Filipino based Facial Emotion Features Datasets using Haar-Cascade Classifier and Fisherfaces Linear Discriminant Analysis Algorithm


Authors
  • Anna Liza A. Ramos
  • Paolo A. Buenafe
  • Evander Keannu C. Cabrales
  • Jasreel D. Teñido
  • Shaina O. Portas
  • St. Michael's College of Laguna
Published in


Abstract
  • Emotion detection is one of emerging topics in the field of research. In fact, various studies conducted utilized the available datasets – applying different methodologies and implementing the best suited algorithms to improve the classification performance and increase the recognition rate. This study aims to apply the Filipino-based facial emotion features through the revalidation of the available features in Visage Cloud API. It served as a basis in determining how the emotion differs from the expert’s validation and testing through the WEKA tool. The validation mainly checked the classification accuracy performance of the Fisher Faces Linear Discriminant Analysis used in this study. In result, the study marked a classification accuracy of 90.66% based on the API outcomes with 150 instances and 83.61 % classification rate for 609 features – it clearly outperformed the results acquired in the existing studies. Furthermore, the prototype model was built using Phyton and tested on 10 subjects with two groups of training datasets validated by the API of 150 features. The used datasets also adopted the validation of the experts with 609 facial features and a recognition rate of 22.98 % and 54.2 % respectively.


Keywords
  • Emotion, Facial expression, Filipino based features




Cite As
  • APA 7th Edition:
    Ramos, A., Buenafe, P., Cabrales, E., Teñido, J., & Portas, S. (2019). Filipino based Facial Emotion Features Datasets using Haar-Cascade Classifier and Fisherfaces Linear Discriminant Analysis Algorithm. Innovatus, 2(1), 47-53.
  • Harvard:
    Ramos, A., Buenafe, P., Cabrales, E., Teñido, J. and Portas, S., 2019. Filipino based Facial Emotion Features Datasets using Haar-Cascade Classifier and Fisherfaces Linear Discriminant Analysis Algorithm. Innovatus, 2(1), pp.47-53.
  • IEEE:
    [1] A. Ramos, P. Buenafe, E. Cabrales, J. Teñido and S. Portas, "Filipino based Facial Emotion Features Datasets using Haar-Cascade Classifier and Fisherfaces Linear Discriminant Analysis Algorithm", Innovatus, vol. 2, no. 1, pp. 47-53, 2019.


References
  • Chaudhari D. & Gosavi A. (2014). Facial Expression Recognition using Neural Network –An Overview. International Journal of Soft Computing and Engineering (IJSCE)
  • Dubey, M & Singh L. (2016). Automatic Emotion Recognition Using Facial Expression: A Review. International Research Journal of Engineering and Technology (IRJET)
  • Gosavi P. & Khot S. (2015). Facial Expression Recognition Using Principal Component Analysis. International Journal of Soft Computing and Engineering (IJSCE)
  • Islam, N & Loo, C. (2014). Geometric Feature-Based Facial Emotion Recognition Using Two-Stage Fuzzy Reasoning Model. SSN 0302-9743
  • Patil S. & Shukla P. (2016). Survey Paper on Emotion Recognition. International Journal of Engineering and Applied Sciences (IJEAS).
  • Belhumeur P., Hespanha J., & Kriegman D. (2015). Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, VOL. 19, NO. 7
  • Bhadauria S., Jadon R. S., & Jaiswal S. (2016). Comparison Between Face Recognition Algorithm-Eigenfaces, Fisherface and Elastic Bunch Graphmatching. Journal of Global Research in Computer Science
  • Singla R. (2016). A Literature Survey on Mood Detection System. (IJCSIT) International Journal of Computer Science and Information Technologies
  • Reddy P., Syed W., Uppalapti A., Vadakattu A., & Vasireddy A.. (2018). Emotion Detection through Facial Feature Recognition. International Research Journal of Engineering and Technology (IRJET)
  • Dhage V. & Ramteke P. (2014). Human Face Detection and Recognition using Genetic Algorithm: A Review. International Journal of Science, Engineering and Technology Research (IJSETR)
  • Bala J. & Fernandes S. (2014). Performance Analysis of PCA-based and LDA-based Algorithms for Face Recognition. International Journal of Signal Processing Systems.
  • Singh V., Shooken V. & Singh B. (2013). Review of PCA, LDA and LBP algorithms used for 3D Face Recognition. International Journal of Engineer ing Science and Innovative Technology (IJESIT)
  • Dubey M. & Singh L. (2016). Automatic Emotion Recognition Using Facial Expression: A Review. International Research Journal of Engineering and Technology (IRJET
  • Islam N., & Loo C. (2014). Geometric Feature-Based Facial Emotion Recognition Using Two-Stage Fuzzy Reasoning Model. SSN 0302-9743
  • Wang M. & Yee S. (2016). Determining Mood from Facial Expressions. CS 229 Project
  • Reddy K. S. M. (2017). Comparison of Various Face Recognition Algorithms. International Journal of Advanced Research in Science, Engineering and Technology Vol. 4, Issue 2
  • Cao B., Gao W., Shan S. & Zhao D. (2014). Extended Fisherface For Face Recognition From A Single Example Image Per Person. IEEE International Symposium on Circuits and Systems
  • Kumar G. & Saurabh S. (2016). A Comparative Study of Face Recognition Algorithms. International Journal of Recent Research Aspects Vol. 3, Issue 2.
  • Agarwal D., Saini A. & Saini R. (2014). Analysis of Different Face Recognition Algorithms. International Journal of Engineering Research & Technology (IJERT) Vol 3. Issue 11
  • Dimitoglou G., Johnson A., Murphy K., & Shelley J. (2014). Automatic Solar Cavity Detection Using Haar Cascade Classifier. Department of Computer Science, Hood College, Frederick, M. D,
  • Fernandez J. & Wilson P. I. (2014). Facial Feature Detection Using Haar Classifier. CCSC: South Central Conference JCSC 21,4
  • Soo S. (2016). Object detection using Haar-cascade Classifier. Institute of Computer Science, University of Tartu
  • Mananec J. (2014). Support Vector Machines, PCA and LDA in Face Recognition. 8th Journal of Electrical Engineering
  • Dhere P. (2015). Emotions and their Effect Adult Learning: A Constructivist Perspective. Florida International University, USA


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