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Person Recognition using Facial Micro-Expressions with Deep Learning (2306.13907v1)
Published 24 Jun 2023 in cs.CV
Abstract: This study investigates the efficacy of facial micro-expressions as a soft biometric for enhancing person recognition, aiming to broaden the understanding of the subject and its potential applications. We propose a deep learning approach designed to capture spatial semantics and motion at a fine temporal resolution. Experiments on three widely-used micro-expression databases demonstrate a notable increase in identification accuracy compared to existing benchmarks, highlighting the potential of integrating facial micro-expressions for improved person recognition across various fields.
- A survey on facial soft biometrics for video surveillance and forensic applications. Artificial Intelligence Review, 52:1155–1187, 2019.
- Person recognition based on head and mouth dynamics. In 2006 IEEE Workshop on Multimedia Signal Processing, pages 29–32, 2006.
- Paul Ekman. Emotions Revealed: Recognizing Faces and Feelings to Improve Communication and Emotional Life. Henry Holt and Company, illustrated edition, 2004.
- Paul Ekman. Lie catching and microexpressions. In Clancy Martin, editor, The Philosophy of Deception. Oxford Academic, online edition edition, 2009.
- I see how you feel: Training laypeople and professionals to recognize fleeting emotions. In The Annual Meeting of the International Communication Association. Sheraton New York, New York City, pages 1–35, 2009.
- Usman Saeed. Facial micro-expressions as a soft biometric for person recognition. Pattern Recognition Letters, 143(C):95–103, 2021.
- Multimodal decomposition with magnification on micro-expressions and its impact on facial biometric recognition. In 2017 IEEE International Symposium on Consumer Electronics (ISCE), pages 45–46, 2017.
- Individual identification based on facial dynamics during expressions using active-appearance-based hidden markov models. In 2011 IEEE International Conference on Automatic Face & Gesture Recognition (FG), pages 797–802, 2011.
- M. Gavrilescu. Study on using individual differences in facial expressions for a face recognition system immune to spoofing attacks. IET Biometrics, 5(3):236–242, 2016.
- Changes in facial expression as biometric: A database and benchmarks of identification. In 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pages 621–628, 2018.
- Facial expression analysis and expression-invariant face recognition by manifold-based synthesis. Machine Vision and Applications, 29(2):263–284, 2018.
- A study on the discriminability of facs from spontaneous facial expressions. In 2016 IEEE International Conference on Image Processing (ICIP), pages 1674–1678, 2016.
- Slow-fast networks for video recognition. arXiv, 1812.03982v3, 2019. arXiv:1812.03982v3 [cs.CV] 29 Oct 2019.
- What have we learned from deep representations for action recognition? arXiv, 1801.01415v1, 2018. arXiv:1801.01415v1 [cs.CV] 4 Jan 2018.
- A spontaneous micro-expression database: Inducement, collection and baseline. In 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pages 1–6, 2013.
- Samm: A spontaneous micro-facial movement dataset. IEEE Transactions on Affective Computing, 9(1):116–129, 2018.
- CASME II: An Improved Spontaneous Micro-Expression Database and the Baseline Evaluation. PLoS ONE, 9(1):e86041, January 2014.
- Grad-cam: Visual explanations from deep networks via gradient-based localization. International Journal of Computer Vision, 128(2):336–359, 2020. arXiv:1610.02391v4 [cs.CV] 3 Dec 2019.
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