Abstract
Recognizing age from facial images has attracted considerable attention because of its wide array of applications and practical utilities. These include support for advertising platforms, access control, forensic objectives, and video surveillance. Efficient facial age recognition for these varied purposes is essential, necessitating smooth operation on low-cost devices or, at the very least, on a CPU to minimize implementation costs. This work proposes a lightweight CNN architecture efficiently integrated with a transformer encoder to perform facial age group recognition. An assisted bottleneck transformer encoder (ABTE) is introduced to enhance the feature extractor, generating only a few parameters and requiring low computation. As a result, the proposed architecture can achieve competitive performance on the two benchmark datasets, UTKFace and FG-NET. Moreover, this recognizer can attain real-time speed at 147 and 136 frames per second (FPS) with a single and double utilization of the ABTE, respectively, on a CPU device with Intel Core i7-9750H 2.6 GHz and 20 GB of RAM while maintaining its performance.
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References
Badr, M.M., Elbasiony, R.M., Sarhan, A.M.: Lrti: landmark ratios with task importance toward accurate age estimation using deep neural networks. Neural Comput. Appl. 34(12), 9647–9659 (2022)
Becerra-Riera, F., Morales-González, A., Méndez-Vázquez, H.: A survey on facial soft biometrics for video surveillance and forensic applications. Artif. Intell. Rev. 52(2), 1155–1187 (2019)
Berg, A., Oskarsson, M., O’Connor, M.: Deep ordinal regression with label diversity. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 2740–2747. IEEE (2021)
Cao, W., Mirjalili, V., Raschka, S.: Rank consistent ordinal regression for neural networks with application to age estimation. Pattern Recogn. Lett. 140, 325–331 (2020)
Chen, G., Peng, J., Wang, L., Yuan, H., Huang, Y.: Feature constraint reinforcement based age estimation. Multimedia Tools Appl. 82(11), 17033–17054 (2023)
Dosovitskiy, A., et al.: An image is worth 16\(\times \)16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)
Gupta, S.K., Nain, N.: Single attribute and multi attribute facial gender and age estimation. Multimedia Tools Appl. 82(1), 1289–1311 (2023)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778. IEEE (2016)
Hu, J., Shen, L., Albanie, S., Sun, G., Wu, E.: Squeeze-and-excitation networks. IEEE Trans. Pattern Anal. Mach. Intell. 42(8), 2011–2023 (2019)
Lanitis, A., Taylor, C.J., Cootes, T.F.: Toward automatic simulation of aging effects on face images. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 442–455 (2002)
Li, W., Lu, J., Feng, J., Xu, C., Zhou, J., Tian, Q.: Bridgenet: a continuity-aware probabilistic network for age estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1145–1154 (2019)
Liu, H., Lu, J., Feng, J., Zhou, J.: Label-sensitive deep metric learning for facial age estimation. IEEE Trans. Inf. Forensics Secur. 13(2), 292–305 (2017)
Mai, A.T., Nguyen, D.H., Dang, T.T.: Real-time age-group and accurate age prediction with bagging and transfer learning. In: 2021 International Conference on Decision Aid Sciences and Application (DASA), pp. 27–32. IEEE (2021)
Priadana, A., Putro, M.D., An, J., Nguyen, D.L., Vo, X.T., Jo, K.H.: Gender recognizer based on human face using cnn and bottleneck transformer encoder. In: 2023 International Workshop on Intelligent Systems (IWIS), pp. 1–6. IEEE (2023)
Priadana, A., Putro, M.D., Nguyen, D.L., Vo, X.T., Jo, K.H.: Age group recognizer based on human face supporting smart digital advertising platforms. In: 2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE), pp. 1–7. IEEE (2023)
Priadana, A., Putro, M.D., Vo, X.T., Jo, K.H.: An efficient face-based age group detector on a CPU using two perspective convolution with attention modules. In: 2022 International Conference on Multimedia Analysis and Pattern Recognition (MAPR), pp. 1–6. IEEE (2022)
Putro, M.D., Nguyen, D.L., Jo, K.H.: Lightweight convolutional neural network for real-time face detector on CPU supporting interaction of service robot. In: 2020 13th International Conference on Human System Interaction (HSI), pp. 94–99. IEEE (2020)
Savchenko, A.V.: Efficient facial representations for age, gender and identity recognition in organizing photo albums using multi-output convnet. PeerJ Comput. Sci. 5, e197 (2019)
Shen, W., Guo, Y., Wang, Y., Zhao, K., Wang, B., Yuille, A.: Deep differentiable random forests for age estimation. IEEE Trans. Pattern Anal. Mach. Intell. 43(2), 404–419 (2019)
Shin, N.H., Lee, S.H., Kim, C.S.: Moving window regression: a novel approach to ordinal regression. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18760–18769 (2022)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Suman, S., Urolagin, S.: Age gender and sentiment analysis to select relevant advertisements for a user using CNN. In: Jacob, I.J., Shanmugam, S.K., Bestak, R. (eds.) Data Intelligence and Cognitive Informatics: Proceedings of ICDICI 2021, pp. 543–557. Springer, Heidelberg (2022). https://doi.org/10.1007/978-981-16-6460-1_42
Taheri, S., Toygar, Ö.: On the use of dag-cnn architecture for age estimation with multi-stage features fusion. Neurocomputing 329, 300–310 (2019)
Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)
Wang, C., Li, Z., Mo, X., Tang, X., Liu, H.: Exploiting unfairness with meta-set learning for chronological age estimation. IEEE Trans. Inf. Forensics Secur. 18, 5678–5690 (2023)
Wang, H., Sanchez, V., Li, C.T.: Improving face-based age estimation with attention-based dynamic patch fusion. IEEE Trans. Image Process. 31, 1084–1096 (2022)
Xia, M., Zhang, X., Weng, L., Xu, Y., et al.: Multi-stage feature constraints learning for age estimation. IEEE Trans. Inf. Forensics Secur. 15, 2417–2428 (2020)
Yu, W., Zhou, P., Yan, S., Wang, X.: Inceptionnext: when inception meets convnext. arXiv preprint arXiv:2303.16900 (2023)
Zhang, Z., Song, Y., Qi, H.: Age progression/regression by conditional adversarial autoencoder. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4352–4360. IEEE (2017)
Acknowledgment
This result was supported by “Regional Innovation Strategy (RIS)" through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(MOE)(2021RIS-003).
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Priadana, A., Nguyen, DL., Vo, XT., Jo, K. (2024). Human Facial Age Group Recognizer Using Assisted Bottleneck Transformer Encoder. In: Irie, G., Shin, C., Shibata, T., Nakamura, K. (eds) Frontiers of Computer Vision. IW-FCV 2024. Communications in Computer and Information Science, vol 2143. Springer, Singapore. https://doi.org/10.1007/978-981-97-4249-3_9
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