{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T17:00:40Z","timestamp":1776445240991,"version":"3.51.2"},"reference-count":38,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T00:00:00Z","timestamp":1695600000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Age estimation from facial images has gained significant attention due to its practical applications such as public security. However, one of the major challenges faced in this field is the limited availability of comprehensive training data. Moreover, due to the gradual nature of aging, similar-aged faces tend to share similarities despite their race, gender, or location. Recent studies on age estimation utilize convolutional neural networks (CNN), treating every facial region equally and disregarding potentially informative patches that contain age-specific details. Therefore, an attention module can be used to focus extra attention on important patches in the image. In this study, tests are conducted on different attention modules, namely CBAM, SENet, and Self-attention, implemented with a convolutional neural network. The focus is on developing a lightweight model that requires a low number of parameters. A merged dataset and other cutting-edge datasets are used to test the proposed model\u2019s performance. In addition, transfer learning is used alongside the scratch CNN model to achieve optimal performance more efficiently. Experimental results on different aging face databases show the remarkable advantages of the proposed attention-based CNN model over the conventional CNN model by attaining the lowest mean absolute error and the lowest number of parameters with a better cumulative score.<\/jats:p>","DOI":"10.3390\/data8100145","type":"journal-article","created":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T03:56:49Z","timestamp":1695614209000},"page":"145","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Attention-Based Human Age Estimation from Face Images to Enhance Public Security"],"prefix":"10.3390","volume":"8","author":[{"given":"Md. Ashiqur","family":"Rahman","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chattogram 4349, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1335-7530","authenticated-orcid":false,"given":"Shuhena Salam","family":"Aonty","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chattogram 4349, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7345-0999","authenticated-orcid":false,"given":"Kaushik","family":"Deb","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chattogram 4349, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1740-5517","authenticated-orcid":false,"given":"Iqbal H.","family":"Sarker","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chattogram 4349, Bangladesh"},{"name":"School of Science, Edith Cowan University, Perth, WA 6027, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,25]]},"reference":[{"key":"ref_1","unstructured":"Scholarpedia (2023, May 02). Facial Age Estimation\u2014Scholarpedia.org. Available online: http:\/\/www.scholarpedia.org\/article\/Facial_Age_Estimation."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1955","DOI":"10.1109\/TPAMI.2010.36","article-title":"Age synthesis and estimation via faces: A survey","volume":"32","author":"Fu","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2401","DOI":"10.1109\/TPAMI.2013.51","article-title":"Facial age estimation by learning from label distributions","volume":"35","author":"Geng","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Han, H., Otto, C., and Jain, A.K. (2013, January 4\u20137). Age estimation from face images: Human vs. machine performance. Proceedings of the 2013 International Conference on Biometrics (ICB), Madrid, Spain.","DOI":"10.1109\/ICB.2013.6613022"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, M., Zhang, W., Hu, B., Kang, J., Wang, Y., and Lu, S. (2022). Automatic assessment of depression and anxiety through encoding pupil-wave from HCI in VR scenes. ACM Trans. Multimid. Comput. Commun. Appl.","DOI":"10.1145\/3513263"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"93229","DOI":"10.1109\/ACCESS.2019.2927825","article-title":"Comprehensive analysis of the literature for age estimation from facial images","volume":"7","author":"Elrefaei","year":"2019","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"24048","DOI":"10.1109\/ACCESS.2022.3154403","article-title":"Cross-dataset learning for age estimation","volume":"10","author":"Zhang","year":"2022","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1186\/s13640-018-0278-6","article-title":"Age estimation via face images: A survey","volume":"2018","author":"Angulu","year":"2018","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1097\/00006534-200111000-00049","article-title":"A classification of facial wri","volume":"108","author":"Lemperle","year":"2001","journal-title":"Plast. Reconstr. Surg."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1097\/00006534-197759040-00006","article-title":"Abdominoplasty assessed by survey, with emphasis on complications","volume":"59","author":"Grazer","year":"1977","journal-title":"Plast. Reconstr. Surg."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s41095-022-0271-y","article-title":"Attention mechanisms in computer vision: A survey","volume":"8","author":"Guo","year":"2022","journal-title":"Comput. Vis. Media"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 22\u201325). