Facial Age Estimation Using Multi-Stage Deep Neural Networks
Abstract
:1. Introduction
- We introduce a straightforward yet effective architecture that leverages channels at various stages of a deep neural network within the regression module.
- Our approach is implemented with two distinct CNN architectures: EfficientNet and MobileNetV3. Notably, this scalable method can be adapted to work with any CNN architecture.
- We conduct experiments using three public datasets: MORPH2, CACD, and AFAD. We provide an extensive comparison with several leading-edge methods, demonstrating that age estimation accuracy can be enhanced using the same backbone network without incurring additional costs.
2. Related Work
3. Methodology
3.1. Preprocessing
3.2. Proposed Architecture: Multi-Stage Deep Neural Nets (MSDNN)
3.2.1. Efficientnet
3.2.2. Mobilenetv3
3.3. Loss Function Used
4. Performance Evaluation
4.1. Datasets
4.2. Evaluation Metrics
4.3. Implementation
4.4. Comparison with State-of-the-Art Methods
4.5. Ablation Study
4.6. Cross-Dataset Evaluation
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Zhao, W.; Chellappa, R.; Phillips, P.J.; Rosenfeld, A. Face recognition: A literature survey. ACM Comput. Surv. (CSUR) 2003, 35, 399–458. [Google Scholar] [CrossRef]
- Jain, A.K.; Li, S.Z. Handbook of Face Recognition; Springer: Berlin/Heidelberg, Germany, 2011; Volume 1. [Google Scholar]
- Bekhouche, S.E. Facial Soft Biometrics: Extracting Demographic Traits. Ph.D. Thesis, Faculté des Sciences et Technologies, Vandoeuvre-lès-Nancy, France, 2017. [Google Scholar]
- Guehairia, O.; Dornaika, F.; Ouamane, A.; Taleb-Ahmed, A. Facial Age Estimation Using Tensor Based Subspace Learning and Deep Random Forests. Inf. Sci. 2022, 609, 1309–1317. [Google Scholar] [CrossRef]
- Kim, S.; Kim, H.; Lee, E.S.; Lim, C.; Lee, J. Risk score-embedded deep learning for biological age estimation: Development and validation. Inf. Sci. 2022, 586, 628–643. [Google Scholar] [CrossRef]
- Lanitis, A.; Draganova, C.; Christodoulou, C. Comparing different classifiers for automatic age estimation. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 2004, 34, 621–628. [Google Scholar] [CrossRef] [PubMed]
- Fu, Y.; Guo, G.; Huang, T.S. Age synthesis and estimation via faces: A survey. IEEE Trans. Pattern Anal. Mach. Intell. 2010, 32, 1955–1976. [Google Scholar] [PubMed]
- Asif, M.K.; Nambiar, P.; Ibrahim, N.; Al-Amery, S.M.; Khan, I.M. Three-dimensional image analysis of developing mandibular third molars apices for age estimation: A study using CBCT data enhanced with Mimics & 3-Matics software. Leg. Med. 2019, 39, 9–14. [Google Scholar] [CrossRef]
- Liu, S.; Huang, S.; Fu, W.; Lin, J.C.W. A descriptive human visual cognitive strategy using graph neural network for facial expression recognition. Int. J. Mach. Learn. Cybern. 2024, 15, 19–35. [Google Scholar] [CrossRef]
- Ricanek, K.; Tesafaye, T. Morph: A Longitudinal Image Database of Normal Adult Age-Progression. In Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition (FGR06), Southampton, UK, 10–12 April 2006; pp. 341–345. [Google Scholar]
- Guo, G.; Mu, G. A framework for joint estimation of age, gender and ethnicity on a large database. Image Vis. Comput. 2014, 32, 761–770. [Google Scholar] [CrossRef]
- Rothe, R.; Timofte, R.; Van Gool, L. Deep expectation of real and apparent age from a single image without facial landmarks. Int. J. Comput. Vis. 2018, 126, 144–157. [Google Scholar] [CrossRef]
