Chest region estimation using chin landmark keypoints for heart attack detection
This paper proposes a lightweight and noninvasive approach for localizing the external chest area by utilizing facial landmarks, aiming to support early detection of chest pain related to heart attacks. While most existing research focuses on internal chest imaging such as X-rays and MRI, external c...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
IEEE
2025
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| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/46045/ |
| _version_ | 1848827551637569536 |
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| author | Noraizan, Ibrahim Rohana, Abdul Karim Nurul Wahidah, Arshad Wan Nur Azhani, Wan Samsudin Marlina, Yakno |
| author_facet | Noraizan, Ibrahim Rohana, Abdul Karim Nurul Wahidah, Arshad Wan Nur Azhani, Wan Samsudin Marlina, Yakno |
| author_sort | Noraizan, Ibrahim |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | This paper proposes a lightweight and noninvasive approach for localizing the external chest area by utilizing facial landmarks, aiming to support early detection of chest pain related to heart attacks. While most existing research focuses on internal chest imaging such as X-rays and MRI, external chest localization remains largely unexplored. The challenge arises because areas around the chest, including the hands, face, and neck, often move slightly or change position, which can make it difficult for the system to consistently and accurately identify the chest location. To address these challenges, our approach focuses on accurately estimating the chin as a stable reference point using two techniques: Local Minima Detection based on grayscale intensity changes and Harris Corner Detection, known for its robustness in identifying geometric features. We evaluated both methods on six test samples, each consisting of 200 images captured under controlled conditions. The results show that Harris Corner Detection achieves a higher peak accuracy of 85%, outperforming the Local Minima method at 80%. This improved performance is mainly due to Harris Corner Detection’s ability to reliably detect the chin-neck junction even in the presence of visual noise and variations in subject posture. |
| first_indexed | 2025-11-15T04:02:31Z |
| format | Conference or Workshop Item |
| id | ump-46045 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T04:02:31Z |
| publishDate | 2025 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-460452025-10-28T01:07:22Z https://umpir.ump.edu.my/id/eprint/46045/ Chest region estimation using chin landmark keypoints for heart attack detection Noraizan, Ibrahim Rohana, Abdul Karim Nurul Wahidah, Arshad Wan Nur Azhani, Wan Samsudin Marlina, Yakno RC Internal medicine TK Electrical engineering. Electronics Nuclear engineering This paper proposes a lightweight and noninvasive approach for localizing the external chest area by utilizing facial landmarks, aiming to support early detection of chest pain related to heart attacks. While most existing research focuses on internal chest imaging such as X-rays and MRI, external chest localization remains largely unexplored. The challenge arises because areas around the chest, including the hands, face, and neck, often move slightly or change position, which can make it difficult for the system to consistently and accurately identify the chest location. To address these challenges, our approach focuses on accurately estimating the chin as a stable reference point using two techniques: Local Minima Detection based on grayscale intensity changes and Harris Corner Detection, known for its robustness in identifying geometric features. We evaluated both methods on six test samples, each consisting of 200 images captured under controlled conditions. The results show that Harris Corner Detection achieves a higher peak accuracy of 85%, outperforming the Local Minima method at 80%. This improved performance is mainly due to Harris Corner Detection’s ability to reliably detect the chin-neck junction even in the presence of visual noise and variations in subject posture. IEEE 2025 Conference or Workshop Item PeerReviewed pdf en https://umpir.ump.edu.my/id/eprint/46045/1/Chest%20region%20estimation%20using%20chin%20landmark%20keypoints%20for%20heart%20attack%20detection.pdf Noraizan, Ibrahim and Rohana, Abdul Karim and Nurul Wahidah, Arshad and Wan Nur Azhani, Wan Samsudin and Marlina, Yakno (2025) Chest region estimation using chin landmark keypoints for heart attack detection. In: 2025 IEEE 16th Control and System Graduate Research Colloquium, ICSGRC 2025 - Conference Proceedings. 16th IEEE Control and System Graduate Research Colloquium, ICSGRC 2025 , 02 August 2025 , Shah Alam, Malaysia. pp. 204-209.. ISBN 979-833152700-6 (Published) https://doi.org/10.1109/ICSGRC65918.2025.11159829 |
| spellingShingle | RC Internal medicine TK Electrical engineering. Electronics Nuclear engineering Noraizan, Ibrahim Rohana, Abdul Karim Nurul Wahidah, Arshad Wan Nur Azhani, Wan Samsudin Marlina, Yakno Chest region estimation using chin landmark keypoints for heart attack detection |
| title | Chest region estimation using chin landmark keypoints for heart attack detection |
| title_full | Chest region estimation using chin landmark keypoints for heart attack detection |
| title_fullStr | Chest region estimation using chin landmark keypoints for heart attack detection |
| title_full_unstemmed | Chest region estimation using chin landmark keypoints for heart attack detection |
| title_short | Chest region estimation using chin landmark keypoints for heart attack detection |
| title_sort | chest region estimation using chin landmark keypoints for heart attack detection |
| topic | RC Internal medicine TK Electrical engineering. Electronics Nuclear engineering |
| url | https://umpir.ump.edu.my/id/eprint/46045/ https://umpir.ump.edu.my/id/eprint/46045/ |