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@article{176261,
        author = {Hemant Kumar Bhardwaj and Nitin Kumar and Suaib Saifi and Varun Tyagi and Aman Goswami},
        title = {Facial Micro-Expression Analysis Using Eye Landmark Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {7919-7924},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176261},
        abstract = {This paper addresses the challenge of facial micro-expression analysis in image sequences, with a focus on face landmark identification in the eye region. Various modern approaches for this task are examined, highlighting their key characteristics. The Spontaneous Actions and Micro-Movements (SAMM) dataset is selected for experiments due to its use of the Facial Action Coding System (FACS) for precise facial expression labelling. The study compares landmark detection using the widely known Active Shape Model (ASM) with a newly developed approach. The suggested approach enables efficient and accurate eye landmark detection with minimal computational resources. Key implementation stages of this method are outlined, demonstrating its advantages over ASM in terms of accuracy and simplicity.},
        keywords = {Active Shape Model (ASM), emotion recognition, eye detection, Facial Action Coding System (FACS), facial landmark detection, Micro-expressions, Spontaneous Actions and Micro-Movements (SAMM) dataset.},
        month = {May},
        }