Developing an Assistive Technology to Help Children with Autism for Recognising Human Emotion

References

• Abbo, A. A., V. Jeanne, et al., Mapping facial expression recognition algorithms on a low-power smart camera. (2008).
   Google Scholar
• Aha, D., D. Kible, Instance based learning algorithms. Machine Learning. Machine Learning. Volume 6, pp37–66. (1991).
   Google Scholar
• Daugman, J., Uncertainty relation for resolution in space, spatial frequency, and orientation optimised by 2-dimensional visual cortical filters. J Opt Soc Amer Volume 2, No 7, pp1160–1169. (1985).
   Google Scholar
• Hadid, A., J. Y., Heikkilä, et al., Face and eye detection for person authentication in mobile phones. (2007).
   Google Scholar
• Hall, M. A., L. A. Smith, Practical Feature Subset Selection for Machine Learning. Proceedings of the 21st Australasian Computer Science Conference. Springer. (1998).
   Google Scholar
• Hall, M. A., L. A. Smith, Feature Selection for Machine Learning: Comparing a Correlation-based Filter Approach to the Wrapper. FLAIRS Conference. pp235–239. (1999).
   Google Scholar
• Hart, M., Autism/Excel study. ASSETS 2005 – The 7th International ACM SIGACCESS Conference on Computers and Accessibility. (2005).
   Google Scholar
• John, G. H., P. Langley, Estimating Continuous Distributions in Bayesian Classifiers. The 11th Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann, San Mateo (1995).
   Google Scholar
• Jones, J. P., L. A. Palmer., An evaluation of the 2-dimensional Gabor Filter model of simple Receptive fields in cat striate cortex. J Neurophysiol. Volume 58, No 6, pp1233–1258. (1987).
   Google Scholar
• Kaliouby, R. E., P. Robinson, The emotional hearing aid: an assistive tool for children with Asperger syndrome. Universal Access in the Information Society. Volume 4, No 2. (2005).
   Google Scholar
• Kleinman, J., P. L. Marciano, et al., Advanced theory of mind in high functioning adults with autism. J Autism Dev Disord. Volume 31, No 1, pp29–36. (2001).
   Google Scholar
• Koyama, A., A Review on the Cognitive Neuroscience of Autism. (2005).
   Google Scholar
• La Cara, G. E., M. Ursino, et al., Extraction of Salient Contours in Primary Visual Cortex: A Neural Network Model Based on Physiological Knowledge. Proceedings of the 25th Annual International Conference of the IEEE. Engineering in Medicine and Biology Society. (2003).
   Google Scholar
• Lee, D. S., Y. B. Lee, et al., 3D facial expression recognition synthesis on PDA incorporating emotional timing. Lecture Notes in Computer Science. Volume 3332, pp569–576. (2004).
   Google Scholar
• Li, S. Z., A. K. Jain, Handbook of Face Recognition. Springer. New York, USA (2005).
   Google Scholar
• LoPresti, E. F., C. Bodine, et al., Assistive technology for cognition. (Understanding the Needs of Persons with Disabilities). Engineering in Medicine and Biology Magazine. Volume 27, No 2, pp29–39. IEEE. (2008).
   Google Scholar
• Matson, J. L., S. V. LoVullo, Trends and topics in autism spectrum disorders research. Research in Autism Spectrum Disorders. Volume 3, No 1, pp252–257. (2009).
   Google Scholar
• Quinlan, J. R., C4.5: Programme for Machine Learning. Morgan Kaufmann (1993).
   Google Scholar
• Rish, I., An empirical study of the naïve Bayes classifier. Technical Report RC 22230. (2001).
   Google Scholar
• S. Baron-Cohen, e. a., Another advanced test of theory of mind: evidence from very high functioning adults with autism or Asperger syndrome. J Child Psychol Psychiatry. Volume 38, No 7, pp813–822. (1997).
   Google Scholar
• S. Baron-Cohen, e. a., Social intelligence in the normal and autistic brain: an fMRI study. Eur J Neurosci. Volume 11, No 6, pp1891–1898. (1999).
   Google Scholar
• Simon, B. C., O. Golan, et al., Mind Reading: The Interactive Guide to Emotions. Jessica Kingsley Publishers. London (2004).
   Google Scholar
• Viola, P., M. Jones., Robust real-time face detection. J Computer Vision. Volume 57, No 2, pp137–154. (2004).
   Google Scholar
• Yang, M. H., D. J. Kriegman, et al., Detecting faces in images: A survey Pattern Analysis and Machine Intelligence. IEEE Transactions. Volume 24, No 1, pp34–58. (2002).
   Google Scholar
• Yue, L., Y. Yafeng, et al., Wrapper Feature Selection Optimised SVM Model for Demand Forecasting. The 9th International Conference for Young Computer Scientists. ICYCS. (2008).
   Google Scholar
• Zhou, X. J., D. T. S. A, Heuristic - Statistical Feature Selection Criterion for Inductive Machine Learning in the Real World. Proceedings of the 1988 IEEE International Conference Systems, Man, and Cybernetics. (1988).
   Google Scholar