Facial Emotion Recognition and Synthesis with Convolutional Neural Networks
Karkuzhali S1, Murugeshwari. R2 , Umadevi V3
1Karkuzhali S, Mepco Schlenk Engineering College, Sivakasi (Tamil Nadu), India.
2Murugeshwari. R, Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi (Tamil Nadu), India.
3Umadevi V, Department of Computer Science and Engineering, Mepco Schlenk Engineering College, Sivakasi (Tamil Nadu), India.
Manuscript received on 11 August 2025 | First Revised Manuscript received on 20 September 2025 | Second Revised Manuscript received on 17 January 2026 | Manuscript Accepted on 15 February 2026 | Manuscript published on 28 February 2026 | PP: 31-42 | Volume-14 Issue-3, February 2026 | Retrieval Number: 100.1/ijese.C255912030224 | DOI: 10.35940/ijese.F2559.14030226
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A crucial component of human communication is conveying emotions, intentions, and social signals. In this era of artificial intelligence and computer vision, the development of automated systems for facial expression synthesis and recognition has attracted significant attention due to their wide range of applications, including human-computer interaction, virtual reality, emotional analysis, and healthcare. This research focuses on integrating deep convolutional neural networks (CNNs) to address challenges in both facial expression synthesis and recognition. On the synthesis front, a generative CNN architecture is proposed to generate realistic facial expressions, enabling the generation of various emotional states from neutral faces. The network learns to capture the intricate details of human expressions, including subtle muscle movements and spatial relationships among facial features. For facial expression recognition, a separate CNN-based model is developed to classify the synthesised expressions accurately. The recognition model is trained on a large dataset of annotated facial expressions and is designed to handle real-world variations in lighting, pose, and occlusions. The CNN leverages its ability to automatically learn relevant features from raw image data, eliminating the need for manual feature engineering. The experimental results demonstrate the effectiveness of the proposed approach. The synthesized expressions exhibit a high degree of realism and diversity, effectively capturing the nuances of human emotions. The recognition model achieves state-of-the art accuracy in classifying these synthesised expressions, surpassing traditional methods and demonstrating the power of deep learning in this domain. This research advances automatic facial expression synthesis and recognition, with potential applications in human-computer interaction, affective computing, and virtual environments. The deep CNN-based approach offers a promising avenue for enhancing our understanding of human expressions and enabling more emotionally aware and responsive AI systems. The significance of emotion classification in human-machine interactions has grown significantly. Over the past decade, businesses have become increasingly attuned to the insights that analysing a person’s facial expressions in images or videos can provide into their emotional state. Various organizations are currently leveraging emotion recognition to gauge customer sentiments towards their products. The applications of this technology extend well beyond market research and digital advertising. Convolutional Neural Networks (CNNs) have emerged as a valuable tool for eliciting emotions from facial landmarks, as they can automatically extract relevant information. Challenges such as brightness variations, background changes, and other factors can be effectively mitigated by isolating the essential features using techniques such as face resizing and normalisation. However, it’s important to note that neural networks depend on extensive datasets for optimal performance. In cases where data availability is limited, strategies like data augmentation through techniques such as rotation can be employed to compensate. Additionally, fine tuning the CNN’s architecture can enhance its accuracy in predicting emotions. Consequently, this approach enables the real-time identification of seven distinct emotions – anger, sadness, happiness, disgust, neutrality, fear, and surprise – from facial expressions in images.
Keywords: Emotion Classification, Human-Machine Communication, Facial Expression Synthesis, Deep Convolutional Neural Network, Emotion Recognition.
Scope of the Article: Computer Science and Engineering