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Research Article
A Recurrent Neural Network on Video-Based Face Identification Suspicious and Also Wanted People Recognition in ATM
R. Vasugi1
Joiceline J2
Monika N3
Piyarijon F4
Priyanka S5
1Assistant Professor, Department of Information Technology, Er. Perumal Manimekalai College of Engineering, Hosur, Tamilnadu, India. 2345 Department of Information Technology, Er. Perumal Manimekalai College of Engineering, Hosur, Tamilnadu, India.
Published Online: May-August 2024
Pages: 140-147
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20240302021
References
1. H. D. Flowe, "Do Characteristics of Faces That Convey Trustworthiness and Dominance Underlie Perceptions of Criminality?", PLOS One,
Vol.7, pp.1-7, 2020.
2. N.D. Thomson, M. Aboutanos, K.A. Kiehl, C. Neumann, C. Galusha and K.A. Fanti, "Physiological reactivity in response to a fear-induced
virtual reality experience: Associations with psychopathic traits", Psychophysiology, Vol. 56, pp. 158-164, 2019.
3. N. N. Oosterhof, A. and Todorov, "The functional basis of face evaluation", Proceedings of the National Academy of Sciences of the United
States of America, Vol. 105, pp. 11087–11092, 2020.
4. C. P. Todorov, S. D. Said, and N. N. Oosterhof, "Understanding evaluation of faces on social dimensions", Trends in Cognitive Sciences,
Vol. 12, pp. 455–460, 2019.
5. S. Subarna, S. Suman, and B. Abinash, "Human Behavior Prediction using Facial Expression Analysis" IEEE International Conference on
Computing, Communication and Automation (ICCCA), pp. 399-404, 2021.
6. T. Chandan, H. Madasu, and V. Shantaram, “Suspicious Face Detection based on Eye and other facial features Movement Monitoring”,
IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pp. 1-8, 2019.
7. G. Zhao, and M. Pietikinen, “Dynamic texture recognition using local binary patterns with an application to facial expressions”, IEEE
Trans. Pattern
8. Anal. Mach. Intell, Vol. 29, pp. 915–928, 2020.
9. M. Suk, and B. Prabhakaran, “Real-time mobile facial expression recognition system—a case study”, CVPR, Computer Vision Foundation,
pp. 132–137, 2021.
10. U. N. Mahesh, and R. Hanumantha, "Hybrid Approach for Facial Expression Recognition using HJDLBP and LBP Histogram in Video
Sequences”, Image, Graphics and Signal Processing, Vol. 2, pp. 1-9, 2019.
11. T. Pursche, J. Krajewski, and R. Moeller, “Video-based Heart Rate Measurement from Human Faces”, IEEE International Conference on
Consumer Electronics (ICCE), pp. 544-545, 2022.
12. M. Rapczynski, P. Werner, and A. Al-Hamadi, "Continuous Low Latency Heart Rate Estimation from Painful Faces in Real Time", 23rd
International Conference on Pattern Recognition (ICPR), pp. 1165-1170, 2019.
13. K. Lin, D. Chen, and W. Tsai, " Face-Based Heart Rate Signal Decomposition and Evaluation Using Multiple Linear Regression”, IEEE
Sensors Journal, Vol. 16, pp. 1351-1360, 2020.
14. S. Fallet, V. Moser, F. Braun, and J. Vesin, “Imaging Photoplethysmography: What are the Best Locations on the Face to Estimate Heart
Rate?”, Computing in Cardiology, Vol. 43, pp. 341-344, 2021.
Vol.7, pp.1-7, 2020.
2. N.D. Thomson, M. Aboutanos, K.A. Kiehl, C. Neumann, C. Galusha and K.A. Fanti, "Physiological reactivity in response to a fear-induced
virtual reality experience: Associations with psychopathic traits", Psychophysiology, Vol. 56, pp. 158-164, 2019.
3. N. N. Oosterhof, A. and Todorov, "The functional basis of face evaluation", Proceedings of the National Academy of Sciences of the United
States of America, Vol. 105, pp. 11087–11092, 2020.
4. C. P. Todorov, S. D. Said, and N. N. Oosterhof, "Understanding evaluation of faces on social dimensions", Trends in Cognitive Sciences,
Vol. 12, pp. 455–460, 2019.
5. S. Subarna, S. Suman, and B. Abinash, "Human Behavior Prediction using Facial Expression Analysis" IEEE International Conference on
Computing, Communication and Automation (ICCCA), pp. 399-404, 2021.
6. T. Chandan, H. Madasu, and V. Shantaram, “Suspicious Face Detection based on Eye and other facial features Movement Monitoring”,
IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pp. 1-8, 2019.
7. G. Zhao, and M. Pietikinen, “Dynamic texture recognition using local binary patterns with an application to facial expressions”, IEEE
Trans. Pattern
8. Anal. Mach. Intell, Vol. 29, pp. 915–928, 2020.
9. M. Suk, and B. Prabhakaran, “Real-time mobile facial expression recognition system—a case study”, CVPR, Computer Vision Foundation,
pp. 132–137, 2021.
10. U. N. Mahesh, and R. Hanumantha, "Hybrid Approach for Facial Expression Recognition using HJDLBP and LBP Histogram in Video
Sequences”, Image, Graphics and Signal Processing, Vol. 2, pp. 1-9, 2019.
11. T. Pursche, J. Krajewski, and R. Moeller, “Video-based Heart Rate Measurement from Human Faces”, IEEE International Conference on
Consumer Electronics (ICCE), pp. 544-545, 2022.
12. M. Rapczynski, P. Werner, and A. Al-Hamadi, "Continuous Low Latency Heart Rate Estimation from Painful Faces in Real Time", 23rd
International Conference on Pattern Recognition (ICPR), pp. 1165-1170, 2019.
13. K. Lin, D. Chen, and W. Tsai, " Face-Based Heart Rate Signal Decomposition and Evaluation Using Multiple Linear Regression”, IEEE
Sensors Journal, Vol. 16, pp. 1351-1360, 2020.
14. S. Fallet, V. Moser, F. Braun, and J. Vesin, “Imaging Photoplethysmography: What are the Best Locations on the Face to Estimate Heart
Rate?”, Computing in Cardiology, Vol. 43, pp. 341-344, 2021.
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