Impact of Image Quality Enhancement Using Homomorphic Filtering on the Performance of Deep Learning-Based Facial Emotion Recognition Systems

Authors

  • Al Bahri Department of Electrical and Computer Engineering, Universitas Syiah Kuala
  • Maulisa Oktiana Department of Electrical and Computer Engineering, Universitas Syiah Kuala
  • Maya Fitria Department of Electrical and Computer Engineering, Universitas Syiah Kuala https://orcid.org/0000-0002-6593-212X
  • Zulfikar Zulfikar Department of Electrical and Computer Engineering, Universitas Syiah Kuala

DOI:

https://doi.org/10.26555/jiteki.v11i2.30409

Keywords:

Facial Emotion Recognition, Image Enhancement, Deep learning, MobileNet, InceptionV3, DenseNet121

Abstract

Facial emotion recognition technology is crucial in understanding human expressions from images or videos by analyzing distinct facial features.  A common challenge in this technology is accurately detecting a person's facial expression, which can be hindered by unclear facial lines, often due to poor lighting conditions. To address these challenges, it is essential to improve image quality. This study investigates how enhancing image quality through homomorphic filtering and sharpening techniques can boost the accuracy and performance of deep learning-based facial emotion recognition systems. Improved image quality allows the classification model to focus on relevant expression features better.  Therefore, this research contributes to in facilitating more intuitive and responsive communications by enabling system to interpret and respond to human emotions effectively. The testing used three different architectures: MobileNet, InceptionV3, and DenseNet121. Evaluasi kinerja dilakukan menggunakan metrik akurasi, presisi, recall, dan F1-score. Experimental results indicated that image enhancement positively impacts the accuracy of the facial emotion recognition system. Specifically, the average accuracy increased by 1-2% for the MobileNet architecture, by 5-7% for InceptionV3, and by 1-3% for DenseNet121.

References

[1] S. Dwijayanti, M. Iqbal, and B. Y. Suprapto, “Real-Time Implementation of Face Recognition and Emotion Recognition in a Humanoid Robot Using a Convolutional Neural Network,” IEEE Access, vol. 10, pp. 89876–89886, 2022, https://doi.org/10.1109/ACCESS.2022.3200762.

[2] N. Zhou, R. Liang and W. Shi, "A Lightweight Convolutional Neural Network for Real-Time Facial Expression Detection," in IEEE Access, vol. 9, pp. 5573-5584, 2021, https://doi.org/10.1109/ACCESS.2020.3046715.

[3] A. Dutta, H. Singh, A. Majumdar, A. Maiti, “Improved Facial Emotion Recognition using Convolutional Neural Network,” Conference: The 14th Inter-University Engineering, Science & Technology Academic, 2024, https://www.researchgate.net/publication/381772613_Improved_Facial_Emotion_Recognition_using_Convolutional_Neural_Network.

[4] R. Sowmiya, G. Sivakamasundari, V. Archana, "Facial Emotion Recognition using Deep Learning Approach," International Conference on Automation, Computing and Renewable Systems (ICACRS), pp. 1064-1069, 2022, https://doi.org/10.1109/ICACRS55517.2022.10029092.

[5] F. Zhao, J. Li, L. Zhang, Z. Li, S. G. Na, ”Multi-view face recognition using deep neural networks,” Future Generation Computer Systems, vol. 111, pp. 75-380, 2020, https://doi.org/10.1016/j.future.2020.05.002.

[6] L. Zahara, P. Musa, E. P. Wibowo, I. Karim, and S. B. Musa, “The Facial Emotion Recognition (FER-2013) Dataset for Prediction System of Micro-Expressions Face Using the Convolutional Neural Network (CNN) Algorithm based Raspberry Pi,” in Fifth International Conference on Informatics and Computing (ICIC), pp. 1–9, 2020, https://doi.org/10.1109/ICIC50835.2020.9288560.

[7] S. Li and W. Deng, “Deep Facial Expression Recognition: A Survey,” in Proceedings of the International Conference on Image Processing (ICIP), pp. 2889–2893, 2020, https://doi.org/10.1109/ICIP40778.2020.9191247.

[8] A. Singh, M. Kaur, “Deep Residual Networks Including Transfer Learning for Facial Emotion Identification,” In International Conference on Cognitive Computing and Cyber Physical Systems, pp. 29-39, 2025, https://doi.org/10.1007/978-981-97-7371-8_3.

