The week's pick
Reseach Article
Emotions Detection using Java and Neural Networks
| Communications on Applied Electronics |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 6 - Number 1 |
| Year of Publication: 2016 |
| Authors: Ayeni Olaniyi Abiodun, Mogaji Stephen Alaba, Olayemi Olufunke C. |
10.5120/cae2016652415
|
Ayeni Olaniyi Abiodun, Mogaji Stephen Alaba, Olayemi Olufunke C. . Emotions Detection using Java and Neural Networks. Communications on Applied Electronics. 6, 1 ( Oct 2016), 58-63. DOI=10.5120/cae2016652415
Abstract
This research work focuses on the design and implementation of a facial expression detection system focusing mainly on three expressions which are happiness, anger and sadness. Every image is received as jpeg files, Images are then preprocessed forl feature extractions. Feature extraction on each images received is extracted using principal component analysis (PCA), extracted features are passed to the neural networks. Java object oriented programming language and support vector machines (SVM) algorithm was used in the design and implementation of the system. In this research work, facial expression detecting system is implemented using java programming language and SVM algorithm. The project introduces a simple architecture for human facial expression detection and elaborate more on what facial expression and facial expression detection system is about. The project looks into the existing system, identifies the problem thereby deploying a new system which is user interactive. The system developed achieved a good result of 81.25% for happiness, 75% for anger and 60% for sadness.
References
- Abdulrahman, Muzammil, Tajuddeen R.Gwadabe, Fahad J. Abdu, and Alaa Eleyan. "Gabor wavelet transform based facial expression recognition using PCA and LBP." In Signal Processing and Communications Applications Conference, 2014 22nd, pp. 2265-2268. IEEE, 2014
- Baskerville, R. (1991). "Risk Analysis as a Source of Professional Knowledge". Computers & Security 10
- Boehm. B (2000), "Spiral Development: Experience, Principles,and Refinements”
- Cortes C. Vapnik V. (1995). "Support-vector networks". Machine Learning 20
- Ekman. P and Friesen. W (1978) "Facial Action Coding System: A Technique for the Measurement of Facial Movement"
- Henry A. Rowley, Shumeet Baluja &Takeo Kanade. (1997) Rotation Invariant Neural Network-Based Face Detection, December, CMU-CS-97-201
- Henry Rowley, Baluja S. & Kanade T. (1999) “Neural Network-Based Face Detection, Computer Vision and Pattern Recognition”, Neural Network-Based Face Detection, Pitts-burgh, Carnegie Mellon University, PhD thesis.
- International Journal of Computer Trends and Technology (IJCTT) – volume 17 Number 4 Nov 2014 Page 161
- International Journal of Application or Innovation in Engineering & Management (IJAIEM) Volume 2, Issue 6, June 2013
- Jaimini Suthar, Narendra Limbad “A Literature Survey on Facial Expression Recognition techniques using Appearance based features”
- Jyh-Yeong Chang and Jia-Lin Chen (2001) “Automated Facial Expression Recognition System Using Neural Networks”
- KahKay Sung & Tomaso Poggio (1994) Example Based Learning For View Based Human Face Detection, Massachusetts Institute of Technology Artificial Intelligence Laboratory and Center For Biological And Computational Learning, Memo 1521, CBCL Paper 112, MIT, December
- Lonnie D. Bentley p. “Systems Analysis and Design for the Global Enterprise”.
- L. Beaurepaire, K.Chehdi, B.Vozel (1997), “Identification of the nature of the noise and estimation of its statistical parameters by analysis of local histograms”, Proceedings of ICASSP-97, Munich, 1997.
- Manal Abdullah1, Majda Wazzan1 and Sahar Bo-saeed “Optimizing Face Recognition Using PCA” International Journal of Artificial Intelligence & Applications (IJAIA), Vol.3, No.2, March 2012.
- Marian Beszedes & Milos Oravec (2005) “A System For Localization Of Human Faces In Images Using Neural Networks”, Journal Of Electrical Engineering, Vol. 56, No 7-8, pp195-199.
- Marakas, James A. O'Brien, George M. (2010). Management information systems.
- Meher, Sukanya Sagarika, and Pallavi Maben "Face recognition and facial expression identification using PCA." In Advance Computing Conference, 2014 IEEE International, pp. 1093- 1098. IEEE, 2014.
- Ding, W. and Marchionini, G. 1997 A Study on Video Browsing Strategies. Technical Report. University of Maryland at College Park.
- Fröhlich, B. and Plate, J. 2000. The cubic mouse: a new device for three-dimensional input. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
- Tavel, P. 2007 Modeling and Simulation Design. AK Peters Ltd.
- Sannella, M. J. 1994 Constraint Satisfaction and Debugging for Interactive User Interfaces. Doctoral Thesis. UMI Order Number: UMI Order No. GAX95-09398., University of Washington.
- Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3 (Mar. 2003), 1289-1305.
- Brown, L. D., Hua, H., and Gao, C. 2003. A widget framework for augmented interaction in SCAPE.
- Y.T. Yu, M.F. Lau, "A comparison of MC/DC, MUMCUT and several other coverage criteria for logical decisions", Journal of Systems and Software, 2005, in press.
- Spector, A. Z. 1989. Achieving application requirements. In Distributed Systems, S. Mullender
- Mu-Chunsu and Chien-Hsing Chou “Application of Associative memory in human face detection
- Samiksha Agrawal, Pailavikhatri and Shashikant Gupta (2014). “Facial expression recognition technique: A survey.
- Zermir Narima, Ramdani Messaond, Saaidia. M, Snani Cherifa (2015). “two dimensional principal component Analysis (2DPCA) for human facial expression recognition.
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