TY - GEN
T1 - 3D facial expression recognition based on properties of line segments connecting facial feature points
AU - Tang, Hao
AU - Huang, Thomas S.
PY - 2008
Y1 - 2008
N2 - The 3D facial geometry contains ample information about human facial expressions. Such information is invariant to pose and lighting conditions, which have imposed serious hurdles on many 2D facial analysis problems. In this paper, we perform person and gender independent facial expression recognition based on properties of the line segments connecting certain 3D facial feature points. The normalized distances and slopes of these line segments comprise a set of 96 distinguishing features for recognizing six universal facial expressions, namely anger, disgust, fear, happiness, sadness, and surprise. Using a multi-class support vector machine (SVM) classifier, an 87.1% average recognition rate is achieved on the publicly available 3D facial expression database BU-3DFE [1]. The highest average recognition rate obtained in our experiments is 99.2% for the recognition of surprise. Our result outperforms the result reported in the prior work [2], which uses elaborately extracted primitive facial surface features and an LDA classifier and which yields an average recognition rate of 83.6% on the same database.
AB - The 3D facial geometry contains ample information about human facial expressions. Such information is invariant to pose and lighting conditions, which have imposed serious hurdles on many 2D facial analysis problems. In this paper, we perform person and gender independent facial expression recognition based on properties of the line segments connecting certain 3D facial feature points. The normalized distances and slopes of these line segments comprise a set of 96 distinguishing features for recognizing six universal facial expressions, namely anger, disgust, fear, happiness, sadness, and surprise. Using a multi-class support vector machine (SVM) classifier, an 87.1% average recognition rate is achieved on the publicly available 3D facial expression database BU-3DFE [1]. The highest average recognition rate obtained in our experiments is 99.2% for the recognition of surprise. Our result outperforms the result reported in the prior work [2], which uses elaborately extracted primitive facial surface features and an LDA classifier and which yields an average recognition rate of 83.6% on the same database.
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U2 - 10.1109/AFGR.2008.4813304
DO - 10.1109/AFGR.2008.4813304
M3 - Conference contribution
AN - SCOPUS:67650697330
SN - 9781424421541
T3 - 2008 8th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2008
BT - 2008 8th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2008
T2 - 2008 8th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2008
Y2 - 17 September 2008 through 19 September 2008
ER -