A New Approach of Facial Expression Recognition for Ambient Assisted Living

Ambient Assisted Living and Ambient Intelligence have seen their impact greatly grows, especially these last decades. It is mainly due to the increase of the ageing population and people with cognitive diseases. Several technologies were developed to make the use of assistive technology more acceptable and comfortable for the elderly in order to reduce or even replace the human assistance. However, there are many challenges and issues, especially in the interaction between the elderly and assistive systems. To make the system interact as human beings, emotions were used. In this paper, we present a new approach to recognize emotions based on facial expressions represented by images. It is based on a new method for feature selection based on distances. We also suggest the use of the well-known K-Nearest Neighbor classifier with optimized parameters. This approach is found effective when tested using two different datasets of images.

[1]  Machiko Tomita,et al.  Smart Home with Healthcare Technologies for Community-Dwelling Older Adults , 2010 .

[2]  D. Kibler,et al.  Instance-based learning algorithms , 2004, Machine Learning.

[3]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[4]  Aliaa A. A. Youssif,et al.  Automatic Facial Expression Recognition System Based on Geometric and Appearance Features , 2011, Comput. Inf. Sci..

[5]  Le Hoang Thai,et al.  Facial Expression Classification Using Artificial Neural Network and K-Nearest Neighbor , 2015 .

[6]  Alex Mihailidis,et al.  A Survey on Ambient-Assisted Living Tools for Older Adults , 2013, IEEE Journal of Biomedical and Health Informatics.

[7]  Michael J. Lyons,et al.  Coding facial expressions with Gabor wavelets , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[8]  Stephen M. Omohundro,et al.  Five Balltree Construction Algorithms , 2009 .

[9]  P. Ekman.  An argument for basic emotions , 1992 .

[10]  Zahir M. Hussain,et al.  Local feature extraction methods for facial expression recognition , 2009, 2009 17th European Signal Processing Conference.

[11]  Inchul Song,et al.  Deep learning for real-time robust facial expression recognition on a smartphone , 2014, 2014 IEEE International Conference on Consumer Electronics (ICCE).

[12]  Josephine Sullivan,et al.  One millisecond face alignment with an ensemble of regression trees , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  D. Lundqvist,et al.  Karolinska Directed Emotional Faces , 2015 .

[14]  C. McCreadie,et al.  The acceptability of assistive technology to older people , 2005, Ageing and Society.

[15]  Boris E. R. de Ruyter,et al.  Benefits of Social Intelligence in Home Dialogue Systems , 2005, INTERACT.

[16]  Jon Louis Bentley,et al.  Multidimensional binary search trees used for associative searching , 1975, CACM.

[17]  Prabir Bhattacharya,et al.  Classification of Facial Expressions Using K-Nearest Neighbor Classifier , 2007, MIRAGE.

[18]  S. Demleitner.  [Communication without words]. , 1997, Pflege aktuell.

[19]  Amit Konar,et al.  Introduction to Emotion Recognition , 2015 .

[20]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[21]  Zhiyong Feng,et al.  Facial expression recognition via deep learning , 2014, 2014 International Conference on Smart Computing.

[22]  Abdesselam Bouzerdoum,et al.  Feature selection for facial expression recognition , 2010, 2010 2nd European Workshop on Visual Information Processing (EUVIP).

[23]  B. K. Julsing,et al.  Face Recognition with Local Binary Patterns , 2012 .