- 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, Boston, MA, USA, 7–12 June 2015; pp. 34–42. [Google Scholar]
- Wang, X.; Guo, R.; Kambhamettu, C. Deeply-Learned Feature for Age Estimation. In Proceedings of the 2015 IEEE Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA, 5–9 January 2015; pp. 534–541. [Google Scholar]
- Tan, M.; Le, Q. Efficientnet: Rethinking Model Scaling for Convolutional Neural Networks. In Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA, 9–15 June 2019; pp. 6105–6114. [Google Scholar]
- Howard, T.C. Why Race and Culture Matter in Schools: Closing the Achievement Gap in America’s Classrooms; Teachers College Press: New York, NY, USA, 2019. [Google Scholar]
- Aonty, S.S.; Deb, K.; Sarker, I.H. Attention-Based Human Age Estimation from Face Images to Enhance Public Security. Data 2023, 8, 145. [Google Scholar] [CrossRef]
- Bekhouche, S.E.; Ouafi, A.; Taleb-Ahmed, A.; Hadid, A.; Benlamoudi, A. Facial Age Estimation using BSIF and LBP. In Proceedings of the First International Conference on Electrical Engineering ICEEB’14, Biskra, Algeria, 7–8 December 2014. [Google Scholar]
- Ren, H.; Li, Z.N. Age Estimation Based on Complexity-Aware Features. In Proceedings of the Asian Conference on Computer Vision, Singapore, 1–5 November 2014; pp. 115–128. [Google Scholar]
- Tian, Q.; Cao, M.; Chen, S.; Yin, H. Relationships Self-Learning Based Gender-Aware Age Estimation. Neural Process. Lett. 2019, 50, 2141–2160. [Google Scholar] [CrossRef]
- Dornaika, F.; Arganda-Carreras, I.; Belver, C. Age estimation in facial images through transfer learning. Mach. Vis. Appl. 2019, 30, 177–187. [Google Scholar] [CrossRef]
- Bekhouche, S.E.; Ouafi, A.; Benlamoudi, A.; Taleb-Ahmed, A.; Hadid, A. Facial Age Estimation and Gender Classification using Multi Level Local Phase Quantization. In Proceedings of the 2015 3rd International Conference on Control, Engineering & Information Technology (CEIT), Tlemcen, Algeria, 25–27 May 2015; pp. 1–4. [Google Scholar]
- Guo, G.; Mu, G.; Fu, Y.; Huang, T.S. Human Age Estimation using Bio-Inspired Features. In Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, 20–25 June 2009; pp. 112–119. [Google Scholar]
- Guo, G.; Mu, G. Simultaneous Dimensionality Reduction and Human Age Estimation via Kernel Partial Least Squares Regression. In Proceedings of the CVPR, Colorado Springs, CO, USA, 20–25 June 2011; pp. 657–664. [Google Scholar]
- Wu, Y.; Hu, H.; Li, H. Age-invariant face recognition using coupled similarity reference coding. Neural Process. Lett. 2019, 50, 397–411. [Google Scholar] [CrossRef]
- Kong, C.; Wang, H.; Luo, Q.; Mao, R.; Chen, G. Deep Multi-Input Multi-Stream Ordinal Model for age estimation: Based on spatial attention learning. Future Gener. Comput. Syst. 2023, 140, 173–184. [Google Scholar] [CrossRef]
- Shen, W.; Guo, Y.; Wang, Y.; Zhao, K.; Wang, B.; Yuille, A.L. Deep Regression Forests for Age Estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–23 June 2018; pp. 2304–2313. [Google Scholar]
- Wang, Z.; Tang, X.; Luo, W.; Gao, S. Face Aging with Identity-Preserved Conditional Generative Adversarial Networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–23 June 2018; pp. 7939–7947. [Google Scholar]
- Dornaika, F.; Bekhouche, S.E.; Arganda-Carreras, I. Robust regression with deep CNNs for facial age estimation: An empirical study. Expert Syst. Appl. 2020, 141, 112942. [Google Scholar] [CrossRef]
