Facial Expression Recognition Based on Local Binary Patterns and Kernel Discriminant Isomap

Abstract

Facial expression recognition is an interesting and challenging subject. Considering the nonlinear manifold structure of facial images, a new kernel-based manifold learning method, called kernel discriminant isometric mapping (KDIsomap), is proposed. KDIsomap aims to nonlinearly extract the discriminant information by maximizing the interclass scatter while minimizing the intraclass scatter in a reproducing kernel Hilbert space. KDIsomap is used to perform nonlinear dimensionality reduction on the extracted local binary patterns (LBP) facial features, and produce low-dimensional discrimimant embedded data representations with striking performance improvement on facial expression recognition tasks. The nearest neighbor classifier with the Euclidean metric is used for facial expression classification. Facial expression recognition experiments are performed on two popular facial expression databases, i.e., the JAFFE database and the Cohn-Kanade database. Experimental results indicate that KDIsomap obtains the best accuracy of 81.59% on the JAFFE database, and 94.88% on the Cohn-Kanade database. KDIsomap outperforms the other used methods such as principal component analysis (PCA), linear discriminant analysis (LDA), kernel principal component analysis (KPCA), kernel linear discriminant analysis (KLDA) as well as kernel isometric mapping (KIsomap).

Facial emotion recognition using empirical mode decomposition

“…Some of them have utilized pixel based (Rahulamathavan, Phan, Chambers, & Parish, 2013;Wang & Ruan, 2010), Gabor filter (Deng, Jin, Zhen, & Huang, 2005;Donato, Bartlett, Hager, Ekman, & Sejnowski, 1999;Owusu, Zhan, & Mao, 2014), wavelet transform (Kazmi, Qurat-ul-Ain, & Jaffar, 2012;Shih, Chuang, & Wang, 2008), facial contour (Gu, Venkatesh, & Xiang, 2010), edge and skin detection (Ilbeygi & Hosseini, 2012), discrete cosine transform (Gupta, Agrwal, Meena, & Nain, 2011;Kharat & Dudul, 2009) and local binary pattern (Feng, Hadid, & Pietik ainen, 2005;Liu, Yi, & Wang, 2009;Luo, Wu, & Zhang, 2013;Moore & Bowden, 2009;Shan, Gong, & McOwan, 2009;Zhang, Zhao, & Lei, 2012;Zhao & Zhang, 2011) have gained lots of successful experiences. Eventhough facial emotion recognitions have achieved a certain level of success, however the performance is far from human perception.…”

Efficient Privacy-Preserving Facial Expression Classification

“…Note that for some classes, e.g. the SA class, the recognition rate tends to be lower than other expression classes because the class is highly confused with other expressions [36]. We consider only the top recognition case (i.e., p = 90 and l = 40) to evaluate the proposed scheme.…”

Robust Facial Expression Recognition via Compressive Sensing

“…Likewise, the LBP features are obtained by applying the LBP operator to the whole region of the cropped facial images. Similar to the settings in [13–15,38], we selected the 59-bin operator, ${\mathit{LBP}}_{P,R}^{u2}$, where the notation ( P , R ) denotes a neighborhood of P equally spaced sampling points on a circle of radius of R that form a circularly symmetric neighbor set, and the superscript u2 in ${\mathit{LBP}}_{P,R}^{u2}$ indicates using only uniform patterns and labeling all remaining patterns with a single label. And then we divided the 110 × 150 pixels facial images into 18 × 21 pixels regions, giving a good trade-off between recognition performance and feature vector length.…”