[9] P. Giannopoulos, I. Perikos, I. Hatzilygeroudis, “Deep Learning Approaches for Facial Emotion Recognition: A Case Study on FER-2013,” In Advances in hybridization of intelligent methods: Models, systems and applications, pp. 1-16, 2007, https://doi.org/10.1007/978-3-319-66790-4_1.

[10] S. Daeng, “Pengaruh Histogram Equalization untuk Perbaikan,” Jurnal Simetris, vol. 7, no. 1, pp. 177–182, 2016, https://doi.org/10.24176/simet.v7i1.502.

[11] R. Al Sobbahi and J. Tekli, “Low-light homomorphic filtering network for integrating image enhancement and classification,” Signal Process Image Commun, vol. 100, p. 116527, 2022, https://doi.org/10.1016/j.image.2021.116527 .

[12] N. A. Olobo, ”Image Contrast Enhancement Using General Histogram Equalization and Homomorphic,” Filtering. Asian Journal of Science, Technology, Engineering, and Art, vol. 3, no. 1, pp. 1-31, 2024, https://doi.org/10.58578/ajstea.v3i1.4244.

[13] F. Arnia, M. Oktiana, K. Saddami, K. Munadi, R. Roslidar, and B. Pradhan, “Homomorphic Filtering and Phase-Based Matching for Cross-Spectral Cross-Distance Face Recognition,” Sensorsvol. 21, no. 13, p. 4575, Jul. 2021, https://doi.org/10.3390/S21134575.

[14] M. Oktiana et al., “Cross-spectral iris recognition using phase-based matching and homomorphic filtering,” Heliyon, vol. 6, no. 2, Feb. 2020, https://www.cell.com/heliyon/fulltext/S2405-8440(20)30252-8.

[15] Z. Yue, F. Yanyan, Z. Shangyou and P. Bing, "Facial Expression Recognition Based on Convolutional Neural Network," IEEE 10th International Conference on Software Engineering and Service Science (ICSESS), pp. 410-413, 2019, https://doi.org/10.1109/ICSESS47205.2019.9040730.

[16] Y.-T. Han, G.-J. Lin, L.-J. Zhao, X.-L. Tang, Y. Huang, and H. Jiang, “An Improved Homomorphic Filtering Algorithm for Face Image Preprocessing,” J Comput (Taipei), vol. 32, no. 6, pp. 66–82, 2021, http://www.csroc.org.tw/journal/JOC32-6/JOC3206-06.pdf.

[17] A. Thamizharasi, “Performance analysis of face recognition by combining multiscale techniques and homomorphic filter using fuzzy K nearest neighbour classifier,” in International Conference on Communication Control and Computing Technologies, pp. 394–401, 2010, https://doi.org/10.1109/ICCCCT.2010.5670584.

[18] H. Hafidz, A. Ananda, M. Akbar, “Perbaikan Citra RGB dengan Metode Homomorphic Filtering Menggunakan Butterworth Filter| Jurnal Komputer Terapan,” Jurnal Komputer Terapan, vol. 1, no. 1, pp. 1-9, 2015, https://web.archive.org/web/20180426023059id_/https://jurnal.pcr.ac.id/index.php/jkt/article/viewFile/6/5.

[19] P. Musa et al.,” Pembelajaran Mendalam Pengklasifikasi Ekspresi Wajah Manusia dengan Model Arsitektur Xception pada Metode Convolutional Neural Network,” Rekayasa, vol. 16, no. 1, pp. 65-73. 2023, https://doi.org/10.21107/rekayasa.v16i1.16974

[20] P. A. Nugroho, I. Fenriana, R. Arijanto, “Implementasi Deep Learning Menggunakan Convolutional Neural Network (CNN) Pada Ekspresi Manusia,” In Journal of Physics: Conference Series, vol. 2, no. 1, pp. 12–20, 2020, https://jurnal.buddhidharma.ac.id/index.php/algor/article/view/441.

[21] M. Muhajir et al., “USK-FEMO: A Face Emotion Dataset using Deep Learning for Effective Learning,” Proceeding 2nd International Conference on Computer System, Information Technology, and Electrical Engineering: Sustainable Development for Smart Innovation System, COSITE 2023, pp. 199–203, 2023, https://doi.org/10.1109/COSITE60233.2023.10249834.