- Akbari, A.; Awais, M.; Bashar, M.; Kittler, J. A Theoretical Insight Into the Effect of Loss Function for Deep Semantic-Preserving Learning. IEEE Trans. Neural Netw. Learn. Syst. 2021, 34, 1–15. [Google Scholar] [CrossRef] [PubMed]
- Van, E.; Hospital, R. Innovative Approaches to Clinical Diagnosis: Transfer Learning in Facial Image Classification for Celiac Disease Identification. Appl. Sci. 2024, 14, 6207. [Google Scholar] [CrossRef]
- Antipov, G.; Baccouche, M.; Berrani, S.A.; Dugelay, J.L. Apparent Age Estimation from Face Images Combining General and Children-Specialized Deep Learning Models. In Proceedings of the Conference on Computer Vision and Pattern Recognition Workshops, Las Vegas, NV, USA, 27–30 June 2016; pp. 96–104. [Google Scholar]
- Yang, T.Y.; Huang, Y.H.; Lin, Y.Y.; Hsiu, P.C.; Chuang, Y.Y. Ssr-net: A compact soft stagewise regression network for age estimation. In Proceedings of the IJCAI, Stockholm, Sweden, 13–19 July 2018; Volume 5, p. 7. [Google Scholar]
- Liu, X.; Li, S.; Kan, M.; Zhang, J.; Wu, S.; Liu, W.; Han, H.; Shan, S.; Chen, X. Agenet: Deeply Learned Regressor and Classifier for Robust Apparent Age Estimation. In Proceedings of the IEEE International Conference on Computer Vision Workshops, Santiago, Chile, 7–13 December 2015; pp. 16–24. [Google Scholar]
- ELKarazle, K.; Raman, V.; Then, P. Facial Age Estimation Using Machine Learning Techniques: An Overview. Big Data Cogn. Comput. 2022, 6, 128. [Google Scholar] [CrossRef]
- Kang, J.S.; Kim, C.S.; Lee, Y.W.; Cho, S.W.; Park, K.R. Age estimation robust to optical and motion blurring by deep residual CNN. Symmetry 2018, 10, 108. [Google Scholar] [CrossRef]
- Liu, X.; Zou, Y.; Kuang, H.; Ma, X. Face image age estimation based on data augmentation and lightweight convolutional neural network. Symmetry 2020, 12, 146. [Google Scholar] [CrossRef]
- Zhang, K.; Zhang, Z.; Li, Z.; Qiao, Y. Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process. Lett. 2016, 23, 1499–1503. [Google Scholar] [CrossRef]
- Yang, J.; Qian, H.; Zou, H.; Xie, L. Learning decomposed hierarchical feature for better transferability of deep models. Inf. Sci. 2021, 580, 385–397. [Google Scholar] [CrossRef]
- Zhou, J.; Gan, J.; Gao, W.; Liang, A. Image retrieval based on aggregated deep features weighted by regional significance and channel sensitivity. Inf. Sci. 2021, 577, 69–80. [Google Scholar] [CrossRef]
- Authors, V. Dental Age Estimation Using Deep Learning: A Comparative Survey. Computation 2023, 8, 145. [Google Scholar]
- Howard, A.G.; Zhu, M.; Chen, B.; Kalenichenko, D.; Wang, W.; Weyand, T.; Andreetto, M.; Adam, H. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv 2017, arXiv:1704.04861. [Google Scholar]
- Howard, A.; Sandler, M.; Chu, G.; Chen, L.C.; Chen, B.; Tan, M.; Wang, W.; Zhu, Y.; Pang, R.; Vasudevan, V.; et al. Searching for mobilenetv3. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Republic of Korea, 27 October–2 November 2019; pp. 1314–1324. [Google Scholar]
- Tan, M.; Chen, B.; Pang, R.; Vasudevan, V.; Sandler, M.; Howard, A.; Le, Q.V. Mnasnet: Platform-Aware Neural Architecture Search for Mobile. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 15–20 June 2019; pp. 2820–2828. [Google Scholar]
