Mouth Features Extraction for Emotion Analysis

Face emotions analysis is one of the fundamental techniques that might be exploited in a natural human-computer interaction process and thus is, one of the most studied topics in current computer vision literature. In consequence face features extraction is an indispensable element of the face emotion analysis as it influences decision making performance. The paper concentrates on mouth features extraction, which next to eye region features becomes one of the most representative face regions in the context of emotions retrieval. In the paper original, gradient based, mouth features extraction method was presented. Its high performance (exceeding 90 \(\%\) for selected features) was also verified for a subset of the Yale images database.

[1]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[2]  Yong-Hwan Lee,et al.  Facial landmarks detection using improved active shape model on android platform , 2013, Multimedia Tools and Applications.

[3]  Ayyaz Hussain,et al.  Survey of various feature extraction and classification techniques for facial expression recognition , 2012 .

[4]  Zhengyou Zhang,et al.  A Survey of Recent Advances in Face Detection , 2010 .

[5]  Mau-Tsuen Yang,et al.  Facial Expression Recognition for Learning Status Analysis , 2011, HCI.

[6]  Ioannis Pitas,et al.  A novel method for automatic face segmentation, facial feature extraction and tracking , 1998, Signal Process. Image Commun..

[7]  Wen Gao,et al.  Locally Assembled Binary (LAB) feature with feature-centric cascade for fast and accurate face detection , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Rainer Lienhart,et al.  An extended set of Haar-like features for rapid object detection , 2002, Proceedings. International Conference on Image Processing.

[9]  Suchita Goswami,et al.  An Extensive Survey on Feature Extraction Techniques for Facial Image Processing , 2014, 2014 International Conference on Computational Intelligence and Communication Networks.

[10]  Javier Lorenzo-Navarro,et al.  Combining Face and Facial Feature Detectors for Face Detection Performance Improvement , 2012, CIARP.

[11]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[12]  Guoyin Wang,et al.  Expression Recognition Methods Based on Feature Fusion , 2010, Brain Informatics.

[13]  Sung-Il Chien,et al.  Face and Facial Landmarks Location Based on Log-Polar Mapping , 2000, Biologically Motivated Computer Vision.

[14]  Tao Yan,et al.  Research on the Methods of Chinese Text Classification using Bayes and Language Model , 2008, 2008 Chinese Conference on Pattern Recognition.

[15]  Shu Liang,et al.  Improved detection of landmarks on 3D human face data , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[16]  Mi-Hye Kim,et al.  Comparison of Lip Image Feature Extraction Methods for Improvement of Isolated Word Recognition Rate , 2015 .

[17]  Changyin Sun,et al.  Gender classification using 3D statistical models , 2016, Multimedia Tools and Applications.

[18]  Takeo Kanade,et al.  Computer recognition of human faces , 1980 .

[19]  Alex Pentland,et al.  View-based and modular eigenspaces for face recognition , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Erik Hjelmås,et al.  Face Detection: A Survey , 2001, Comput. Vis. Image Underst..

[21]  Takeo Kanade,et al.  A statistical method for 3D object detection applied to faces and cars , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[22]  Trent W. Lewis,et al.  Lip Feature Extraction Using Red Exclusion , 2000, VIP.

[23]  Lianwen Jin,et al.  Realistic Smile Expression Recognition Using Biologically Inspired Features , 2011, Australasian Conference on Artificial Intelligence.

[24]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[25]  Jing-Yu Yang,et al.  Robust Facial Feature Location on Gray Intensity Face , 2009, PSIVT.

[26]  Roberto Brunelli,et al.  Face Recognition: Features Versus Templates , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Oscar Déniz-Suárez,et al.  A comparison of face and facial feature detectors based on the Viola–Jones general object detection framework , 2011, Machine Vision and Applications.

[28]  B. Michaelis,et al.  Facial expression recognition based on Haar-like feature detection , 2008, Pattern Recognition and Image Analysis.

[29]  Timothy F. Cootes,et al.  Extraction of Visual Features for Lipreading , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[31]  Oksam Chae,et al.  Robust Facial Expression Recognition Based on Local Directional Pattern , 2010 .

[32]  Wei Wu,et al.  An algorithm of lips secondary positioning and feature extraction based on YCbCr color space , 2015 .

[33]  Mohamed Abdou Berbar Three robust features extraction approaches for facial gender classification , 2013, The Visual Computer.