Gender Recognition, Proceedings of the 2010 20th International Conference on Pattern Recognition, 3810-3813, August 2010.
• 34. Hu M., Wang Y., Zhang Z., Zhang D., Gait-Based Gender Classification Using Mixed Conditional Random Field, IEEE Trans. Syst. Man, Cybern. Part B, 41 (5), 1429-1439, 2011.
• 35. Handri S., Nomura S., Nakamura K., Determination of Age and Gender Based on Features of Human Motion Using AdaBoost Algorithms, Int. J. Soc. Robot, 3 (3), 233-241, 2011.
• 36. Oskuie F.B., Faez K., Gender classification using a novel gait template: Radon transform of mean gait energy image, Lecture Notes in Computer Science 6754, Editors: Kamel M., Campilho A., Springer-Verlag, 161-169, 2011.
• 37. Hu M., Wang Y., A New Approach for Gender Classification Based on Gait Analysis, Proceedings of the 2009 Fifth International Conference on Image and Graphics, 869-874, September 2009.
• 38. Hassan O.M.S., Abdulazeez A.M., Tiryaki V.M., Gait-Based Human Gender Classification Using Lifting 5/3 Wavelet and Principal Component Analysis, Proceedings of the 2018 International Conference on Advanced Science and Engineering, 173-178, October 2018.
• 39. Livne M., Sigal L., Troje N.F., Fleet D.J., Human attributes from 3D pose tracking, Comput. Vis. Image Underst., 116 (5), 648-660, 2012.
• 40. Lawson W., Duric Z., Wechsler H., Gait Analysis using Independent Components of image motion, Proceedings of the 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition, 1-6, September 2008.
• 41. Arai K., Andrie R., Gender Classification with Human Gait Based on Skeleton Model, Proceedings of the 2013 10th International Conference on Information Technology: New Generations, 113-118, April 2013.
• 42. Handri S., Nakamura K., Nomura S., Gender and Age Classification Based on Pattern of Human Motion Using Choquet Integral Agent Networks, Journal of Advanced Computational Intelligence and Intelligent Informatics, 13 (4), 481-488, 2009.
• 43. LeCun Y., Haffner P., Bottou L., Bengio Y., Object Recognition with Gradient-Based Learning. Lecture Notes in Computer Science 1681, Editors: Mundy J.L. et al., Springer-Verlag, 319-345, 1999.
• 44. Marin-Jimenez M.J., Castro F.M., Guil N., De la Torre F., Medina-Carnicer R., Deep multi-task learning for gait-based biometrics, Proceedings of the 2017 IEEE International Conference on Image Processing, 106-110, September 2017.
• 45. Liu T., Ye X., Sun B., Combining Convolutional Neural Network and Support Vector Machine for Gait-based Gender Recognition, Proceedings of the 2018 Chinese Automation Congress, 3477-3481, November 2018.
• 46. Xu C., Makihara Y., Ogi G., Li X., Yagi Y., Lu J., The OU-ISIR Gait Database comprising the Large Population Dataset with Age and performance evaluation of age estimation, IPSJ Trans. Comput. Vis. Appl., 9 (1), 24, 2017.
• 47. Han J.J., Bhanu B.B., Individual recognition using gait energy image, IEEE Trans. Pattern Anal. Mach. Intell., 28 (2), 316-322, 2006.
• 48. Liu Y., Li X., Chen X., Wang X., Li H., High-Performance Machine Learning for Large-Scale Data Classification Considering Class Imbalance, Sci. Program., 1953461, 2020.
• 49. Luo C., Li X., Wang L., He J., Li D., Zhou J., How Does the Data Set Affect CNN-based Image Classification Performance?, Proceedings of the 5th International Conference on Systems and Informatics (ICSAI 2018), 361-366, November 2018.
• 50. Cruciani F., Vafeiadis A., Nugent C., Cleland I., McCullagh P., Votis K., Giakoumis D., Tzovaras D., Chen L., Hamzaoui R., Feature Learning for Human Activity Recognition using Convolutional Neural Networks, CCF Trans. Pervasive Comput. Interact., 2, 18-32, 2020.
• 51. Shiraga K., Makihara Y., Muramatsu D., Echigo T., Yagi Y., GEINet: View-invariant gait recognition using a convolutional neural network, Proceedings of the 2016 International Conference on Biometrics, 1-8, June 2016.
• 52. Krizhevsky A., Sutskever I., Hinton G. E., ImageNet Classification with Deep Convolutional Neural Networks, Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1, 097-1105, December 2012.
• 53. Chollet F., Keras, GitHub. https://github.com/fchollet/keras. Yayın tarihi Şubat 11, 2021. Erişim tarihi Eylül 13, 2022.
• 54. Nair V., Hinton G.E., Rectified linear units improve restricted boltzmann machines, Proceedings of the 27th International Conference on International Conference on Machine Learning, 807-814, June 2010.
• 55. Srivastava N., Hinton G., Krizhevsky A., Sutskever I., Salakhutdinov R., Dropout: A Simple Way to Prevent Neural Networks from Overfitting, J. Mach. Learn. Res., 15 (1), 1929-1958, 2014.
• 56. Kingma D. P., Ba J., Adam: A Method for Stochastic Optimization. arXiv:1412.6980. https://arxiv.org/abs/1412.6980. Yayın tarihi Ocak 31, 2017. Erişim tarihi Ekim 01, 2022.
• 57. İlgün E.G., Samet R., Increasing the performance of intrusion detection models developed using machine learning method with preprocessing applied to the dataset, Journal of the Faculty of Engineering and Architecture of Gazi University, 39 (2), 679-692, 2024.
• 58. Bottou L., Bousquet O., The Tradeoffs of Large Scale Learning, Proceedings of the 20th International Conference on Neural Information Processing Systems, 161-168, December 2007.
• 59. Hinton G., Srivastava N., Swersky K., Lecture 6a Overview of mini-batch gradient descent. https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf. Yayın tarihi Mart 27, 2014. Erişim tarihi Ekim 01, 2022.
• 60. Zeiler M.D., ADADELTA: An Adaptive Learning Rate Method. arXiv:1212.5701. https://arxiv.org/abs/1212.5701, Yayın tarihi Aralık 04, 2012. Erişim tarihi Ekim 01, 2022.
• 61. Goodfellow I., Bengio Y., Courville A., Deep Learning - 1st ed. Cambridge MA: MIT Press, 2016.
• 62. Simonyan K., Zisserman A., Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409.1556. https://arxiv.org/abs/1409.1556. Yayın tarihi Nisan 15, 2015. Erişim tarihi Ekim 03, 2022.
• 63. Gu J. et al., Recent advances in convolutional neural networks, Pattern Recognit., 77 (1), 354-377, 2018.
• 64. Lu J. Tan Y.-P., Gait-Based Human Age Estimation, IEEE Trans. Inf. Forensics Secur., 5 (4), 761–770, 2010.
• 65. Doğruyol Z., Güner S., Examining the impact of product variety on design, supply, and production processes using system dynamics approach, Journal of the Faculty of Engineering and Architecture of Gazi University, 36 (3), 1185-1198, 2021.