Abstract
As a new biometric recognition technology, automatic age estimation based on facial image has become an important subject in the field of computer vision and human-computer interaction (HCI). And automatic facial age estimate system has been increasingly used in criminal investigation, image retrieval and intelligent monitoring in recent years. Therefore, the research of facial age estimation has a broad prospect. Convolution neural network (CNN) as a deep learning architecture can extract the essential features of the facial image with a better effect than traditional methods, especially in the case of large changes in imaging shooting conditions. In this paper, an improved method of facial age estimate based on CNN is proposed. By considering the number limitation of the existing age estimation data sets, we adopt the method of fine-tuning the existed network model. The recognition rate can be increased by 3% based on the proposed method. A facial age estimate system has been constructed for applications and the experimental results show that the system can meet the real-time application needs.
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References
Zhang, D., Tsai, J.J.P.: Machine learning and software engineering. Softw. Quality J. 11(2), 87–119 (2014)
Natarajan, B.K.: Machine Learning, pp. 207–214 (2014)
Goodfellow, I.J., et al.: Challenges in representation learning: a report on three machine learning contests. Neural Inf. Process. 64, 117–124 (2013)
Yu, Z., Zhang, C.: Image based static facial expression recognition with multiple deep network learning. In: Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, pp. 435–442 (2015)
Kahou, S.E., et al.: Combining modality specific deep neural networks for emotion recognition in video. In: Proceedings of the 15th ACM on International Conference on Multimodal Interaction, pp. 543–550 (2013)
Minchul, S., et al.: Baseline CNN structure analysis for facial expression recognition. In: 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pp. 26–31 (2014)
Kwon, Y.H., Lobo, N.V.: Age classification from facial images. Comput. Vis. Image Understand. 74(1), 1–21 (1999)
Hayashi, J.: Age and gender estimation based on wrinkle texture and color of facial images. In: Proceedings of the 16th International Conference, vol. 1(1), pp. 405–408 (2002)
Nakano, M., Yasukata, F., Fukumi, M.: Age classification from face images focusing on edge information. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds.) KES 2004. LNCS, vol. 3213, pp. 898–904. Springer, Heidelberg (2004). doi:10.1007/978-3-540-30132-5_121
Lanitis, A., Draganova, C., Christodoulou, C.: Comparing different classifiers for automatic age estimation. IEEE Trans. Syst. Man Cybern. Part B Cybern. 34(1), 621–628 (2004)
Chao, E.L., Liu, J.Z., Ding, J.J.: Facial age estimation based on label-sensitive learning and age-oriented regression. Pattern Recogn. 46(3), 628–641 (2013)
Krizhevsky, A., et al.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
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Zou, J., Yang, X., Zhang, H., Chen, X. (2018). Automatic Facial Age Estimate Based on Convolution Neural Network. In: Pan, JS., Tsai, PW., Watada, J., Jain, L. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. IIH-MSP 2017. Smart Innovation, Systems and Technologies, vol 82. Springer, Cham. https://doi.org/10.1007/978-3-319-63859-1_3
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DOI: https://doi.org/10.1007/978-3-319-63859-1_3
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