Detection and expression classification systems for face images (FADECS)

Towards building new, friendlier human-computer interaction systems and multimedia interactive services systems, we developed a neural network-based image processing system (called FADECS), which first determines automatically whether or not there are any faces in given images and, if so, returns the location and extent of each face. Next, FADECS uses neural network-based classifiers which allow the classification of several facial expressions from features that we develop and describe.

[1]  Ioanna-Ourania Stathopoulou,et al.  A NEW NEURAL NETWORK-BASED METHOD FOR FACE DETECTION IN IMAGES AND APPLICATIONS IN BIOINFORMATICS , 2004 .

[2]  Demetri Terzopoulos,et al.  Analysis and Synthesis of Facial Image Sequences Using Physical and Anatomical Models , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Aleix M. Martinez,et al.  The AR face database , 1998 .

[4]  Takeo Kanade,et al.  Comprehensive database for facial expression analysis , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[5]  Larry S. Davis,et al.  Human expression recognition from motion using a radial basis function network architecture , 1996, IEEE Trans. Neural Networks.

[6]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[7]  Takeo Kanade,et al.  Rotation invariant neural network-based face detection , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[8]  Takeo Kanade,et al.  Neural Network-Based Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Thomas S. Huang,et al.  Human face detection in a complex background , 1994, Pattern Recognit..

[10]  Michael C. Burl,et al.  Finding faces in cluttered scenes using random labeled graph matching , 1995, Proceedings of IEEE International Conference on Computer Vision.

[11]  Alex Pentland,et al.  Coding, Analysis, Interpretation, and Recognition of Facial Expressions , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Maria Virvou,et al.  Mobile educational features in authoring tools for personalised tutoring , 2005, Comput. Educ..

[13]  Thomas S. Huang,et al.  Face detection with information-based maximum discrimination , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[14]  Ioanna-Ourania Stathopoulou,et al.  An improved neural-network-based face detection and facial expression classification system , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[15]  Kuo-Chin Fan,et al.  Triangle-based approach to the detection of human face , 2001, Pattern Recognit..

[16]  Rae-Hong Park,et al.  Recognition of human front faces using knowledge-based feature extraction and neurofuzzy algorithm , 1996, Pattern Recognit..

[17]  P. Ekman,et al.  Unmasking the Face: A Guide to Recognizing Emotions From Facial Expressions , 1975 .

[18]  Jitendra Malik,et al.  Efficient spatiotemporal grouping using the Nystrom method , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[19]  Garrison W. Cottrell,et al.  A Six-Unit Network is All You Need to Discover Happiness , 2000 .

[20]  Tomaso A. Poggio,et al.  Example-Based Learning for View-Based Human Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Christine L. Lisetti,et al.  Automatic facial expression interpretation: Where human-computer interaction, artificial intelligence and cognitive science intersect , 2000 .

[22]  Paul Juell,et al.  A hierarchical neural network for human face detection , 1996, Pattern Recognit..

[23]  Michael J. Black,et al.  Recognizing facial expressions under rigid and non-rigid facial motions , 1995 .