Facial expression recognition is vital in pattern recognition and affective computing. With the advancement of deep learning, its performance has improved, yet challenges remain in nonlaboratory environments due to occlusion, poor lighting, and varying head poses. This study explores a robust facial expression recognition approach using a CNN‐based model integrated with key point localization techniques. Instead of relying on a dense set of landmarks, the proposed method focuses on fewer but more informative expression key points. Each point is analyzed for local shape features, and contour consistency is verified using indexing along the normal direction. This strategy enhances robustness while reducing computational complexity. Specifically, the hybrid active shape model (ASM) + structure method significantly lowers the processing load compared to the traditional ASM approach. Experimental results demonstrate a 3.02% improvement in recognition accuracy over one‐to‐many SVM classifiers when dealing with clear facial images. Furthermore, the system shows strong resilience to partial occlusions and maintains real‐time performance, making it suitable for real‐world applications. The proposed framework highlights the importance of selecting effective key points and optimizing feature extraction to enhance both accuracy and efficiency in facial expression recognition tasks under challenging conditions.
Jun Li (2026) studied this question.