- Yang, T.J.; Howard, A.; Chen, B.; Zhang, X.; Go, A.; Sandler, M.; Sze, V.; Adam, H. Netadapt: Platform-Aware Neural Network Adaptation for Mobile Applications. In Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 8–14 September 2018; pp. 285–300. [Google Scholar]
- Cao, W.; Mirjalili, V.; Raschka, S. Rank consistent ordinal regression for neural networks with application to age estimation. Pattern Recognit. Lett. 2020, 140, 325–331. [Google Scholar] [CrossRef]
- Chen, B.C.; Chen, C.S.; Hsu, W.H. Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval. In Proceedings of the Computer Vision—ECCV 2014, Zurich, Switzerland, 6–12 September 2014; Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T., Eds.; Springer: Cham, Switzerland, 2014; pp. 768–783. [Google Scholar]
- Niu, Z.; Zhou, M.; Wang, L.; Gao, X.; Hua, G. Ordinal Regression with Multiple Output cnn for Age Estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 4920–4928. [Google Scholar]
- Akbari, A.; Awais, M.; Feng, Z.; Farooq, A.; Kittler, J. A Flatter Loss for Bias Mitigation in Cross-Dataset Facial Age Estimation. In Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, 10–15 January 2021; pp. 10629–10635. [Google Scholar]
- Dagher, I.; Barbara, D. Facial age estimation using pre-trained CNN and transfer learning. Multimed. Tools Appl. 2021, 80, 20369–20380. [Google Scholar] [CrossRef]
- Shi, X.; Cao, W.; Raschka, S. Deep neural networks for rank-consistent ordinal regression based on conditional probabilities. Pattern Anal. Appl. 2023, 26, 941–955. [Google Scholar] [CrossRef]
- Zeng, X.; Huang, J.; Ding, C. Soft-Ranking Label Encoding for Robust Facial Age Estimation. IEEE Access 2020, 8, 134209–134218. [Google Scholar] [CrossRef]
- Paplham, J.; Franc, V. A Call to Reflect on Evaluation Practices for Age Estimation: Comparative Analysis of the State-of-the-Art and a Unified Benchmark. arXiv 2023, arXiv:2307.04570. [Google Scholar]
- Tian, Q.; Cao, M.; Sun, H.; Qi, L.; Mao, J.; Cao, Y.; Tang, J. Facial age estimation with bilateral relationships exploitation. Neurocomputing 2021, 444, 158–169. [Google Scholar] [CrossRef]
- Zhang, B.; Bao, Y. Cross-dataset learning for age estimation. IEEE Access 2022, 10, 24048–24055. [Google Scholar] [CrossRef]
- Liu, H.; Sun, P.; Zhang, J.; Wu, S.; Yu, Z.; Sun, X. Similarity-aware and variational deep adversarial learning for robust facial age estimation. IEEE Trans. Multimed. 2020, 22, 1808–1822. [Google Scholar] [CrossRef]
- Xia, M.; Zhang, X.; Liu, W.; Weng, L.; Xu, Y. Multi-Stage Feature Constraints Learning for Age Estimation. IEEE Trans. Inf. Forensics Secur. 2020, 15, 2417–2428. [Google Scholar] [CrossRef]
- Akbari, A.; Awais, M.; Fatemifar, S.; Khalid, S.S.; Kittler, J. A Novel Ground Metric for Optimal Transport-Based Chronological Age Estimation. IEEE Trans. Cybern. 2021, 52, 9986–9999. [Google Scholar] [CrossRef] [PubMed]
- Wang, H.; Sanchez, V.; Li, C.T. Improving Face-Based Age Estimation with Attention-Based Dynamic Patch Fusion. IEEE Trans. Image Process. 2022, 31, 1084–1096. [Google Scholar] [CrossRef] [PubMed]
- Zhang, Z.; Song, Y.; Qi, H. Age Progression/Regression by Conditional Adversarial Autoencoder. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017; pp. 5810–5818. [Google Scholar]
- Or-El, R.; Sengupta, S.; Fried, O.; Shechtman, E.; Kemelmacher-Shlizerman, I. Lifespan Age Transformation Synthesis. In Proceedings of the European Conference on Computer Vision, Glasgow, UK, 23–28 August 2020; Springer: Berlin/Heidelberg, Germany, 2020; pp. 739–755. [Google Scholar]
