PRE2018 4 Group8

Day 1

Members

Name	Student ID	Email
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://reader.elsevier.com/reader/sd/pii/S016786551930008X?token=3E015F2B3E9E6290D0EA5A3C8CA42C6F7198698E6A17043ADA159C2A5106C4053CBDEE27E39196AE6C415A0DDAF711F4

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.

Facial recognition with the Google glass, for children with Autism Spectrum Disorder (ASD): https://humanfactors.jmir.org/2018/1/e1/ Second Version of Google Glass as a Wearable Socio-Affective Aid: Positive School Desirability, High Usability, and Theoretical Framework in a Sample of Children with Autism. See also https://www.youtube.com/watch?v=_kzfuXy1yMI for a demonstration of Stanford's autismglass.

https://www.researchgate.net/profile/Antonio_Fernandez-Caballero/publication/278707087_Improvement_of_the_Elderly_Quality_of_Life_and_Care_through_Smart_Emotion_Regulation/links/562e0bc808ae518e34825f40/Improvement-of-the-Elderly-Quality-of-Life-and-Care-through-Smart-Emotion-Regulation.pdf. This paper proposes that the quality of life of the elderly improves if smart sensors that recognise their emotions are installed in their environment.