11,960 research outputs found
Micro-expression Recognition using Spatiotemporal Texture Map and Motion Magnification
Micro-expressions are short-lived, rapid facial expressions that are exhibited by individuals when they are in high stakes situations. Studying these micro-expressions is important as these cannot be modified by an individual and hence offer us a peek into what the individual is actually feeling and thinking as opposed to what he/she is trying to portray. The spotting and recognition of micro-expressions has applications in the fields of criminal investigation, psychotherapy, education etc. However due to micro-expressions’ short-lived and rapid nature; spotting, recognizing and classifying them is a major challenge. In this paper, we design a hybrid approach for spotting and recognizing micro-expressions by utilizing motion magnification using Eulerian Video Magnification and Spatiotemporal Texture Map (STTM). The validation of this approach was done on the spontaneous micro-expression dataset, CASMEII in comparison with the baseline. This approach achieved an accuracy of 80% viz. an increase by 5% as compared to the existing baseline by utilizing 10-fold cross validation using Support Vector Machines (SVM) with a linear kernel
Binary Pattern Analysis for 3D Facial Action Unit Detection
In this paper we propose new binary pattern features for use in the problem of 3D facial action unit (AU) detection. Two representations of 3D facial geometries are employed, the depth map and the Azimuthal Projection Distance Image (APDI). To these the traditional Local Binary Pattern is applied, along with Local Phase Quantisation, Gabor filters and Monogenic filters, followed by the binary pattern feature extraction method. Feature vectors are formed for each feature type through concatenation of histograms formed from the resulting binary numbers. Feature selection is then performed using a two-stage GentleBoost approach. Finally, we apply Support Vector Machines as classifiers for detection of each AU. This system is tested in two ways. First we perform 10-fold cross-validation on the Bosphorus database, and then we perform cross-database testing by training on this database and then testing on apex frames from the D3DFACS database, achieving promising results in both
Distinguishing Posed and Spontaneous Smiles by Facial Dynamics
Smile is one of the key elements in identifying emotions and present state of
mind of an individual. In this work, we propose a cluster of approaches to
classify posed and spontaneous smiles using deep convolutional neural network
(CNN) face features, local phase quantization (LPQ), dense optical flow and
histogram of gradient (HOG). Eulerian Video Magnification (EVM) is used for
micro-expression smile amplification along with three normalization procedures
for distinguishing posed and spontaneous smiles. Although the deep CNN face
model is trained with large number of face images, HOG features outperforms
this model for overall face smile classification task. Using EVM to amplify
micro-expressions did not have a significant impact on classification accuracy,
while the normalizing facial features improved classification accuracy. Unlike
many manual or semi-automatic methodologies, our approach aims to automatically
classify all smiles into either `spontaneous' or `posed' categories, by using
support vector machines (SVM). Experimental results on large UvA-NEMO smile
database show promising results as compared to other relevant methods.Comment: 16 pages, 8 figures, ACCV 2016, Second Workshop on Spontaneous Facial
Behavior Analysi
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