Analysis of range images used in 3D facial expression recognition

Researches on 3D facial expression recognition (3D FER) have been greatly fostered by the creation of BU-3DFE (Binghamton University 3D Facial Expression) database, but given limited 3D methods, they encounter some bottlenecks now. A new approach to solve this problem is to transform facial expression from 3D models into 2D data, thus range image emerges, playing an intermediary role in current researches. There are various range images up to date, but it's relatively tough to identify which one is optimal for 3D FER. This paper aims to analyse the performances of different range images which are generated for 3D FER. Therefore, different features and classifiers are investigated in expression recognition framework. The recognition ratios of proposed experiments are higher than 88%, almost falling within the same scope. It reveals that range images still retain main discriminative information, which is significant for 3D FER. Also, there are a few differences among various range images' performances. More importantly, the range images can make 3D FER implement automatically. The findings above are so remarkable that they would benefit the future research of 3D facial expression recognition.

[1]  Ashraf A. Kassim,et al.  Resampling Approach to Facial Expression Recognition Using 3D Meshes , 2010, 2010 20th International Conference on Pattern Recognition.

[2]  Montse Pardàs,et al.  Facial animation parameters extraction and expression recognition using Hidden Markov Models , 2002, Signal Process. Image Commun..

[3]  Yoichi Sato,et al.  Pose-Invariant Facial Expression Recognition Using Variable-Intensity Templates , 2007, ACCV.

[4]  Garrison W. Cottrell,et al.  Representing Face Images for Emotion Classification , 1996, NIPS.

[5]  Hasan Demirel,et al.  Facial Expression Recognition Using 3D Facial Feature Distances , 2007, ICIAR.

[6]  Maurício Pamplona Segundo,et al.  3D Face Recognition Using Simulated Annealing and the Surface Interpenetration Measure , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Yuxiao Hu,et al.  Spontaneous Emotional Facial Expression Detection , 2006, J. Multim..

[8]  Xiaoou Tang,et al.  Robust 3D Face Recognition by Local Shape Difference Boosting , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Bülent Sankura,et al.  Comparative Evaluation of 3 D versus 2 D Modality for Automatic Detection of Facial Action Units , 2011 .

[10]  Qiuqi Ruan,et al.  Facial expression recognition with local Gabor filters , 2010, IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS.

[11]  Thomas S. Huang,et al.  3D facial expression recognition based on automatically selected features , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[12]  Jun Wang,et al.  3D Facial Expression Recognition Based on Primitive Surface Feature Distribution , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[13]  Hasan Demirel,et al.  Feature selection for person-independent 3D facial expression recognition using NSGA-II , 2009, 2009 24th International Symposium on Computer and Information Sciences.

[14]  Arman Savran,et al.  Comparative evaluation of 3D vs. 2D modality for automatic detection of facial action units , 2012, Pattern Recognit..

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

[16]  Alberto Del Bimbo,et al.  A Set of Selected SIFT Features for 3D Facial Expression Recognition , 2010, 2010 20th International Conference on Pattern Recognition.

[17]  Thomas S. Huang,et al.  3D facial expression recognition based on properties of line segments connecting facial feature points , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[18]  Kazuhiro Hotta.  Robust face recognition under partial occlusion based on support vector machine with local Gaussian summation kernel , 2008, Image Vis. Comput..

[19]  Ioannis A. Kakadiaris,et al.  Expressive Maps for 3D Facial Expression Recognition , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[20]  Gwen Littlewort,et al.  Real Time Face Detection and Facial Expression Recognition: Development and Applications to Human Computer Interaction. , 2003, 2003 Conference on Computer Vision and Pattern Recognition Workshop.

[21]  Wang Chengzhang,et al.  BJUT-3D Large Scale 3D Face Database and Information Processing , 2009 .

[22]  Ruan Qiu-qi.  Facial Expression Recognition Using Discriminant Locality Preserving Projections , 2009 .

[23]  Maurício Pamplona Segundo,et al.  Automatic 3D facial segmentation and landmark detection , 2007, 14th International Conference on Image Analysis and Processing (ICIAP 2007).

[24]  Ashraf A. Kassim,et al.  A novel approach to classification of facial expressions from 3D-mesh datasets using modified PCA , 2009, Pattern Recognit. Lett..

[25]  Maurício Pamplona Segundo,et al.  Automatic Face Segmentation and Facial Landmark Detection in Range Images , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[26]  Jun Wang,et al.  A 3D facial expression database for facial behavior research , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[27]  H. Demirel,et al.  3D facial expression recognition with geometrically localized facial features , 2008, 2008 23rd International Symposium on Computer and Information Sciences.

[28]  Gérard G. Medioni,et al.  Performance of Geometrix ActiveID^TM 3D Face Recognition Engine on the FRGC Data , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[29]  Mi Lu,et al.  3-D Face Recognition Based on Warped Example Faces , 2007, IEEE Transactions on Information Forensics and Security.

[30]  Gwen Littlewort,et al.  Recognizing facial expression: machine learning and application to spontaneous behavior , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[31]  Sílvia Cristina Dias Pinto,et al.  3D facial expression analysis by using 2D AND 3D wavelet transforms , 2011, 2011 18th IEEE International Conference on Image Processing.

[32]  Thomas S. Huang,et al.  Capturing subtle facial motions in 3D face tracking , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[33]  Hüseyin Özkaramanli,et al.  Face Modeling and Adaptive Texture Mapping for Model Based Video Coding , 2005, CAIP.

[34]  BennamounMohammed,et al.  An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition , 2007 .

[35]  Chi Chung Ko,et al.  Using moment invariants and HMM in facial expression recognition , 2002, Pattern Recognit. Lett..

[36]  David P. Dobkin,et al.  The quickhull algorithm for convex hulls , 1996, TOMS.

[37]  Shaogang Gong,et al.  Synthesis and recognition of facial expressions in virtual 3D views , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..