PRE2018 4 Group8: Difference between revisions
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Bayesian face recognition | Bayesian face recognition | ||
https://www.sciencedirect.com/science/article/pii/S003132039900179X | https://www.sciencedirect.com/science/article/pii/S003132039900179X | ||
Kalman filters for emotion recognition: | |||
https://link.springer.com/chapter/10.1007/978-3-642-24600-5_53: Kalman Filter-Based Facial Emotional Expression Recognition | |||
This article uses a 3D candide face model, that describes features of face movement, such as 'brow raiser' and they have selected the most important ones according to them. The joint probability describes the similarity between the image and the emotion described by the parameters of the Kalman filter of the emotional expression as described by the features, and it is maximised to find the emotion corresponding to the picture. The system is more effective than other Bayesian methods like Hidden Markov Models and Principle Component Analysis. | |||
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4658455: Kalman Filter Tracking for Facial Expression Recognition using Noticeable Feature Selection. This paper used conventional CNNs to recognise the facial expression, but the tracking of the features was carried out with a Kalman Filter. | |||
Revision as of 11:40, 29 April 2019
Day 1
Members
| Name | Student ID | |
|---|---|---|
| Rik Hoekstra | 1262076 | r.hoekstra@student.tue.nl |
| Wietske Blijjenberg | 1025111 | |
| Kilian Cozijnsen | 1004704 | k.d.t.cozijnsen@student.tue.nl |
| Arthur Nijdam | 1000327 | c.e.nijdam@student.tue.nl |
| Selina Janssen | 1233328 | s.a.j.janssen@student.tue.nl |
Ideas
Surgery robots (Autonomous robots), Elderly care robots, New technology robot, Facial recognition (Just like Facebook) (happy/not happy)
Subject
Facial recognition (Just like Facebook) (happy/not happy) The use of Convolutional Neural Networks (CNNs) for the purposes of emotion recognition.
Plan
contains a subject (Problem statement and objectives), What do they require?, objectives, users, state-of-the-art, approach, planning, milestones, deliverables, who will do what, SotA: literature study, at least 25 relevant scientific papers and/or patents studied, summary on the wiki!
Interesting persons
Emilia Barakova
weriak@iti.uio.no
Sources
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8039024
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=956083
https://link.springer.com/content/pdf/10.1007%2Fs00521-018-3358-8.pdf
https://link.springer.com/content/pdf/10.1007%2F978-94-007-3892-8.pdf
https://ieeexplore.ieee.org/abstract/document/1556608
https://pdfs.semanticscholar.org/e97f/4151b67e0569df7e54063d7c198c911edbdc.pdf
Bayesian face recognition https://www.sciencedirect.com/science/article/pii/S003132039900179X
Kalman filters for emotion recognition:
https://link.springer.com/chapter/10.1007/978-3-642-24600-5_53: Kalman Filter-Based Facial Emotional Expression Recognition This article uses a 3D candide face model, that describes features of face movement, such as 'brow raiser' and they have selected the most important ones according to them. The joint probability describes the similarity between the image and the emotion described by the parameters of the Kalman filter of the emotional expression as described by the features, and it is maximised to find the emotion corresponding to the picture. The system is more effective than other Bayesian methods like Hidden Markov Models and Principle Component Analysis.
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4658455: Kalman Filter Tracking for Facial Expression Recognition using Noticeable Feature Selection. This paper used conventional CNNs to recognise the facial expression, but the tracking of the features was carried out with a Kalman Filter.