| Proj No. | A3234-251 |
| Title | Facial Emotion Recognition Using Deep Learning |
| Summary | Facial Emotion Recognition (FER) is increasingly important in applications such as healthcare, education, driver monitoring, and human–computer interaction, yet real-world conditions pose major challenges such as head pose variation, partial occlusion from masks or glasses, illumination variations, and micro expressions. This project aims to benchmark and compare SOTA FER models across multiple datasets and simulated occlusion scenarios, analyzing their strengths and weaknesses to determine which approach best balances accuracy, robustness, and computational efficiency, guiding the development of more reliable FER systems for practical deployment. |
| Supervisor | A/P Yap Kim Hui (Loc:S2 > S2 B2B > S2 B2B 53, Ext: +65 67904339) |
| Co-Supervisor | - |
| RI Co-Supervisor | - |
| Lab | Computer Engineering II (Loc: S2-B3b-08) |
| Single/Group: | Single |
| Area: | Digital Media Processing and Computer Engineering |
| ISP/RI/SMP/SCP?: |