Abstract
Features in the facial images are multi-dimensional. Different facial expressions-based images are actually the interplay of the edges, which are found on faces. The wavelet transform which has been extensively used as a tool for mathematical analysis of the facial images has the disadvantage of poor directionality. Thus, curvelet transform is preferred for the facial image analysis due to better representation of edges by the directional elements. However, its feature dimension is of large size which makes the curvelet approach computationally expensive. In order to capture the interrelationship in the curvelet transform-based features at the higher level to construct new feature vectors, the proposed work suggests the use of graph signal processing along with the curvelet transform for recognizing the facial expressions. Not only the dimension of the feature vectors has been reduced but also recognition of the facial expression has been significantly improved. Experiments for Japanese female facial expression database and Cohn–Kanade (CK+) database show the effectiveness of the proposed approach.
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References
Mandal, M.K., Pandey, R., Prasad, A.B.: Facial expressions of emotions and schizophrenia: a review. Schizophr. Bull. 24(3), 399 (1998)
Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. 23(6), 681–685 (2001)
Tzimiropoulos, G., Argyriou, V., Zafeiriou, S., Stathaki, T.: Robust FFT-based scale-invariant image registration with image gradients. IEEE Trans. Pattern Anal. Mach. Intell. 32(10), 1899–1906 (2010)
Baker, S., Matthews, I.: Lucas-kanade 20 years on: a unifying framework. Int. J. Comput. Vision 56(3), 221–255 (2004)
Sariyanidi, E., Gunes, H., Cavallaro, A.: Automatic analysis of facial affect: a survey of registration, representation, and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(6), 1113–1133 (2015)
Pinto, N., Cox, D.D., DiCarlo, J.J.: Why is real-world visual object recognition hard? PLoS Comput. Biol. 4(1), e27 (2008)
Dalal, Navneet, Triggs, Bill: Histograms of oriented gradients for human detection. In: International Conference on Computer Vision & Pattern Recognition (CVPR’05), Vol. 1, pp. 886–893. IEEE Computer Society (2005)
Tong, Y., Chen, J., Ji, Q.: A unified probabilistic framework for spontaneous facial action modeling and understanding. IEEE Trans. Pattern Anal. Mach. Intell. 32(2), 258–273 (2010)
Lecun, Y.: Generalization and network design strategies. In: Connectionism in Perspective, pp 143–155 (1989)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Yu, Z., Zhang, C.: Image based static facial expression recognition with multiple deep network learning. In: Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, pp 435–442 (2015)
Shan, K., Guo, J., You, W., Lu, D., Bie, R.: Automatic facial expression recognition based on a deep convolutional-neural-network structure. In: 2017 IEEE 15th International Conference on Software Engineering Research, Management and Applications (SERA), pp 123–128 (2017)
Do, M.N., Vetterli, M.: The finite ridgelet transform for image representation. Trans. Image Process. 12(1), 16–28 (2003)
Starck, J.-L., Candès, E.J., Donoho, D.L.: The curvelet transform for image denoising. IEEE Trans. Image Process. 11(6), 670–684 (2002)
Manikandan, M., Saravanan, A., Bagan, K.B.: Curvelet transform based embedded lossy image compression. In: 2007 International Conference on Signal Processing, Communications and Networking, pp. 274–276 (2007)
Starck, J.-L., Donoho, D.L., Candes, E.J.: Very high quality image restoration by combining wavelets and curvelets. In: International Symposium on Optical Science and Technology, pp. 9–19 (2001)
Mandal, T., Majumdar, A., Wu, Q.M.J.: Face recognition by curvelet based feature extraction. In: Proceedings of the 4th International Conference on Image Analysis and Recognition, pp. 806–817 (2007)
Majumdar, A., Bhattacharya, A.: Face recognition by multi-resolution curvelet transform on bit quantized facial images. In: International Conference on Conference on Computational Intelligence and Multimedia Applications, 2007, vol. 2, pp. 209–213 (2007)
Xianxing, W., Zhao, J.: (2010) Curvelet feature extraction for face recognition and facial expression recognition. In: Sixth International Conference on Natural Computation , vol. 3, pp. 1212–1216 (2010)
Tanaya Mandal, Q.M., Jonathan, W., Yuan, Y.: Curvelet based face recognition via dimension reduction. Signal Process. 89(12), 2345–2353 (2009)
Rui, L., Nejati, H., Cheung, N.-M.: Dimensionality reduction of brain imaging data using graph signal processing. In 2016 IEEE International Conference on Image Processing (ICIP), pp 1329–1333. IEEE (2016)
Ménoret, M., Farrugia, N., Pasdeloup, B., Gripon, V.: Evaluating graph signal processing for neuroimaging through classification and dimensionality reduction. In 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP), pp 618–622. IEEE (2017)
Meena, H.K., Sharma, K.K., Joshi, S.D.: Improved facial expression recognition using graph signal processing. Electron. Lett. 53(11), 718–720 (2017)
Candes, E., Demanet, L., Donoho, D., Ying, L.: Fast discrete curvelet transforms. Multiscale Model. Simul. 5(3), 861–899 (2006)
Niyogi, P., He, X.: Locality preserving projections. Neural Inf. Process. Syst. MIT 16, 153–160 (2003)
Starck, J L.: Image processing by the curvelet transform. http://jstarck.free.fr, p. 4 (2002)
Candès, E.J.: What is... a curvelet? Not. Am. Math. Soc. 50(11), 1402–1403 (2003)
Lyons, M., Akamatsu, S., Kamachi, M., Gyoba, J.: Coding facial expressions with gabor wavelets. In: IEEE International Conference on Face and Gesture Recognition, pp 200–205 (1998)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001. CVPR 2001, vol. 1:, pp. I–511 (2001)
Tang, M., Chen, F.: Facial expression recognition and its application based on curvelet transform and PSO-SVM. Opt. Int. J. Light Electron Opt. 124(22), 5401–5406 (2013)
Uçar, A., Demir, Y., Güzeliş, C.: A new facial expression recognition based on curvelet transform and online sequential extreme learning machine initialized with spherical clustering. Neural Comput. Appl. 27(1), 131–142 (2016)
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Meena, H.K., Sharma, K.K. & Joshi, S.D. Effective curvelet-based facial expression recognition using graph signal processing. SIViP 14, 241–247 (2020). https://doi.org/10.1007/s11760-019-01547-9
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DOI: https://doi.org/10.1007/s11760-019-01547-9