- He, Z.; Kan, M.; Shan, S.; Chen, X. S2gan: Share Aging Factors across Ages and Share Aging Trends among Individuals. In Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, Republic of Korea, 27 October–2 November 2019; pp. 9440–9449. [Google Scholar]
- Cao, Z.; Ma, L.; Long, M.; Wang, J. Partial Adversarial Domain Adaptation. In Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 8–14 September 2018; pp. 135–150. [Google Scholar]
- You, K.; Long, M.; Cao, Z.; Wang, J.; Jordan, M.I. Universal Domain Adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 15–20 June 2019; pp. 2720–2729. [Google Scholar]
- Saito, K.; Yamamoto, S.; Ushiku, Y.; Harada, T. Open Set Domain Adaptation by Backpropagation. In Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 8–14 September 2018; pp. 153–168. [Google Scholar]
- Boris, C.; Sadek, A.; Wolf, C. Universal Domain Adaptation in Ordinal Regression. arXiv 2021, arXiv:2106.11576. [Google Scholar]
- Saito, K.; Kim, D.; Sclaroff, S.; Saenko, K. Universal domain adaptation through self supervision. arXiv 2020, arXiv:2002.07953. [Google Scholar]
- Li, Z.; Jiang, R.; Aarabi, P. Continuous Face Aging via Self-Estimated Residual Age Embedding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA, 20–25 June 2021; pp. 15008–15017. [Google Scholar]
Approach | MORPH2 |
---|---|
SGD [49] | 5.69 |
OR-CNN [48] | 3.27 |
Google-Net [50] | 2.94 |
CORN [51] | 2.98 |
Soft-Ranking [52] | 2.83 |
IMDB-WIKI (ResNet-50) [53] | 2.81 |
deep-JREAE [54] | 2.77 |
CDCNN [55] | 2.76 |
Adaptive-CNN [29] | 2.75 |
SADAL [56] | 2.75 |
MSFCL [57] | 2.73 |
Wasserstein [58] | 2.71 |
CORAL-CNN [46] | 2.64 |
ADPF [59] | 2.71 |
Ours | 2.59 |
Approach | CACD |
CAAE [60] | 44.20 |
Lifespan [61] | 11.70 |
IPCGAN [28] | 9.10 |
GAN [62] | 8.40 |
PADA [63] | 7.71 |
UAN [64] | 7.57 |
OPDA-BP [65] | 7.48 |
ORUDA [66] | 7.26 |
DANCE [67] | 7.13 |
Self-Estimate [68] | 6.70 |
ADPF [59] | 5.39 |
CORAL-CNN [46] | 5.25 |
Ours | 4.90 |
Approach | AFAD |
PADA [63] | 7.11 |
OPDA-BP [65] | 6.84 |
UAN [64] | 6.73 |
DANCE [67] | 6.25 |
ORUDA [66] | 6.19 |
CNN + LSVR [14] | 5.56 |
CORAL-CNN [46] | 3.47 |
OR-CNN [48] | 3.34 |
CDCNN [55] | 3.30 |
Ours | 3.25 |
Backbone | MSDNN | Params | MAE (Years) | Time |
---|---|---|---|---|
EfficientNet | ✕ | 8,693,521 | 2.74 | 16.1 ms |
✓ | 8,693,825 | 2.59 | 16.2 ms | |
MobileNetV3 | ✕ | 4,399,945 | 2.93 | 13.0 ms |
✓ | 4,400,193 | 2.72 | 13.0 ms |
Train | Test | MAE | CS |
---|---|---|---|
MORPH2 | CACD | 10.62 | 26.23% |
AFAD | 7.15 | 40.22% | |
CACD | MORPH2 | 8.38 | 33.41% |
AFAD | 9.54 | 33.89% | |
AFAD | MORPH2 | 8.10 | 41.89% |
CACD | 9.59 | 29.42% |
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Bekhouche, S.E.; Benlamoudi, A.; Dornaika, F.; Telli, H.; Bounab, Y. Facial Age Estimation Using Multi-Stage Deep Neural Networks. Electronics 2024, 13, 3259. https://doi.org/10.3390/electronics13163259
Bekhouche SE, Benlamoudi A, Dornaika F, Telli H, Bounab Y. Facial Age Estimation Using Multi-Stage Deep Neural Networks. Electronics. 2024; 13(16):3259. https://doi.org/10.3390/electronics13163259
Chicago/Turabian StyleBekhouche, Salah Eddine, Azeddine Benlamoudi, Fadi Dornaika, Hichem Telli, and Yazid Bounab. 2024. "Facial Age Estimation Using Multi-Stage Deep Neural Networks" Electronics 13, no. 16: 3259. https://doi.org/10.3390/electronics13163259