A Smile Detection for Hands-Free Selfie Capture Using Machine Learning
This paper presents a novel, real-time smile detection system designed to enable hands-free selfie capture using machine learning. The system leverages computer vision techniques and deep learning models to accurately detect smiles in live camera feeds, triggering automatic photo capture without use...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2025
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/2151/ http://eprints.intimal.edu.my/2151/1/jods2025_09.pdf http://eprints.intimal.edu.my/2151/2/694 |
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| author | G., Ramya J, Sreeja K., Jyothi J.J., Ranjith K., Sanjana M., Harika R. Gnana, Deepthi K., Navya |
| author_facet | G., Ramya J, Sreeja K., Jyothi J.J., Ranjith K., Sanjana M., Harika R. Gnana, Deepthi K., Navya |
| author_sort | G., Ramya |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | This paper presents a novel, real-time smile detection system designed to enable hands-free selfie capture using machine learning. The system leverages computer vision techniques and deep learning models to accurately detect smiles in live camera feeds, triggering automatic photo capture without user intervention. Built on a modular architecture utilizing OpenCV for face detection and a convolutional neural network (CNN) for smile classification, the application ensures low-latency performance suitable for mobile and embedded platforms. The system is evaluated on public datasets such as GENKI-4K and CelebA, achieving an average accuracy of 94.2% in real-world lighting and expression conditions. A lightweight, Flask-based web interface offers live preview, detection feedback, and photo gallery integration. Experimental results show that the system operates at over 15 FPS on mid-range hardware, confirming its applicability for edge devices. Future extensions include emotion-based gesture capture, multilingual voice commands, and AR filter integration. The system demonstrates the potential of machine learning to create intuitive, user-friendly photo applications with minimal manual input. |
| first_indexed | 2025-11-14T11:59:02Z |
| format | Article |
| id | intimal-2151 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:59:02Z |
| publishDate | 2025 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-21512025-07-03T08:41:54Z http://eprints.intimal.edu.my/2151/ A Smile Detection for Hands-Free Selfie Capture Using Machine Learning G., Ramya J, Sreeja K., Jyothi J.J., Ranjith K., Sanjana M., Harika R. Gnana, Deepthi K., Navya QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) This paper presents a novel, real-time smile detection system designed to enable hands-free selfie capture using machine learning. The system leverages computer vision techniques and deep learning models to accurately detect smiles in live camera feeds, triggering automatic photo capture without user intervention. Built on a modular architecture utilizing OpenCV for face detection and a convolutional neural network (CNN) for smile classification, the application ensures low-latency performance suitable for mobile and embedded platforms. The system is evaluated on public datasets such as GENKI-4K and CelebA, achieving an average accuracy of 94.2% in real-world lighting and expression conditions. A lightweight, Flask-based web interface offers live preview, detection feedback, and photo gallery integration. Experimental results show that the system operates at over 15 FPS on mid-range hardware, confirming its applicability for edge devices. Future extensions include emotion-based gesture capture, multilingual voice commands, and AR filter integration. The system demonstrates the potential of machine learning to create intuitive, user-friendly photo applications with minimal manual input. INTI International University 2025-06 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2151/1/jods2025_09.pdf text en cc_by_4 http://eprints.intimal.edu.my/2151/2/694 G., Ramya and J, Sreeja and K., Jyothi and J.J., Ranjith and K., Sanjana and M., Harika and R. Gnana, Deepthi and K., Navya (2025) A Smile Detection for Hands-Free Selfie Capture Using Machine Learning. Journal of Data Science, 2025 (09). pp. 1-10. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) G., Ramya J, Sreeja K., Jyothi J.J., Ranjith K., Sanjana M., Harika R. Gnana, Deepthi K., Navya A Smile Detection for Hands-Free Selfie Capture Using Machine Learning |
| title | A Smile Detection for Hands-Free Selfie Capture Using Machine Learning |
| title_full | A Smile Detection for Hands-Free Selfie Capture Using Machine Learning |
| title_fullStr | A Smile Detection for Hands-Free Selfie Capture Using Machine Learning |
| title_full_unstemmed | A Smile Detection for Hands-Free Selfie Capture Using Machine Learning |
| title_short | A Smile Detection for Hands-Free Selfie Capture Using Machine Learning |
| title_sort | smile detection for hands-free selfie capture using machine learning |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) |
| url | http://eprints.intimal.edu.my/2151/ http://eprints.intimal.edu.my/2151/ http://eprints.intimal.edu.my/2151/1/jods2025_09.pdf http://eprints.intimal.edu.my/2151/2/694 |