Recognition of facial expressions extracting salient features using local binary patterns and histogram of oriented gradients

The extraction of accurate facial landmarks play an exceedingly significant role for recognition of facial expressions. This paper proffers an approach in order for recognition of varied expressions of the faces by incipiently implementing the LBP (Local Binary Pattern) descriptor on the facial landmarks and then the HOG (Histogram of Oriented Gradients) descriptor is applied on the extracted LBP features and finally they are classified into seven different expressions of the face: anger, happiness, surprise, disgust, sadness, neutral and fear adopting the Multiclass support vector machine (SVM). The structured model implemented on the Multimedia Understanding Group (MUG) and the Japanese Female Facial Expression (JAFFE) databases signifies 96% and 97.1% rate of recognition respectively and found to be functioning considerably well under diverse illuminating variation conditions.

[1]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[2]  P. Ekman,et al.  Facial action coding system , 2019 .

[3]  Zhengyou Zhang,et al.  Comparison between geometry-based and Gabor-wavelets-based facial expression recognition using multi-layer perceptron , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[4]  Anastasios Delopoulos,et al.  The MUG facial expression database , 2010, 11th International Workshop on Image Analysis for Multimedia Interactive Services WIAMIS 10.

[5]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[6]  Deepak Ghimire,et al.  Facial expression recognition based on region specific appearance and geometric features , 2015, 2015 Tenth International Conference on Digital Information Management (ICDIM).

[7]  Alaa Eleyan,et al.  Gabor wavelet transform based facial expression recognition using PCA and LBP , 2014, 2014 22nd Signal Processing and Communications Applications Conference (SIU).

[8]  Ammad Ali,et al.  Face Recognition with Local Binary Patterns , 2012 .

[9]  Shaogang Gong,et al.  Facial expression recognition based on Local Binary Patterns: A comprehensive study , 2009, Image Vis. Comput..

[10]  Raphael C.-W. Phan,et al.  Facial Expression Recognition in the Encrypted Domain Based on Local Fisher Discriminant Analysis , 2013, IEEE Transactions on Affective Computing.

[11]  Abdellah Madani,et al.  Facial Expression Recognition Using Decision Trees , 2016, 2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV).

[12]  Guodong Guo,et al.  Learning from examples in the small sample case: face expression recognition , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[13]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).