Call For Paper Volume: V, Issue: 08 | AUGUST 2026 | International Journal of Advanced Trends in Engineering and Management (IJATEM)
Volume V | Issue 7 | 2026 | Paper ID: IJATEM-V05I07P2

A Seagull Optimization Algorithm Enhanced EfficientNet-B0 Framework for Accurate Facial Expression Recognition

, R. Sahila Devi

In recent days, Facial expression recognition (FER) has a major role in machine learning to identify change of emotions in human being. Nevertheless, due to individual variances and changes in intensity of emotion, it is hard to gather precise hand-crafted elements that are closely linked to changes in expression such as illumination, occlusion, head pose and subtle variations. Hence, features that accurately details about facial expression change in human is required. To overcome the limitations, this study proposes a Seagull Optimization Algorithm (SOA) with Enhanced EfficientNet-B0 framework for precise FER. Data preprocessing is performed using Median Filter. Enhance EfficientNet-B0 approaches uses compound scaling for effective balance of network depth, weight and input resolution. Hence, to extract feature by learning discriminative and hierarchical facial representation thereby maintaining computational complexity, Enhanced EfficientNet-B0 is deployed. Optimizing model parameters is performed using SOA thereby improving discriminative capability of extracted feature. Feature learning and classification process of EfficentNet-B0 is contributed using SOA which effectively aids in searching optimal model parameters. Performance of proposed model is identified using standardized parameters such as precision, recall, accuracy and F1-score in which, values are 94%, 94%, 94% and 94% respectively.

Facial expression recognition (FER), Seagull Optimization algorithm (SOA), Enhanced EfficientNet-B0, Median Filter, K-means clustering.

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