[22] T. I. Rizky, N. A. Hasibuan, and R. Syahputra, “RGB Image Improvement Using Homomorphic Filtering Method with Butterworth Filter,” Komik (Konferensi Nasional Teknologi Informasi dan Komputer), vol. 3, no. 1, pp. 41–48, 2019, https://doi.org/10.30865/Komik.V3i1.1565.

[23] M. J. Seow and V. K. Asari, “Ratio Rule and Homomorphic Filter for Enhancement of Digital Colour Image,” Neurocomputing, vol. 69, no. 7–9, pp. 954–958, 2006, https://doi.org/10.1016/J.Neucom.2005.07.003.

[24] P. Yugander, C. H. Tejaswini, J. Meenakshi, K. S. Kumar, B. V. N. S. Varma, and M. Jagannath, “MR Image Enhancement Using Adaptive Weighted Mean Filtering and Homomorphic Filtering,” Procedia Comput Sci, vol. 167, pp. 677–685, 2020, https://doi.org/10.1016/J.Procs.2020.03.334.

[25] A. Chavarin, E. Cuevas, O. Avalos, J. Galvez, and M. Perez-Cisneros, “Contrast Enhancement in Images by Homomorphic Filtering and Cluster-Chaotic Optimization,” IEEE Access, vol. 11, pp. 73803–73822, 2023, https://doi.org/10.1109/Access.2023.3287559.

[26] U. Hairah, M. Wati, A.P. A. Masa, A. Septiarini, N. Puspitasari, O. Gultom, “Enhanced Corn Leaf Disease Classification Using DenseNet121 and Hybrid Machine Learning Models,” International Conference on Mechatronics Engineering (ICOM), pp. 417-422, 2024, https://doi.org/10.1109/ICOM61675.2024.10652337.

[27] A. Priyangka, I. M. S. Kumara, “Classification of rice plant diseases using the convolutional neural network method,” Jurnal Ilmiah Teknologi Informasi, vol. 12, no. 2, p. 123, 2021, https://pdfs.semanticscholar.org/45b7/280b0a93d2a70870e22864cbf7be3ace9036.pdf.

[28] D. A. Dharmawan, L. Listyalina, “Performance Analysis of Lung Cancer Diagnosis Algorithms on X-Ray Images,” Journal of Electrical Technology, vol. 2, no. 2, pp. 34-40, 2018, https://journal.umy.ac.id/index.php/jet/article/view/7429

[29] K. V. Jamuna, V. B. Ambhore, A. Amudha, J. Singh, N. T. Velusudha and N. Vashisht, "A Comparative Analysis of Noise Reduction Techniques in Magnetic Resonance Imaging," International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1-6, 2024, https://doi.org/10.1109/ICCCNT61001.2024.10724152.

[30] W. Jiang and D. Xue, “Infrared Image Enhancement for Photovoltaic Panels Based on Improved Homomorphic Filtering and CLAHE,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14495, pp. 348–361, 2024, https://doi.org/10.1007/978-3-031-50069-5_29.

[31] A. Malik et al., “Design and Evaluation of a Hybrid Technique for Detecting Sunflower Leaf Disease Using Deep Learning Approach,” Journal of Food Quality, p. 9211700, 2022, https://doi.org/10.1155/2022/9211700.

[32] X. Fu and X. Cao, “Underwater Image Enhancement with Global–Local Networks and Compressed-Histogram Equalization,” Signal Process Image Commun, vol. 86, p. 115892, 2020, https://doi.org/10.1016/J.Image.2020.115892.

[33] I. N. Purnama, “Herbal Plant Detection Based On Leaves Image Using Convolutional Neural Network With Mobile Net Architecture,” Jurnal Ilmu Pengetahuan Dan Teknologi Komputer), vol. 6, no. 1, pp. 27-32. 2020, https://doi.org/10.33480/jitk.v6i1.1400.

[34] Y. ; Hu et al., “Detail Enhancement Multi-Exposure Image Fusion Based on Homomorphic Filtering,” Electronics, vol. 11, no. 8, p. 1211, Apr. 2022, https://doi.org/10.3390/ELECTRONICS11081211.

