Categorising  Identity  from  Facial  Motion

Due to the non-convexity of the functionals which are typi- cally used in global patch-based methods, having a reliable initialization is crucial for the local minimization.

Procrustes distance (learnt or classical) kernel based classifiers are insensitive to translation, scaling and rotation, which is a key factor in recognizing facial expressions both

According to the above-mentioned issues, we pro- pose in this paper a different approach to measure the bilateral facial asymmetry, when first avoiding the mirror plane detection,

Although recent deep learning methods achieve better performances for macro expression recognition, it is impor- tant to emphasize the relevance of a unified method that

As the animations closely resembled real human facial movement in the absence of spatial cues, it is possible to generalise the current data to faces in real life.. Indeed, there

To evaluate the quality of a set of mark- ers, we use a blendshape-based synthesis technique that learns the mapping between marker positions and blendshape weights, and we

The constancy of the pointwise Hölder exponent of FBM is a strong limitation in many situations (synthesis of articial mountains, detection of cancer tumors in medical

Recently, with the surge of deep learning, more promising results have been reported [3,4,6,5,7] tackling the automatic facial emotion recognition task using both geometric