International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Confidence-Calibrated Landmark Attention for Lightweight Facial Expression Recognition |
|---|---|
| ABSTRACT | Facial Expression Recognition (FER) must classify expressions with limited computational overhead, particularly on resource-constrained devices. Compact CNNs use depthwise-separable convolution and global average pooling, yet occlusion, blur, and lighting changes can make landmark estimates unreliable. This paper proposes Confidence-Calibrated Landmark Attention (CCLA), which combines landmark positions with per-point reliability scores to create a weighted spatial guide for a lightweight CNN. The evaluation plan covers accuracy, Macro-F1, model size, inference latency, and masked-face robustness. Quantitative findings will be added after the proposed experiments are run. |
| AUTHOR | SHANTHA KUMAR S, IMRANA SIDDIQUA S, DR. S MOHANA, DR. V PUNITHA Department of Computer Science and Engineering, Saranathan College of Engineering, Panjappur, Tamil Nadu, India Professor, Department of Computer Science and Engineering, Saranathan College of Engineering, Panjappur, Tamil Nadu, India Professor & Head, Department of Computer Science and Engineering, Saranathan College of Engineering, Panjappur, Tamil Nadu, India |
| VOLUME | 189 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1410006 |
| pdf/6_Confidence-Calibrated Landmark Attention for Lightweight Facial Expression Recognition.pdf | |
| KEYWORDS | |
| References |
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