[35] S. Gamini and S. S. Kumar, “Homomorphic filtering for the image enhancement based on fractional-order derivative and genetic algorithm,” Computers and Electrical Engineering, vol. 106, p. 108566, Mar. 2023, https://doi.org/10.1016/J.COMPELECENG.2022.108566.

[36] S. D. Lalitha and K. K. Thyagharajan, "Micro-Facial Expression Recognition Based on Deep-Rooted Learning Algorithm," arXiv preprint arXiv:2009.05778, 2020. [Online]. Available: https://arxiv.org/abs/2009.05778.

[37] Y. Hu et al., "Detail Enhancement Multi-Exposure Image Fusion Based on Homomorphic Filtering," Electronics, vol. 11, no. 8, p. 1211, Apr. 2022. [Online]. Available: https://www.mdpi.com/2079-9292/11/8/1211.

[38] M. Bentoumi, M. Daoud, M. Benaouali, and A. Taleb-Ahmed, "Improvement of Emotion Recognition from Facial Images Using Deep Learning and Early Stopping Cross Validation," Multimedia Tools and Applications, vol. 81, pp. 29887–29917, 2022. [Online]. Available: https://link.springer.com/article/10.1007/s11042-022-12058-0.

[39] G. Balachandran, S. Ranjith, T. R. Chenthil, and G. C. Jagan, "Facial Expression-Based Emotion Recognition Across Diverse Age Groups: A Multi-Scale Vision Transformer with Contrastive Learning Approach," Journal of Combinatorial Optimization, vol. 49, 2025. [Online]. Available: https://link.springer.com/article/10.1007/s10878-024-01241-8.

[40] S. Li and W. Deng, "Reliable Crowdsourcing and Deep Locality-Preserving Learning for Unconstrained Facial Expression Recognition," IEEE Transactions on Image Processing, vol. 28, no. 1, pp. 356–370, 2019. [Online]. Available: https://doi.org/10.1109/TIP.2018.2868382.

[41] Y. Han, G. Lin, L. Zhao, X. Tang, Y. Huang, and H. Jiang, "An Improved Homomorphic Filtering Algorithm for Face Image Preprocessing," Journal of Computational Science, vol. 32, pp. 102–113, 2021. [Online]. Available: http://www.csroc.org.tw/journal/JOC32-6/JOC3206-06.pdf.

[42] S. S. Jarande, P. K. Kadbe and A. W. Bhagat, "Comparative analysis of image enhancement techniques," International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), pp. 307-309, 2016, https://doi.org/10.1109/ICEEOT.2016.7755249.

[43] G. Balachandran, S. Ranjith, T. R. Chenthil, and G. C. Jagan, “Facial expression-based emotion recognition across diverse age groups: a multi-scale vision transformer with contrastive learning approach,” Journal of Combinatorial Optimization, vol. 49, no. 1, Dec. 2024, https://doi.org/10.1007/s10878-024-01241-8.

[44] C. Saravanan, M. Poonkodi, and P. Sankar, “Comprehensive Exploration of Facial Emotion Recognition using Conventional Machine Learning and Transfer learning Models,” Research Square (Research Square), pp. 161-172, 2024, https://doi.org/10.21203/rs.3.rs-4304090/v1.

[45] S. Bobojanov, B. M. Kim, M. Arabboev, and S. Begmatov, “Comparative analysis of vision transformer models for facial emotion recognition using augmented balanced datasets,” Applied Sciences, vol. 13, no. 22, p. 12271, Nov. 2023, https://doi.org/10.3390/app132212271.

[46] N. Yalçin and M. Alisawi, “Introducing a Novel Dataset for Facial Emotion Recognition and Demonstrating Significant Enhancements in Deep Learning Performance through Pre-processing Techniques,” Heliyon, vol. 10, no. 20, p. e38913, Oct. 2024, https://doi.org/10.1016/j.heliyon.2024.e38913.

[47] S. Roka, D. B. Rawat, “Fine tuning Vision Transformer model for facial Emotion recognition: Performance Analysis for Human-Machine Teaming,” 24th International Conference on Information Reuse and Integration for Data Science (IRI), pp. 134-139, 2023, https://doi.org/10.1109/IRI58017.2023.00030.

[48] A. Chaudhari, C. Bhatt, A. Krishna, and P. L. Mazzeo, “ViTFER: Facial Emotion Recognition with Vision Transformers,” Applied System Innovation, vol. 5, no. 4, p. 80, Aug. 2022, https://doi.org/10.3390/asi5040080.

