Facial expression recognition of 3D image using facial action coding system (FACS)

Facial expression or mimic is one of the results of muscle motion on the face. In a large Indonesian dictionary, the expression is a disclosure or process of declaring, i.e. showing or expressing intentions, ideas of feelings and so on. Facial expression is affected by the cranial nerve VII or Nervus Facialis. In research conducted Paul Ekman got a standardization of expression in the format of a movement called the Facial Action Coding System (FACS). In his research, Paul Ekman said six basic expressions of happiness, sadness, shock, fear, anger, and disgust. In muscle anatomy, that every moving muscle must be contraction, and in the event of contraction, the muscle will expand or swell. Muscles are divided into three parts of origo and insersio as the tip of muscle and belli as the midpoint of the muscle, so any movement occurs then the muscle part belli will expand or swell. Data retrieval technique that is by recording data in 3D, any contraction occurs then the belli part of the muscle will swell and this data will be processed and compared. From this data processing will be obtained the maximum strength of contraction that will be used as a reference for the magnitude of expression made by the model. In the detection of expression is ecluidience distance by comparing the initial data with movement data. The result of this research is a detection of expression and the amount of expression that occurs. A conclusion of this research, we can reconstruction of facial expression detection using FACS, for the example the happiness expression using AU 6 and AU 12 and in this research AU 6 and AU 12 in area 1 and area 4, and in this area it so higher than the other

Affective Computing

This book provides an overview of state of the art research in Affective Computing. It presents new ideas, original results and practical experiences in this increasingly important research field. The book consists of 23 chapters categorized into four sections. Since one of the most important means of human communication is facial expression, the first section of this book (Chapters 1 to 7) presents a research on synthesis and recognition of facial expressions. Given that we not only use the face but also body movements to express ourselves, in the second section (Chapters 8 to 11) we present a research on perception and generation of emotional expressions by using full-body motions. The third section of the book (Chapters 12 to 16) presents computational models on emotion, as well as findings from neuroscience research. In the last section of the book (Chapters 17 to 22) we present applications related to affective computing

Facial motion cloning with radial basis functions in MPEG-4 FBA

Facial Motion Cloning (FMC) is the technique employed to transfer the motion of a virtual face (namely the source) to a mesh representing another face (the target), generally having a different geometry and connectivity. In this paper, we describe a novel method based on the combination of the Radial Basis Functions (RBF) volume morphing with the encoding capabilities of the widely used MPEG-4 Facial and Body Animation (FBA) international standard. First, we find the morphing function G(P) that precisely fits the shape of the source into the shape of the target face. Then, all the MPEG-4 encoded movements of the source face are transformed using the same function G(P) and mapped to the corresponding vertices of the target mesh. By doing this, we obtain, in a straightforward and simple way, the whole set of the MPEG-4 encoded facial movements for the target face in a short time. This animatable version of the target face is able to perform generic face animation stored in a MPEG-4 FBA data stream. (c) 2006 Elsevier Inc. All rights reserved

Facial motion cloning with radial basis functions in MPEG-4 FBA

Facial Motion Cloning (FMC) is the technique employed to transfer the motion of a virtual face (namely the source) to a mesh representing another face (the target), generally having a different geometry and connectivity. In this paper, we describe a novel method based on the combination of the Radial Basis Functions (RBF) volume morphing with the encoding capabilities of the widely used MPEG-4 Facial and Body Animation (FBA) international standard. First, we find the morphing function G(P) that precisely fits the shape of the source into the shape of the target face. Then, all the MPEG-4 encoded movements of the source face are transformed using the same function G(P) and mapped to the corresponding vertices of the target mesh. By doing this, we obtain, in a straightforward and simple way, the whole set of the MPEG-4 encoded facial movements for the target face in a short time. This animatable version of the target face is able to perform generic face animation stored in a MPEG-4 FBA data stream. \ua9 2006 Elsevier Inc. All rights reserved

Fast Facial Motion Cloning in MPEG-4

Facial Motion Cloning (FMC) is the technique employed to transfer the motion of a virtual face (namely, the source) to a mesh representing another face (the target), generally having a different geometry and topology. We present a novel FMC method relying on the combination of the Radial Basis Functions (RBF) based scattered data interpolation with the encoding capabilities of the MPEG-4 Facial and Body Animation (FBA) international standard. Starting from a manually picked set of correspondences, we find a scattered data interpolation function that precisely fit the source face mesh into the target one. Then, all the MPEG-4 FBA basic movements (encoded as morph targets) of the source face are deformed using the same function and the deformations are mapped to the corresponding vertices of the target mesh. By doing this, we obtain, in a straightforward and simple way, the animatable MPEG-4 compliant version of the target face in a short amount of time