Techniques for Mimicry and Identity Blending Using Morph Space PCA

We describe a face modelling tool allowing image representation in a high-dimensional morph space, compression to a small number of coefficients using PCA [1], and expression transfer between face models by projection of the source morph description (a parameterisation of complex facial motion) into the target morph space. This technique allows creation of an identity-blended avatar model whose high degree of realism enables diverse applications in visual psychophysics, stimulus generation for perceptual experiments, animation and affective computing.

[1]  Marc Alexa,et al.  Face-to-face with your assistant. Realization issues of animated user interface agents for home appliances , 2001, Comput. Graph..

[2]  M. Hasselmo,et al.  The role of expression and identity in the face-selective responses of neurons in the temporal visual cortex of the monkey , 1989, Behavioural Brain Research.

[3]  Zicheng Liu,et al.  Expressive expression mapping with ratio images , 2001, SIGGRAPH.

[4]  J. Haxby,et al.  The distributed human neural system for face perception , 2000, Trends in Cognitive Sciences.

[5]  Cecilia Heyes,et al.  Self-recognition of avatar motion: how do I know it's me? , 2012, Proceedings of the Royal Society B: Biological Sciences.

[6]  Stephen Grossberg,et al.  How do children learn to follow gaze , share joint attention , imitate their teachers , and use tools during social interactions ? , 2010 .

[7]  F. Galton Composite Portraits, Made by Combining Those of Many Different Persons Into a Single Resultant Figure. , 1879 .

[8]  A. Treves,et al.  Morphing Marilyn into Maggie dissociates physical and identity face representations in the brain , 2005, Nature Neuroscience.

[9]  Susan E. Brennan,et al.  From the Leonardo Archive , 2007, Leonardo.

[10]  A. Young,et al.  Dissociable face processing impairments after brain injury. , 1991, Journal of clinical and experimental neuropsychology.

[11]  Barry-John Theobald,et al.  Real-time expression cloning using appearance models , 2007, ICMI '07.

[12]  Laurent Itti,et al.  Realistic avatar eye and head animation using a neurobiological model of visual attention , 2004, SPIE Optics + Photonics.

[13]  Peter W. McOwan,et al.  Real-Time Emotion Recognition Using Biologically Inspired Models , 2003, AVBPA.

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

[15]  V. Rajan A Realistic Video Avatar System for Networked Virtual Environments , 2002 .

[16]  J. N. Bassili Facial motion in the perception of faces and of emotional expression. , 1978, Journal of experimental psychology. Human perception and performance.

[17]  Giovanni Ramponi,et al.  Image enhancement via adaptive unsharp masking , 2000, IEEE Trans. Image Process..

[18]  Shree K. Nayar,et al.  Face swapping: automatically replacing faces in photographs , 2008, SIGGRAPH 2008.

[19]  Andrew J. Calder,et al.  PII: S0042-6989(01)00002-5 , 2001 .

[20]  Shyan-Shu Shieh,et al.  An experimental study of model predictive control based on artificial neural networks , 2003 .

[21]  Peter W. McOwan,et al.  The algorithms of natural vision: the multi-channel gradient model , 1995 .

[22]  Alex Pentland,et al.  Facial expression recognition using a dynamic model and motion energy , 1995, Proceedings of IEEE International Conference on Computer Vision.

[23]  K. Grammer,et al.  Human (Homo sapiens) facial attractiveness and sexual selection: the role of symmetry and averageness. , 1994, Journal of comparative psychology.

[24]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[25]  Tim Valentine,et al.  Face–space models of face recognition. , 2001 .

[26]  Thomas Vetter,et al.  A morphable model for the synthesis of 3D faces , 1999, SIGGRAPH.