Abstract
Age estimation is a difficult process as it is impacted by race, gender, internal and external characteristics. The performance of age estimation was lower in order to accurately recognize the age of face images with minimal time. In order to overcome these issues, the Adaptive Deep Recurrent Brown Boosted Softmax Regressive Classification (ADRBSRC) method is proposed for aging facial recognition. Initially, the ADRBSRC method performs preprocessing using the adaptive bilateral filtering method, which is employed to eliminate the noise in the images. The ADRBSRC approach then extracts the important features of the input facial image using Adaptive Deep Recurrent Feature Learning (ADRFL) technique. Then, this method employs an ensemble learning method called Brown Boosted Softmax Regressive Classifier (BBSRC) in which each input image is classified into multiple age group classes (i.e. childhood age, teenage, young age, middle age, and old age) by designing a strong classifier. When compared to conventional methods like ensemble learning and ensemble CNN2ELM, the experimental results of the ADRBSRC method show that it improves Recognition Accuracy (RA) by 21% and 12% in the FGNET database, 24% and 13% in the MORPH database, 23% and 14% in the AGFW database, and 25% and 19% in the CALFW database. It reduces the computational time (CT) by 12% and 19% in the FGNET database, and 13% and 21% in the MORPH database, and in the AGFW database by 12% and 20%, and by 14% and 22% in the CALFW database.
Similar content being viewed by others
Data availability
The datasets analysed during the current study are available in the following repositories.
1. K. Ricanek Jr. and T. Tesafaye (2006) MORPH: A longitudinal image Age-progression, of normal adult. Proc 7th Int Conf Autom Face Gesture Recognit 0–4https://uncw.edu/oic/tech/morph.html
2. Face and Gestrure Recognition Research Network (FGNET) Database: http://yanwifu.github.io/FG_NET_data/FGNET.zip
3. AginG Faces in the Wild (AGFW) Database:
Url:https://dcnhan.github.io/projects/aging_project/the-agfw-database.html
https://drive.google.com/file/d/171iZQ8dqx3Yyp5t2gq06DtSWv9RMnNjG/view
4. Cross-Age LFW (CALFW) Database:http://vis-www.cs.umass.edu/lfw/
5. UMDAA-02 Face Dataset: https://umdaa02.github.io/
References
AginG Faces in the Wild (AGFW) Database: https://dcnhan.github.io/projects/aging_project/the-agfw-database.html; https://drive.google.com/file/d/171iZQ8dqx3Yyp5t2gq06DtSWv9RMnNjG/view. Accessed 06-10-2022
Choi SE, Lee YJ, Lee SJ, Park KR, Kim J (2011) Age estimation using a hierarchical classifier based on global and local facial features. Pattern Recogn 44:1262–1281. https://doi.org/10.1016/j.patcog.2010.12.005
Cross-Age LFW (CALFW) Database: http://vis-www.cs.umass.edu/lfw/. Accessed 06-10-2022
Dibeklioglu H, Alnajar F, Salah AA, Gevers T (2015) Combining facial dynamics with appearance for Age estimation. IEEE Trans Image Process 24:1928–1943. https://doi.org/10.1109/TIP.2015.2412377
Dornaika F, Bekhouche SE, Arganda-Carreras I (2020) Robust regression with deep CNNs for facial age estimation: an empirical study. Expert Syst Appl 141:112942. https://doi.org/10.1016/j.eswa.2019.112942
Duan M, Li K, Li K (2018) An ensemble CNN2ELM for age estimation. IEEE Trans Inf Forensics Secur 13:758–772. https://doi.org/10.1109/TIFS.2017.2766583
Face and Gestrure Recognition Research Network(FGNET) Database: http://yanwifu.github.io/FG_NET_data/FGNET.zip. Accessed 06-10-2022
Freund Y (2001) An adaptive version of the boost by majority algorithm. Mach Learn 43:293–318. https://doi.org/10.1023/A:1010852229904
Hu H, Li Y, Zhu Z, Zhou G (2018) CNNAuth: continuous authentication via two-stream convolutional neural networks. 2018 IEEE Int Conf networking, Archit storage, NAS 2018 - proc 1–9. https://doi.org/10.1109/NAS.2018.8515693
