Abstract
The human face constitutes various biometric features that could be used to estimate important details from humans, such as age. The automation of age estimation has been further limited by variations in facial landmarks and appearances, together with the lack of enormous databases. These have also limited the efficiencies of conventional approaches such as the handcrafted method for adequate age estimation. More recently, Convolutional Neural Network (CNN) methods have been applied to age estimation and image classification with recorded improvements. In this work, we utilise the CNN-based EfficientNet architecture for age estimation, which, so far, has not been employed in any current study to the best of our knowledge. This research focused on applying the EfficientNet architecture to classify an individual’s age in the appropriate age group using the UTKface and Adience datasets. Seven EfficientNet variants (B0–B6) were presented herein, which were fine-tuned and used to evaluate age classification efficiency. Experimentation showed that the EfficientNet-B4 variant had the best performance on both datasets with accuracy of 73.5% and 81.1% on UTKFace and Adience, respectively. The models showed a promising pathway in solving problems related to learning global features, reducing training time and computational resources.
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References
de Castro, P.V.: Age estimation using deep learning on 3D facial features (2018)
Angulu, R., Tapamo, J.R., Adewumi, A.O.: Age estimation via face images: a survey. EURASIP J. Image Video Process. 2018(1), 1–35 (2018)
Huerta, I., Fernández, C., Segura, C., Hernando, J., Prati, A.: A deep analysis on age estimation. Pattern Recogn. Lett. 68, 239–249 (2015)
Liu, H., Lu, J., Feng, J., Zhou, J.: Ordinal deep feature learning for facial age estimation. In: 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), pp. 157–164. IEEE, May 2017
Yi, D., Lei, Z., Li, S.Z.: Age estimation by multi-scale convolutional network. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9005, pp. 144–158. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16811-1_10
Liu, X., et al.: AgeNet: deeply learned regressor and classifier for robust apparent age estimation. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 16–24 (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst. 25, 1097–1105 (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., Keutzer, K.:. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and \(<\)0.5 MB model size. arXiv preprint arXiv:1602.07360 (2016)
Chollet, F.:. Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)
Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105–6114. PMLR, May 2019
Levi, G., Hassner, T.: 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)
Anand, A., Labati, R.D., Genovese, A., Munoz, E., Piuri, V., Scotti, F.: Age estimation based on face images and pre-trained convolutional neural networks. In: 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–7. IEEE, November 2017
Pohjalainen, J., Räsänen, O., Kadioglu, S.: Feature selection methods and their combinations in high-dimensional classification of speaker likability, intelligibility and personality traits. Comput. Speech Lang. 29(1), 145–171 (2015)
Malhi, A., Gao, R.X.: PCA-based feature selection scheme for machine defect classification. IEEE Trans. Instrum. Meas. 53(6), 1517–1525 (2004)
Qawaqneh, Z., Mallouh, A.A., Barkana, B.D.: Deep convolutional neural network for age estimation based on VGG-face model. arXiv preprint arXiv:1709.01664 (2017)
Huang, G.B., Mattar, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. In: Workshop on Faces in ‘Real-Life’ Images: Detection, Alignment, and Recognition, October 2008
Wolf, L., Hassner, T., Maoz, I.: Face recognition in unconstrained videos with matched background similarity. In: CVPR 2011, pp. 529–534. IEEE, June 2011
Zhang, K.: Age group and gender estimation in the wild with deep RoR architecture. IEEE Access 5, 22492–22503 (2017)
Smith, P., Chen, C.: Transfer learning with deep CNNs for gender recognition and age estimation. In: 2018 IEEE International Conference on Big Data (Big Data), pp. 2564–2571. IEEE, December 2018
Lin, J., Zheng, T., Liao, Y., Deng, W.: CNN-based age classification via transfer learning. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds.) IScIDE 2017. LNCS, vol. 10559, pp. 161–168. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67777-4_14
Das, A., Dantcheva, A., Bremond, F.: 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 (2018). hal-01892103
Han, S.: Age estimation from face images based on deep learning. In: 2020 International Conference on Computing and Data Science (CDS), pp. 288–292. IEEE, August 2020
Dagher, I., Barbara, D.: Facial age estimation using pre-trained CNN and transfer learning. Multimedia Tools Appl. 80(13), 20369–20380 (2021). https://doi.org/10.1007/s11042-021-10739-w
Sukh-Erdene, B., Cho, H.C.: Facial age estimation using convolutional neural networks based on inception modules. Trans. Korean Inst. Electr. Eng. 67(9), 1224–1231 (2018)
Lapuschkin, S., Binder, A., Muller, K.R., Samek, W.: Understanding and comparing deep neural networks for age and gender classification. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 1629–1638 (2017)
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Aruleba, I., Viriri, S. (2021). Deep Learning for Age Estimation Using EfficientNet. In: Rojas, I., Joya, G., Català, A. (eds) Advances in Computational Intelligence. IWANN 2021. Lecture Notes in Computer Science(), vol 12861. Springer, Cham. https://doi.org/10.1007/978-3-030-85030-2_34
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