Remarks on Computational Facial Expression Recognition from HOG Features Using Quaternion Multi-layer Neural Network

Facial expression recognition is an important technology in human-computer interaction. This study investigates a method for facial expression recognition using quaternion neural networks. A multi-layer quaternion neural network that conducts its learning using a quaternion back-propagation algorithm is employed to design the facial expression recognition system. The input feature vector of the recognition system is composed of histograms of oriented gradients calculated from an input facial expression image, and the output vector of the quaternion neural network indicates the class of facial expressions such as happiness, anger, sadness, fear, disgust, surprise and neutral. Computational experimental results show the feasibility of the proposed method for recognising human facial expressions.

[1]  Danilo P. Mandic,et al.  Quaternion-Valued Nonlinear Adaptive Filtering , 2011, IEEE Transactions on Neural Networks.

[2]  P. Ekman.  Unmasking The Face , 1975 .

[3]  Michael J. Lyons,et al.  Coding facial expressions with Gabor wavelets , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[4]  A. A. El-Harby,et al.  Face Recognition: A Literature Review , 2008 .

[5]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[6]  Jonas Schreiber.  Complex Valued Neural Networks Utilizing High Dimensional Parameters , 2016 .

[7]  Hiromi Kusamichi,et al.  A New Scheme for Color Night Vision by Quaternion Neural Network , 2004 .

[8]  Gerald Sommer,et al.  On Clifford neurons and Clifford multi-layer perceptrons , 2008, Neural Networks.

[9]  P. Arena,et al.  Quaternionic Multilayer Perceptrons for Chaotic Time Series Prediction , 1996 .

[10]  Yasuaki Kuroe,et al.  Models of clifford recurrent neural networks and their dynamics , 2011, The 2011 International Joint Conference on Neural Networks.

[11]  Gerald Sommer,et al.  Geometric Computing with Clifford Algebras , 2001, Springer Berlin Heidelberg.

[12]  Tetsuya Takiguchi,et al.  3D human posture estimation using the HOG features from monocular image , 2008, 2008 19th International Conference on Pattern Recognition.

[13]  Zhihong Zeng,et al.  A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions , 2009, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  T. Ogawa,et al.  Quaternion network inversion for solving inverse problems , 2012, 2012 Proceedings of SICE Annual Conference (SICE).

[15]  Lijun Yin,et al.  Static and dynamic 3D facial expression recognition: A comprehensive survey , 2012, Image Vis. Comput..

[16]  Rafael A. Calvo,et al.  Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications , 2010, IEEE Transactions on Affective Computing.