The Analysis of Facial Feature Deformation using Optical Flow Algorithm

Facial features deformed according to the intended facial expression. Specific facial features are associated with specific facial expression, i.e. happy means the deformation of mouth. This paper presents the study of facial feature deformation for each facial expression by using an optical flow algorithm and segmented into three different regions of interest. The deformation of facial features shows the relation between facial the and facial expression. Based on the experiments, the deformations of eye and mouth are significant in all expressions except happy. For happy expression, cheeks and mouths are the significant regions. This work also suggests that different facial features' intensity varies in the way that they contribute to the recognition of the different facial expression intensity. The maximum magnitude across all expressions is shown by the mouth for surprise expression which is 9x10 -4 . While the minimum magnitude is shown by the mouth for angry expression which is 0.4x10 -4 .

[1]  D. N. F. Awang Iskandar,et al.  Facial Action Units Analysis using Rule-Based Algorithm , 2018 .

[2]  Vijayan K. Asari,et al.  An improved face recognition technique based on modular PCA approach , 2004, Pattern Recognit. Lett..

[3]  Andreas Ranftl,et al.  Face Tracking Using Optical Flow , 2015, 2015 International Conference of the Biometrics Special Interest Group (BIOSIG).

[4]  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.

[5]  Qiang Ji,et al.  A unified probabilistic framework for measuring the intensity of spontaneous facial action units , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

[6]  Chairol Mohd Feroz,et al.  Vehicle tracking and speed estimation for traffic surveillance , 2014 .

[7]  Jacey-Lynn Minoi,et al.  Unsupervised segmentation of action segments in egocentric videos using gaze , 2017, 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA).

[8]  Cheng-Chin Chiang,et al.  A Component-based Face Synthesizing Method , 2009 .

[9]  Nicu Sebe,et al.  Multimodal Human Computer Interaction: A Survey , 2005, ICCV-HCI.

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

[11]  F. Albu,et al.  NEURAL NETWORK APPROACHES FOR CHILDREN'S EMOTION RECOGNITION IN INTELLIGENT LEARNING APPLICATIONS , 2015 .

[13]  Michael S. Lew,et al.  A Framework for Real-Time Face and Facial Feature Tracking using Optical Flow Pre-estimation and Template Tracking , 2010, ArXiv.

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

[15]  P. Ekman,et al.  Facial action coding system: a technique for the measurement of facial movement , 1978 .

[16]  Fernando Alonso-Fernandez,et al.  Face Tracking using Optical Flow Development of a Real-Time AdaBoost Cascade Face Tracker , 2015, BIOSIG.

[17]  Ujir Hamimah,et al.  3D facial expression intensity measurement analysis , 2017 .

[18]  Ying Cui,et al.  Facial Feature Points Tracking Based on AAM with Optical Flow Constrained Initialization , 2012 .

[19]  R. Gur,et al.  Computerized measurement of facial expression of emotions in schizophrenia , 2007, Journal of Neuroscience Methods.

[20]  Shang-Hong Lai,et al.  An Optical Flow-Based Approach to Robust Face Recognition Under Expression Variations , 2010, IEEE Transactions on Image Processing.

[21]  Hamimah Ujir,et al.  3D facial expression classification using a statistical model of surface normals and a modular approach , 2013 .

[22]  Hamimah Ujir,et al.  A modular approach and voting scheme on 3D face recognition , 2014, 2014 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS).

[23]  Fernando De la Torre,et al.  Interactive region-based linear 3D face models , 2011, ACM Trans. Graph..

[24]  Athanasios Psaltis,et al.  Optical flow for dynamic facial expression recognition. , 2013 .

[25]  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).

[26]  Pia Rotshtein,et al.  Identification of Emotional Facial Expressions: Effects of Expression, Intensity, and Sex on Eye Gaze , 2016, PloS one.

[27]  Hamimah Ujir,et al.  Measuring task performance using gaze regions , 2015, 2015 9th International Conference on IT in Asia (CITA).

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