[49] J. Soni, N. Prabakar, and H. Upadhyay, “Vision Transformer-Based Emotion Detection in HCI for enhanced interaction,” in Lecture notes in computer science, pp. 76–86, 2024, https://doi.org/10.1007/978-3-031-53827-8_8.

[50] A. K. Roy, H. K. Kathania, and A. Sharma, “Improvement in Facial Emotion Recognition using Synthetic Data Generated by Diffusion Model,” arXiv.org, Nov. 16, 2024. https://arxiv.org/abs/2411.10863.

[51] N. N. Urnisha, S. I. Bithi, M. M. S. Rafee, N. I. Remon, M. M. Hasan, P. R. Chowdhury, “A Transfer Learning Approach for Facial Emotion Recognition Using a Deep Learning Model,” International journal of research and scientific innovation, vol. 11, no. 4, pp. 274-284, 2024, https://doi.org/10.51244/IJRSI.2024.1104022.

[52] A. Chowanda, “Implementing Vision Transformer to Model Emotions Recognition from Facial Expressions,” International Conference on Artificial Intelligence and Data Sciences (AiDAS), pp. 48-53, 2023, https://doi.org/10.1109/AiDAS60501.2023.10284712.

[53] Mejia-Escobar, C, Cazorla, M, Martinez-Martin, E. “Improving facial expression recognition through data preparation and merging,” IEEE Access, vol. 11, pp. 71339-71360, 2023, https://doi.org/10.1109/ACCESS.2023.3293728.

[54] R. Helaly, S. Messaoud, S. Bouaafia, M. A. Hajjaji, and A. Mtibaa, “DTL-I-ResNet18: facial emotion recognition based on deep transfer learning and improved ResNet18,” Signal Image and Video Processing, vol. 17, no. 6, pp. 2731–2744, Feb. 2023, https://doi.org/10.1007/s11760-023-02490-6.

[55] K. Desai, H. N. Patel, D. Dashlaniya, P. Desai, M. Shah and P. Goel, "A Transfer Learning Framework for Facial Emotion Recognition: Leveraging Pre-Trained Convolutional Neural Networks," 2nd International Conference on Advancement in Computation & Computer Technologies (InCACCT), pp. 663-667, 2024, https://doi.org/10.1109/InCACCT61598.2024.10551190.

[56] S. B. Punuri, S. K. Kuanar and T. K. Mishra, "Facial Emotion Recognition in Unconstrained Environments through Rank-Based Ensemble of Deep Learning Models using 1-Cycle Policy," 2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary Technology Management (IC-RVITM), pp. 1-8, 2023, https://doi.org/10.1109/IC-RVITM60032.2023.10435159.

[57] T. Dar, A. Javed, S. Bourouis, H. S. Hussein and H. Alshazly, "Efficient-SwishNet Based System for Facial Emotion Recognition," in IEEE Access, vol. 10, pp. 71311-71328, 2022, https://doi.org/10.1109/ACCESS.2022.3188730.

[58] U. Chinta and A. Atyabi, "Facial Expression Recognition Using Robust Attention-Based CNN," 2023 International Conference on Machine Learning and Applications (ICMLA), pp. 858-863, 2023, https://doi.org/10.1109/ICMLA58977.2023.00126.

[59] J. Wang, “Evaluation and analysis of visual perception using attention-enhanced computation in multimedia affective computing,” Frontiers in Neuroscience, vol. 18, Aug. 2024, https://doi.org/10.3389/fnins.2024.1449527.

[60] K. Zakieldin et al., “ViTCN: Hybrid Vision Transformer with Temporal Convolution for Multi-Emotion Recognition,” Int J Comput Intell Syst, vol. 17, p. 64, 2024, https://doi.org/10.1007/s44196-024-00436-5.

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2025-04-28

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[1]
A. Bahri, M. Oktiana, M. Fitria, and Z. Zulfikar, “Impact of Image Quality Enhancement Using Homomorphic Filtering on the Performance of Deep Learning-Based Facial Emotion Recognition Systems”, J. Ilm. Tek. Elektro Komput. Dan Inform, vol. 11, no. 2, pp. 206–224, Apr. 2025.

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Vol. 11 No. 2 (2025): June

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