Face detection and recognition of natural human emotion using Markov random fields

This paper presents an integrated system for emotion detection. In this research effort, we have taken into account the fact that emotions are most widely represented with eye and mouth expressions. The proposed system uses color images and it is consisted of three modules. The first module implements skin detection, using Markov random fields models for image segmentation and skin detection. A set of several colored images with human faces have been considered as the training set. A second module is responsible for eye and mouth detection and extraction. The specific module uses the HLV color space of the specified eye and mouth region. The third module detects the emotions pictured in the eyes and mouth, using edge detection and measuring the gradient of eyes’ and mouth’s region figure. The paper provides results from the system application, along with proposals for further research.

[1]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[2]  Dimitris N. Metaxas,et al.  Optical Flow Constraints on Deformable Models with Applications to Face Tracking , 2000, International Journal of Computer Vision.

[3]  Dario Maio,et al.  Real-time face location on gray-scale static images , 2000, Pattern Recognition.

[4]  J. Besag.  Spatial Interaction and the Statistical Analysis of Lattice Systems , 1974 .

[5]  Rama Chellappa,et al.  Human and machine recognition of faces: a survey , 1995, Proc. IEEE.

[6]  Narendra Ahuja,et al.  Detecting Faces in Images: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Leonhard Held,et al.  Gaussian Markov Random Fields: Theory and Applications , 2005 .

[8]  Ying Li,et al.  Multi-template ASM Method for feature points detection of facial image with diverse expressions , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

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

[10]  George N. Votsis,et al.  Emotion recognition in human-computer interaction , 2001, IEEE Signal Process. Mag..

[11]  Maja Pantic,et al.  Facial action recognition for facial expression analysis from static face images , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[12]  Kostas Karpouzis,et al.  Dealing with feature uncertainty in facial expression recognition , 2006, Int. J. Intell. Syst. Technol. Appl..

[13]  Shih-Fu Chang,et al.  A highly efficient system for automatic face region detection in MPEG video , 1997, IEEE Trans. Circuits Syst. Video Technol..

[14]  James M. Rehg,et al.  Statistical Color Models with Application to Skin Detection , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[15]  Maja Pantic,et al.  Automatic Analysis of Facial Expressions: The State of the Art , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Kin-Man Lam,et al.  A robust scheme for live detection of human faces in color images , 2003, Signal Process. Image Commun..

[17]  Anil K. Jain,et al.  Markov random fields : theory and application , 1993 .

[18]  Vicki Bruce,et al.  Face Recognition: From Theory to Applications , 1999 .

[19]  Raphaël Féraud,et al.  A Fast and Accurate Face Detector Based on Neural Networks , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

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

[22]  Kostas Karpouzis,et al.  Emotion recognition through facial expression analysis based on a neurofuzzy network , 2005, Neural Networks.

[23]  Gerhard Winkler,et al.  Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction , 2002 .

[24]  R. Schroeder.  LITERATURE SURVEY , 1981 .

[25]  Kostas Karpouzis,et al.  Robust Feature Detection for Facial Expression Recognition , 2007, EURASIP J. Image Video Process..

[26]  Gerhard Winkler,et al.  Image analysis, random fields and dynamic Monte Carlo methods: a mathematical introduction , 1995, Applications of mathematics.

[27]  Gilles Celeux,et al.  EM procedures using mean field-like approximations for Markov model-based image segmentation , 2003, Pattern Recognit..

[28]  Baozong Yuan,et al.  A novel approach for human face detection from color images under complex background , 2001, Pattern Recognit..

[29]  King Ngi Ngan,et al.  Face segmentation using skin-color map in videophone applications , 1999, IEEE Trans. Circuits Syst. Video Technol..