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_13","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1109\/TPAMI.2014.2362759","article-title":"Demographic estimation from face images: Human vs. machine performance","volume":"37","author":"Han","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Guo, G., Mu, G., Fu, Y., and Huang, T.S. (2009, January 20\u201325). Human age estimation using bio-inspired features. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206681"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chen, H., Wang, T., Chen, T., and Deng, W. (2023). Hyperspectral image classification based on fusing S3-PCA, 2D-SSA and random patch network. Remote. Sens., 15.","DOI":"10.3390\/rs15133402"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1006\/cviu.1997.0549","article-title":"Age classification from facial images","volume":"74","author":"Kwon","year":"1999","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1109\/34.927467","article-title":"Active appearance models","volume":"23","author":"Cootes","year":"2001","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3087","DOI":"10.1109\/TIP.2016.2633868","article-title":"Facial age estimation with age difference","volume":"26","author":"Hu","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"102961","DOI":"10.1016\/j.cviu.2020.102961","article-title":"Age estimation from faces using deep learning: A comparative analysis","volume":"196","author":"Othmani","year":"2020","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Shin, N.H., Lee, S.H., and Kim, C.S. (2022, January 18\u201324). Moving window regression: A novel approach to ordinal regression. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01820"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.patrec.2020.11.008","article-title":"Rank consistent ordinal regression for neural networks with application to age estimation","volume":"140","author":"Cao","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1109\/TIP.2021.3139226","article-title":"Improving face-based age estimation with attention-based dynamic patch fusion","volume":"31","author":"Wang","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wang, H., Wei, X., Sanchez, V., and Li, C.T. (2018, January 7\u201310). Fusion network for face-based age estimation. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), IEEE, Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451606"},{"key":"ref_25","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_27","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014). Proceedings of the Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland, 6\u201312 September 2014, Springer."},{"key":"ref_28","unstructured":"(2023, May 04). UTKFace\u2014Susanqq.github.io. Available online: https:\/\/susanqq.github.io\/UTKFace\/."},{"key":"ref_29","unstructured":"(2023, May 04). Facial Age\u2014Kaggle.com. Available online: https:\/\/www.kaggle.com\/datasets\/frabbisw\/facial-age."},{"key":"ref_30","unstructured":"(2023, May 13). FG-NET Data by Yanwei Fu\u2014Yanweifu.github.io. Available online: https:\/\/yanweifu.github.io\/FG_NET_data\/."},{"key":"ref_31","unstructured":"(2023, May 13). Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval\u2014Bcsiriuschen.github.io. Available online: https:\/\/bcsiriuschen.github.io\/CARC\/."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Berg, A., Oskarsson, M., and O\u2019Connor, M. (2021, January 10\u201315). Deep ordinal regression with label diversity. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9412608"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1109\/TMM.2016.2608786","article-title":"Human Facial Age Estimation by Cost-Sensitive Label Ranking and Trace Norm Regularization","volume":"19","author":"Feng","year":"2017","journal-title":"IEEE Trans. Multimed."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Pan, H., Han, H., Shan, S., and Chen, X. (2018, January 18-22). Mean-variance loss for deep age estimation from a face. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00554"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1109\/TPAMI.2019.2937294","article-title":"Deep differentiable random forests for age estimation","volume":"43","author":"Shen","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e295","DOI":"10.1002\/spy2.295","article-title":"Multi-aspects AI-based modeling and adversarial learning for cybersecurity intelligence and robustness: A comprehensive overview","volume":"6","author":"Sarker","year":"2023","journal-title":"Secur. Priv."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1093\/bmb\/lds014","article-title":"Medical, statistical, ethical and human rights considerations in the assessment of age in children and young people subject to immigration control","volume":"102","author":"Cole","year":"2012","journal-title":"Br. Med. Bull."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"67","DOI":"10.14197\/atr.20121324","article-title":"Examining the Body through Technology: Age disputes and the UK border control system","volume":"2","author":"Smith","year":"2013","journal-title":"Anti-Traffick. 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