A Study on Facial Expression Recognition

Year 2017, Volume: 30 Issue: 3, 19 - 27, 20.09.2017

Elena Battini Sonmez

Abstract

This study focuses on the issue of automatic Facial
Expression Recognition (FER) on little databases of 2D faces. Convolutional
Neural Networks (CNN) is a relatively new classification technique, which
reaches the state of the art on big databases; however, the use of CNN with a
scarce number of samples is still an open and interesting challenge. Following
the classical machine learning approach, we considered different combination of
appearance based projection methods, feature extraction techniques and classifiers,
and we compared their performances with special designed CNN. Experimental
results underline the drawback of CNN with scares labeled data.

Keywords

Little databases, facial expression, sparse representation based classifier, convolutional neural net

References

• [1] Caleanu, C., “Face expression recognition: A brief overview of the last decade”, IEEE Eighth International Symposium on applied Computational Intelligence and Informatics (SACI), Timisoara, Romania, 157-161, (2013).
• [2] Sariyanidi, E., Gunes, H., Cavallaro, A., “Automatic analysis of facial affect: A survey of registration, representation, and recognition”, Journal of IEEE Transactions on Pattern Analysis and Machine Intelligence 37(6), 1113-1133, (2015).
• [3] Ashraf, A.B., Lucey, S., Cohn, J.F., Chen, T., Ambadar, Z., Prkachin, K.M. and Solomon, P.E., “The painful face - pain expression recognition using active appearance models”, Image and Vision Computing, 27(12), 1788–1796, (2009).
• [4] Daugman, J.G., “Complete discrete 2-d gabor transforms by neural networks for image analysis and compression”, IEEE Trans. Audio Speech., 36, 1169–1179, (1988).
• [5] Ojala, T., Pietikinen, M. and Harwook, D., “A Comparative Study of texture measures with classification based on feature distribution”, Pattern Recognition Journal, 29(1), 55-59, (1996).
• [6] Duda, R, Hart, P. and Stork, D., Pattern Classification, 2nd Edition, John Wiley, New York, (2001).
• [7] Lee, K.C., Ho, J., Kriegman, D.J., “Acquiring linear subspaces for face recognition under variable lighting”, IEEE Trans. Pattern Anal. Mach. Intell., 27, 684–698, (2005).
• [8] Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S. and Ma, Y., “Robust face recognition via sparse representation”, Transactions on Pattern Analysis and Machine Intelligence, 31(2), 210–227, (2009).
• [9] LeCun, Y., Bengio, Y. and Hinton, G., “Deep learning”, Journal of Nature, 521, 436-444, (2015).
• [10] Lyons, Akamatsu, M.S., Kamachi, M. and Gyoba, J., “Coding facial expressions with Gabor wavelets”, IEEE Int. Conf. on Autom. Face and Gesture Recognition, Nara, Japan, 200–205, 1998.
• [11] Deng, H.B., Jin, L.W., Zhen, L.X. and Huang, J.C, “A New Facial Expression Recognition Method Based on Local Gabor Filter Bank and PCA plus LDA”, International Journal of Information Technology, 11, 86-96, (2005).
• [12] Bashyal S., and Venayagamoorthy, G.K., “Recognition of Facial Expressions using Gabor wavelets and learning vector quantization”, Eng. Applications of Artificial Intelligence, 21(7), 1056-1064, (2008).
• [13] Shan, C., Gong, S. and McOwan, P. W., “Facial expression recognition based on local binary patterns: A comprehensive study”, Image and Vision Computing, 27, 803–816, (2009).
• [14] Zilu, Y. and Guoyi, Z., "Facial Expression Recognition Based on NMF and SVM", International Forum on Information Technology and Applications, Chengdu, China, 612-615, (2009).
• [15] Huang, M.W., Wang, Z. and Ying, Z.L., “A New Method for Facial Expression Recognition based on Sparse Representation plus LBP”, 3th Int. Congress on Image and Signal Processing (CISP), Yantai, China, (2010).
• [16] Zavaschi, T.H.H, Koerich, A.L. and Oliveira, L.E.S., “Facial Expression Recognition using ensemble of Classifiers”, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czech Republic, 1489-1492, (2011).
• [17] Zhao, X. and Zhang, S., “Facial expression recognition using local binary patterns and discriminant kernel locally linear embedding”, EURASIP Journal on Advances in Signal Processing, 20, DOI: 10.1186/1687-6180-2012-20, (2012).
• [18] Liu, P., Han, S., Meng, Z. and Tong, Y., "Facial Expression Recognition via a Boosted Deep Belief Network", IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, 1805-1812, doi: 10.1109/CVPR.2014.233, (2014).
• [19] Donoho, D.L., “Compressed sensing”, IEEE Trans. Inform. Theor., 52, 1289–1306, (2006).
• [20] Battini Sönmez, E., “Robust Classification based on Sparsity”, Lambert Academic Publishing, Germany, (2013).
• [21] Battini Sönmez, E. and Cangelosi, A., “Convolutional neural networks with balanced batches for facial expressions recognition”, 9th International Conference on Machine Vision (ICMV), Nice, France, (2016).