Li C, Liu Q, Dong W, Zhu X, Liu J, Lu H (2015) Human Age estimation based on locality and ordinal information. IEEE Trans Cybern 45:2522–2534. https://doi.org/10.1109/TCYB.2014.2376517
Li Y, Hu H, Zhou G (2019) Using data augmentation in continuous authentication on smartphones. IEEE Internet Things J 6:628–640. https://doi.org/10.1109/JIOT.2018.2851185
Li Y, Hu H, Zhu Z, Zhou G (2020) SCANet: sensor-based continuous authentication with two-stream convolutional neural networks. ACM Trans Sens Networks 16:1–27
Li Y, Zou B, Deng S, Zhou G (2020) Using feature fusion strategies in continuous authentication on smartphones. IEEE Internet Comput 1–1. https://doi.org/10.1109/mic.2020.2971447
Liu H, Lu J, Feng J, Zhou J (2017) Group-aware deep feature learning for facial age estimation. Pattern Recogn 66:82–94. https://doi.org/10.1016/j.patcog.2016.10.026
Liu H, Lu J, Feng J, Zhou J (2018) Label-sensitive deep metric learning for facial Age estimation. IEEE Trans Inf Forensics Secur 13:292–305. https://doi.org/10.1109/TIFS.2017.2746062
Lou Z, Alnajar F, Alvarez JM, Hu N, Gevers T (2018) Expression-invariant Age estimation using structured learning. IEEE Trans Pattern Anal Mach Intell 40:365–375. https://doi.org/10.1109/TPAMI.2017.2679739
Lu J, Liong VE, Zhou J (2015) Cost-sensitive local binary feature learning for facial Age estimation. IEEE Trans Image Process 24:5356–5368. https://doi.org/10.1109/TIP.2015.2481327
Othmani A, Rahman A, Abdelkawy H, Hadid A (2020) Age estimation from faces using deep learning: a comparative analysis. Comput Vis Image Underst 196:102961. https://doi.org/10.1016/j.cviu.2020.102961
Pakulich DV, Yakimov SA, Alyamkin SA (2019) Age recognition from facial images using convolutional neural networks. Optoelectron Instrum Data Process 55:255–262. https://doi.org/10.3103/S8756699019030075
Pontes JK, Britto AS, Fookes C, Koerich AL (2016) A flexible hierarchical approach for facial age estimation based on multiple features. Pattern Recogn 54:34–51. https://doi.org/10.1016/j.patcog.2015.12.003
Ricanek K Jr, Tesafaye T (2006) MORPH: A longitudinal image Age-progression, of normal adult. Proc 7th Int Conf Autom Face Gesture Recognit 0–4. https://uncw.edu/oic/tech/morph.html. Accessed 06-10-2022
Sahoo TK, Banka H (2018) Multi-feature-based facial Age estimation using an incomplete facial aging database. Arab J Sci Eng 43:8057–8078. https://doi.org/10.1007/s13369-018-3293-0
Sawant MM, Bhurchandi K (2019) Hierarchical facial age estimation using Gaussian process regression. IEEE Access 7:9142–9152. https://doi.org/10.1109/ACCESS.2018.2889873
Shoba BT, Shatheesh Sam I (2022) Aging facial recognition for feature extraction using adaptive fully recurrent deep neural learning. Comput J bxab212. https://doi.org/10.1093/comjnl/bxab212
Tingting Y, Junqian W, Lintai W (2019) Yong X. Three-stage network for age estimation 4:122–126. https://doi.org/10.1049/trit.2019.0017
UMDAA-02 Face Dataset: https://umdaa02.github.io/. Accessed 06-10-2022
Wang S, Tao D, Yang J (2016) Relative attribute SVM+ learning for Age estimation. IEEE Trans Cybern 46:827–839. https://doi.org/10.1109/TCYB.2015.2416321
Xie JC, Pun CM (2019) Chronological Age estimation under the guidance of Age-related facial attributes. IEEE Trans Inf Forensics Secur 14:2500–2511. https://doi.org/10.1109/TIFS.2019.2902823
Yu N, Qian L, Huang Y, Wu Y (2019) Ensemble learning for facial Age estimation within non-ideal facial imagery. IEEE Access 7:97938–97948. https://doi.org/10.1109/ACCESS.2019.2928843
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors have no competing interests to declare that are relevant to the content of this article.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Shoba, V.B.T., Sam, I.S. Adaptive deep feature learning based Softmax regressive classification for aging facial recognition. Multimed Tools Appl 82, 22343–22371 (2023). https://doi.org/10.1007/s11042-022-14129-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-022-14129-8