Adaptive Face Recognition and Emotion Analysis Framework (AFREAF): A Real-Time Multi-Modal Deep Learning System for Comprehensive Facial Attribute Detection

Sayyan Shaikh; Prasanna Bammigatti; Rajesh Yakkundimath

doi:10.35940/ijsce.E3687.16030726

PDF

Published: 30-07-2026

DOI: https://doi.org/10.35940/ijsce.E3687.16030726

Keywords:

Facial Analysis, Emotion Recognition, Age Estimation, Gender Classification, Real-Time Processing, Deep Learning, Temporal Smoothing

Sayyan Shaikh

KLE Institute of Technology – Hubli.

https://orcid.org/0000-0001-5461-9165

Prasanna Bammigatti

Associate Professor, KLE Institute of Technology – Hubli

https://orcid.org/0009-0006-7191-6456

Rajesh Yakkundimath

Professor & Head, Department of CSE, KLE Institute of Technology – Hubli

https://orcid.org/0000-0002-7858-4267

Abstract

This paper presents the Adaptive Face Recognition and Emotion Analysis Framework (AFREAF), a comprehensive real-time system for simultaneous detection and analysis of multiple facial attributes including age, gender, and emotional states. AFREAF integrates advanced deep neural networks with adaptive preprocessing techniques, including face alignment using MediaPipe landmarks and histogram equalisation for enhanced feature extraction. The framework employs OpenCV's DNN module for robust face detection, specialised convolutional neural networks for age and gender classification, and the FER+ model for emotion recognition. A novel temporal smoothing algorithm ensures prediction stability across video frames. Experimental evaluation demonstrates that AFREAF achieves real-time performance at 28.4 FPS while maintaining competitive accuracy rates of 68.5% for age estimation, 94.2% for gender classification, and 71.3% for emotion recognition. The modular architecture facilitates easy integration into diverse applications including human-computer interaction, security systems, and behavioural analytics.

Downloads

Download data is not yet available.

Issue

Vol. 16 No. 3 (2026): Volume-16 Issue-3, July 2026

Section

Articles

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

CC-BY-NC-ND 4.0

How to Cite

[1]

Sayyan Shaikh, Prasanna Bammigatti, and Rajesh Yakkundimath, “Adaptive Face Recognition and Emotion Analysis Framework (AFREAF): A Real-Time Multi-Modal Deep Learning System for Comprehensive Facial Attribute Detection”, IJSCE, vol. 16, no. 3, pp. 10–14, Jul. 2026, doi: 10.35940/ijsce.E3687.16030726.

References

G. Levi and T. Hassner, "Age and gender classification using convolutional neural networks," in Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshops, 2015, pp. 34-42. https://openaccess.thecvf.com/content_cvpr_workshops_2015/W08/papers/Levi_Age_and_Gender_2015_CVPR_paper.pdf

R. Rothe, R. Timofte, and L. Van Gool, "Deep expectation of real and apparent age from a single image without facial landmarks," International Journal of Computer Vision, vol. 126, no. 2-4, pp. 144-157, 2018. DOI: https://doi.org/10.1007/s11263-016-0940-3

K. Zhang, C. Gao, L. Guo, M. Sun, X. Yuan, T. X. Han, Z. Zhao, and B. Li, "Age group and gender estimation in the wild with deep RoR architecture," IEEE Access, vol. 5, pp. 22492-22503, 2017. https://arxiv.org/pdf/1710.02985

X. Liu, S. Li, M. Kan, J. Zhang, S. Wu, W. Liu, H. Han, S. Shan, and X. Chen, "AgeNet: Deeply learned regressor and classifier for robust apparent age estimation," in Proc. IEEE Int. Conf. Computer Vision Workshops, 2015, pp. 16-24.

https://openaccess.thecvf.com/content_iccv_2015_workshops/w11/papers/Liu_AgeNet_Deeply_Learned_ICCV_2015_paper.pdf

I. J. Goodfellow et al., "Challenges in representation learning: A report on three machine learning contests," in Neural Information Processing, 2013, pp. 117-124. DOI: https://doi.org/10.1016/j.neunet.2014.09.005

A. Mollahosseini, B. Hasani, and M. H. Mahoor, "AffectNet: A database for facial expression, valence, and arousal computing in the wild," IEEE Transactions on Affective Computing, vol. 10, no. 1, pp. 18-31, 2017. DOI: https://doi.org/10.1109/TAFFC.2017.2740923

E. Barsoum, C. Zhang, C. C. Ferrer, and Z. Zhang, "Training deep networks for facial expression recognition with crowd-sourced label distribution," in Proc. ACM International Conference on Multimodal Interaction, 2016, pp. 279-283. DOI: https://doi.org/10.48550/arXiv.1608.01041

S. Li and W. Deng, "Deep facial expression recognition: A survey," IEEE Transactions on Affective Computing, vol. 13, no. 3, pp. 1195-1215, 2022. DOI: https://doi.org/10.1109/TAFFC.2020.2981446

K. Wang, X. Peng, J. Yang, S. Lu, and Y. Qiao, "Suppressing uncertainties for large-scale facial expression recognition," in Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2020, pp. 6897-6906. https://openaccess.thecvf.com/content_CVPR_2020/papers/Wang_Suppressing_Uncertainties_for_Large-Scale_Facial_Expression_Recognition_CVPR_2020_paper.pdf

Z. Zhang, P. Luo, C. C. Loy, and X. Tang, "Learning deep representation for face alignment with auxiliary attributes," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 5, pp. 918-930, 2016. DOI: https://doi.org/10.1109/TPAMI.2015.2469286

B. C. Chen, C. S. Chen, and W. H. Hsu, "Cross-age reference coding for age-invariant face recognition and retrieval," in Proc. European Conference on Computer Vision, 2014, pp. 768-783. https://link.springer.com/chapter/10.1007/978-3-319-10599-4_49

Article Sidebar

Main Article Content