Intrinsic two-dimensional local structures for micro-expression recognition

An elapsed facial emotion involves changes of facial contour due to the motions (such as contraction or stretch) of facial muscles located at the eyes, nose, lips and etc. Thus, the important information such as corners of facial contours that are located in various regions of the face are crucial to the recognition of facial expressions, and even more apparent for micro-expressions. In this paper, we propose the first known notion of employing intrinsic two-dimensional (i2D) local structures to represent these features for micro-expression recognition. To retrieve i2D local structures such as phase and orientation, higher order Riesz transforms are employed by means of monogenic curvature tensors. Experiments performed on micro-expression datasets show the effectiveness of i2D local structures in recognizing micro-expressions.

[1]  Guoying Zhao,et al.  CASME II: An Improved Spontaneous Micro-Expression Database and the Baseline Evaluation , 2014, PloS one.

[2]  John See,et al.  Monogenic Riesz wavelet representation for micro-expression recognition , 2015, 2015 IEEE International Conference on Digital Signal Processing (DSP).

[3]  P. Ekman Telling lies: clues to deceit in the marketplace , 1985 .

[4]  Matti Pietikäinen,et al.  Improved Spatiotemporal Local Monogenic Binary Pattern for Emotion Recognition in The Wild , 2014, ICMI.

[5]  John See,et al.  Efficient Spatio-Temporal Local Binary Patterns for Spontaneous Facial Micro-Expression Recognition , 2015, PloS one.

[6]  KokSheik Wong,et al.  Subtle Expression Recognition Using Optical Strain Weighted Features , 2014, ACCV Workshops.

[7]  Gerald Sommer,et al.  2D Image Analysis by Generalized Hilbert Transforms in Conformal Space , 2008, ECCV.

[8]  L. Fleischer Telling Lies Clues To Deceit In The Marketplace Politics And Marriage , 2016 .

[9]  P. Ekman,et al.  Nonverbal leakage and clues to deception. , 1969, Psychiatry.

[10]  Matti Pietikäinen,et al.  A Spontaneous Micro-expression Database: Inducement, collection and baseline , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

[11]  D. Hestenes,et al.  Clifford Algebra to Geometric Calculus: A Unified Language for Mathematics and Physics , 1984 .

[12]  Matti Pietikäinen,et al.  Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Michael Felsberg,et al.  The monogenic signal , 2001, IEEE Trans. Signal Process..

[14]  John See,et al.  LBP with Six Intersection Points: Reducing Redundant Information in LBP-TOP for Micro-expression Recognition , 2014, ACCV.

[15]  KokSheik Wong,et al.  Optical strain based recognition of subtle emotions , 2014, 2014 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS).

[16]  Matti Pietikäinen,et al.  Spatiotemporal Local Monogenic Binary Patterns for Facial Expression Recognition , 2012, IEEE Signal Processing Letters.