Age Estimation from Facial Images Using Transfer Learning and k-fold Cross-Validation

dc.contributor.authorProttoy, Mahruf Islam
dc.contributor.authorUddin, S. M. Shihab
dc.contributor.authorMorshed, Md. Samin
dc.date.accessioned2022-04-16T02:37:47Z
dc.date.available2022-04-16T02:37:47Z
dc.date.issued2021-10-30
dc.descriptionSupervised by A.B.M. Ashikur Rahman, Assistant Professor, Department of Computer Science and Engineering (CSE), Islamic University of Technology (IUT), Board Bazar, Gazipur, Bangladesh.en_US
dc.description.abstractThe increasing use of video-based security systems and robotics has increased research on the image analysis of human faces. Thus, face recognition, face detection, gender classification, and facial expression recognition have attracted much attention in digital image processing field. [1, 2, 3, 4, 5]. Estimating the age of a person from the analysis of his/her face image is a relatively new research topic. This thesis is focused on exploring different CNN approach on increasing accuracy in age classification. To achieve higher accuracy we used a few pre existing models and used pre trained weight which is trained for face detection,on Wild and Youtube face dataset. We also fine tuned the model and used k-fold validation. This method shows us higher accuracy. Then we compared our work with already existed papers. In other words, we tried to increase accuracy in age classification. We also compared our work with pre existed paper to show performance of our model relative to other modelsen_US
dc.identifier.citation[1] I. Huerta, C. Fern´andez, C. Segura, J. Hernando, and A. Prati, “A deep analysis on age estimation,” Pattern Recognition Letters, vol. 68, pp. 239– 249, 2015. [2] G. Levi and T. Hassner, “Age and gender classification using convolutional neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp. 34–42, 2015. [3] K. Luu, K. Seshadri, M. Savvides, T. D. Bui, and C. Y. Suen, “Contourlet appearance model for facial age estimation,” in 2011 international joint conference on biometrics (IJCB), pp. 1–8, IEEE, 2011. [4] R. Ranjan, S. Zhou, J. Cheng Chen, A. Kumar, A. Alavi, V. M. Patel, and R. Chellappa, “Unconstrained age estimation with deep convolutional neural networks,” in Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 109–117, 2015. [5] Y. Zhang, L. Liu, C. Li, et al., “Quantifying facial age by posterior of age comparisons,” arXiv preprint arXiv:1708.09687, 2017. [6] A. Gunay and V. V. Nabiyev, “Automatic age classification with lbp,” in 2008 23rd International Symposium on Computer and Information Sciences, pp. 1–4, IEEE, 2008. [7] J. Kannala and E. Rahtu, “Bsif: Binarized statistical image features,” in Proceedings of the 21st international conference on pattern recognition (ICPR2012), pp. 1363–1366, IEEE, 2012. [8] N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), vol. 1, pp. 886–893, Ieee, 2005. [9] Devangini, “Local binary patterns (lbp),” Jun 2016. 44 [10] J. Brownlee, “A gentle introduction to transfer learning for deep learning,” Sep 2019. [11] “Utkface, https://susanqq.github.io/utkface/.” [12] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Advances in neural information processing systems, vol. 25, pp. 1097–1105, 2012. [13] S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards realtime object detection with region proposal networks,” arXiv preprint arXiv:1506.01497, 2015. [14] S. Minaee, A. Abdolrashidi, H. Su, M. Bennamoun, and D. Zhang, “Biometric recognition using deep learning: A survey,” arXiv preprint arXiv:1912.00271, 2019. [15] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,” IEEE transactions on pattern analysis and machine intelligence, vol. 40, no. 4, pp. 834–848, 2017. [16] S. Minaee, Y. Wang, A. Aygar, S. Chung, X. Wang, Y. W. Lui, E. Fieremans, S. Flanagan, and J. Rath, “Mtbi identification from diffusion mr images using bag of adversarial visual features,” IEEE transactions on medical imaging, vol. 38, no. 11, pp. 2545–2555, 2019. [17] H. K. Ekenel and R. Stiefelhagen, “Why is facial occlusion a challenging problem?,” in International Conference on Biometrics, pp. 299–308, Springer, 2009. [18] R. Jana, D. Datta, and R. Saha, “Age estimation from face image using wrinkle features,” Procedia Computer Science, vol. 46, pp. 1754–1761, 2015. 45 [19] K. Ricanek and T. Tesafaye, “Morph: A longitudinal image database of normal adult age-progression,” in 7th International Conference on Automatic Face and Gesture Recognition (FGR06), pp. 341–345, IEEE, 2006. [20] A. Lanitis, “Evaluating the performance of face-aging algorithms,” in 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition, pp. 1–6, IEEE, 2008. [21] T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Transactions on pattern analysis and machine intelligence, vol. 24, no. 7, pp. 971–987, 2002. [22] V. Ojansivu and J. Heikkil¨a, “Blur insensitive texture classification using local phase quantization,” in International conference on image and signal processing, pp. 236–243, Springer, 2008. [23] A. Hyv¨arinen, J. Hurri, and P. O. Hoyer, Natural image statistics: A probabilistic approach to early computational vision., vol. 39. Springer Science & Business Media, 2009. [24] A. Das, A. Dantcheva, and F. Bremond, “Mitigating bias in gender, age and ethnicity classification: a multi-task convolution neural network approach,” in Proceedings of the European Conference on Computer Vision (ECCV) Workshops, pp. 0–0, 2018. [25] F. Schroff, D. Kalenichenko, and J. Philbin, “Facenet: A unified embedding for face recognition and clustering,” 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2015. [26] C.-J. Lin, C.-H. Lin, C.-C. Sun, and S.-H. Wang, “Evolutionary-fuzzyintegral-based convolutional neural networks for facial image classification,” Electronics, vol. 8, no. 9, p. 997, 2019. [27] “Arg (age race gender) detection using transfer learning based on facenet pretrained model,” 2019. 46 [28] R. Rothe, R. Timofte, and L. Van Gool, “Deep expectation of real and apparent age from a single image without facial landmarks,” International Journal of Computer Vision, vol. 126, no. 2, pp. 144–157, 2018. [29] K. Simonyan and A. Zisserman, “Very deep convolutional networks for largescale image recognition,” arXiv preprint arXiv:1409.1556, 2014. [30] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778, 2016. [31] J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7132–7141, 2018. [32] G. B. Huang, M. Mattar, T. Berg, and E. Learned-Miller, “Labeled faces in the wild: A database forstudying face recognition in unconstrained environments,” in Workshop on faces in’Real-Life’Images: detection, alignment, and recognition, 2008. [33] L. Wolf, T. Hassner, and I. Maoz, “Face recognition in unconstrained videos with matched background similarity,” in CVPR 2011, pp. 529–534, IEEE, 2011. 4en_US
dc.identifier.urihttp://hdl.handle.net/123456789/1316
dc.language.isoenen_US
dc.publisherDepartment of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur, Bangladeshen_US
dc.subjectCNN, VGG16, Resnet50, Senet50, UTKFace, classification, modelen_US
dc.titleAge Estimation from Facial Images Using Transfer Learning and k-fold Cross-Validationen_US
dc.typeThesisen_US

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