Facial expression recognition based on compressive sensing and pyramid processing

Bu makalede, geliştirilmiş yüz ifadesi tanıma için yeni bir yaklaşım önerilmiştir. Bu yeni yaklaşım sıkıştırma algılama teorisinden ve yüz ifadesi problemine çoklu çözünürlük yaklaşımından esinlenmektedir. Başlangıçta, her bir görüntü örneği farklı boyutlarda ve çözünürlüklerdeki piramitlerin istenilen seviyesine ayrıştırılmaktadır. Piramidin her seviyesinde, özellikler sıkıştırma algılama teorisine dayanan bir ölçüm matrisi kullanılarak ayrıştırılmaktadır. Bu ölçümlerin tamamı orijinal görüntü için bir özellik vektörü oluşturmak için bir araya getirilmektedir. Üç uzaklık ölçümü sınıflandırıcısı (Manhattan, Öklid, kosinüs) ve destek vektör makinesi kullanımından elde edilen sonuçlar, aynı veri tabanları ve ayarlarının kullanıldığı literatürdeki benzer algoritmaların çoğundan daha etkileyici ve iyidir

Sıkıştırılmış algılama ve piramit işlemeye dayalı yüz ifade tanıma

In this paper, a new approach has been proposed for improved facial expression recognition. The new approach is inspired by the compressive sensing theory and multi-resolution approach to facial expression problems. Initially, each image sample is decomposed into desired levels of its pyramids at different sizes and resolutions. At each level of the pyramid, features are extracted using a measurement matrix based on compressive sensing theory. These measurements are concatenated together to form a feature vector for the original image. The results obtained from the approach using three distance measurement classifiers (Manhattan, Euclidean, Cosine) and support vector machine are impressive and outperforms most of its counterpart algorithms in the literature using the same databases and settings

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[1] Fasel I, Juergen L. “Automatic facial expression analysis”. The Journal of Pattern recognition Society, 36(1), 259-275, 2003.
[2] Min T, Feng C. “Facial expression recognition and its application based on curvelet transform and PSO_SVM”. International Journal for Light and Electron Optics, 124(22), 5401–5406, 2013.
[3] Wenfei G, Cheng X, Venkatesh YV, Dong H, Hai L. “Facial expression recognition using radial encoding of local Gabor features and classifier synthesis”. The Journal of Pattern recognition Society, 45(1), 80-91, 2012.
[4] Shiqing Z, Lemin L, Zhijin Z. “Facial expression recognition based on Gabor wavelets and sparse representation”. 11th International Conference on Signal Processing, Beijing, China, 21-25 October 2012.
[5] Michael J, Shigeru A, Miyuki K, Jiro G. “Coding facial expressions with Gabor wavelets”. 3rd International Conference on Automatic Face and G
[6] Shishir B, Ganesh K. “Recognition of facial expressions using gabor wavelets and learning vector quantization”. Journals of Engineering Applications of Artificial Intelligence, 21(7), 1056–1064, 2008.
[7] Baochang Z, Shiguang S. “Histogram of Gabor phase patterns (HGPP): A novel object representation approach for face recognition”. IEEE Transactions on Image Processing, 16(1), 57–68, 2007.
[8] Yimo G, Zhengguang X. “Local Gabor phase difference pattern for face recognition”. 19th International Conference on Pattern Recognition, Tampa, FL, USA, 8-11 December 2008.
[9] Gonzalez RR, Woods RE. Digital Image Processing. 3rd ed. USA, Pearson publishers. 2008.
[10] Shannon C. “Communication in the presence of noise”. Proceedings of the Institute of Radio Engineers, 37(1), 10-21, 1949.
[11] Emamnuel C. “Compressive sampling”. Proceedings of the International Congress of Mathematicians, Madrid, Spain, 22-30 August, 2006.
[12] Shannon C. “A mathematical theory of communication”. Bell System Technical Journal, 27(5), 379-423, 1948.
[13] Baraniuk R. “Compressed sensing [Lecture Notes]”. IEEE Signal Processing Magazine, 124(2), 118-124, 2007.
[14] Eleyan A, Kose K, Cetin E. “Image feature extraction using compressive sensing”. Advances in Intelligent Systems and Computing, 233, 177-184, 2013.
[15] Kanade T, Cohn J, Tian Y. “Comprehensive database for facial expression analysis”. 4th International Conference on Automatic Face and Gesture Recognition, Grenoble, France, 28-30 March 2000.
[16] Wei-Lu C, Jian-Jiun D, Jun-Zuo L. “Facial expression recognition based on improved local binary pattern and class-regularized locality preserving projection”. An International Journal of Signal Processing, 117, 1-10, 2015.
[17] Ying Z, Fang X. “Combining LBP and adaboost for facial expression recognition”. 9th International Conference on Signal Processing, Beijing, China, 26-29 October 2008.
[18] Guo G, Dyer R. “Facial expression recognition based on Gabor histogram feature and MVBoost”. Journal of Computer Resource and Development, 44(3), 1089-1096, 2007.
[19] Huang M, Wang Z, Ying Z. “A new Method for Facial Expression Recognition based on Sparse Representation plus LBP”. 3rd International Congress on Image and Signal Processing, Yantai, China, 16-18 October 2010.
[20] Cai L, Yin Z. “A new approach of facial expression recognition based on contourlet transform”. International Conference on Wavelet Analysis and Pattern Recognition, Baoding, China, 12-15 July 2009.
[21] Zavaschi T, Koerich A, Oliveira L. “Facial expression recognition using ensemble of classifiers”. International Conference on Signal Acoustics, Speech and (ICASSP), Prague, Czech Republic, 22-27 May 2011.
[22] Shan C, Gong S. “Facial expression analysis across databases”. International Conference on Multimedia Technology, Hangzhou, China, 26-28 July 2011.
[23] Zhang Z, Xu C, Wang J, Chen X. “Facial expression recognition based on MB-LGBP feature and multi-level classification”. Advances in Intelligent and Soft Computing, 129, 37-42, 2011.
[24] Yeasin M, Bullot B, Sharma R. “From facial expression to level of interest: a spatio-temporal approach”. 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, USA, 27 June-2 July 2004.
[25] Aleksic P, Katsaggelos A. “Automatic facial expression recognition using facial animation parameters and multistream HMMS”. IEEE Transactions on Information Forensics and Security, 1(1) 3-11, 2006.
[26] Li Z, Imai J, Kaneko M. “Facial expression recognition using facial-component-based bag of words and phog descriptors”. The Journal of the Institute of Image Information and Television Engineers, 64(2), 230-236, 2010.
[27] Shan C, Gong S, McOwan P. “Robust facial expression recognition using local binary patterns”. International Conference on Image Processing, Genova, Italy, 14 September 2005.
[28] Zhao G, Pietik M. “Dynamic texture recognition using local binary patterns with an application to facial expressions”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(6), 915-928, 2007.
[29] Bartlett M, Littlewort G, Fasel I, Movellan R. “Real-time face detection and facial expression recognition: development and application to human computer interaction”. Conference on Computer Vision and Pattern Recognition Workshop, Madison, Wisconsin, USA, 16-22 June 2003.
[30] Littlewort G, Bartlett M, Fasel I, Susskind J, Movellan J. “Dynamics of facial expression extracted automatically from video”. Journal of Image and Vision Computing, 24(6), 615-625, 2006.
[31] Tian Y. “Evaluation of face resolution for expression analysis”. Conference on Computer Vision and Pattern Recognition Workshop, Washington, DC, USA, 27 June-2 July 2004.