2,398 research outputs found
Facial emotion recognition using min-max similarity classifier
Recognition of human emotions from the imaging templates is useful in a wide
variety of human-computer interaction and intelligent systems applications.
However, the automatic recognition of facial expressions using image template
matching techniques suffer from the natural variability with facial features
and recording conditions. In spite of the progress achieved in facial emotion
recognition in recent years, the effective and computationally simple feature
selection and classification technique for emotion recognition is still an open
problem. In this paper, we propose an efficient and straightforward facial
emotion recognition algorithm to reduce the problem of inter-class pixel
mismatch during classification. The proposed method includes the application of
pixel normalization to remove intensity offsets followed-up with a Min-Max
metric in a nearest neighbor classifier that is capable of suppressing feature
outliers. The results indicate an improvement of recognition performance from
92.85% to 98.57% for the proposed Min-Max classification method when tested on
JAFFE database. The proposed emotion recognition technique outperforms the
existing template matching methods
Deep fusion of multi-channel neurophysiological signal for emotion recognition and monitoring
How to fuse multi-channel neurophysiological signals for emotion recognition is emerging as a hot research topic in community of Computational Psychophysiology. Nevertheless, prior feature engineering based approaches require extracting various domain knowledge related features at a high time cost. Moreover, traditional fusion method cannot fully utilise correlation information between different channels and frequency components. In this paper, we design a hybrid deep learning model, in which the 'Convolutional Neural Network (CNN)' is utilised for extracting task-related features, as well as mining inter-channel and inter-frequency correlation, besides, the 'Recurrent Neural Network (RNN)' is concatenated for integrating contextual information from the frame cube sequence. Experiments are carried out in a trial-level emotion recognition task, on the DEAP benchmarking dataset. Experimental results demonstrate that the proposed framework outperforms the classical methods, with regard to both of the emotional dimensions of Valence and Arousal
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Automatic affective dimension recognition from naturalistic facial expressions based on wavelet filtering and PLS regression
Automatic affective dimension recognition from facial expression continuously in naturalistic contexts is a very challenging research topic but very important in human-computer interaction. In this paper, an automatic recognition system was proposed to predict the affective dimensions such as Arousal, Valence and Dominance continuously in naturalistic facial expression videos. Firstly, visual and vocal features are extracted from image frames and audio segments in facial expression videos. Secondly, a wavelet transform based digital filtering method is applied to remove the irrelevant noise information in the feature space. Thirdly, Partial Least Squares regression is used to predict the affective dimensions from both video and audio modalities. Finally, two modalities are combined to boost overall performance in the decision fusion process. The proposed method is tested in the fourth international Audio/Visual Emotion Recognition Challenge (AVEC2014) dataset and compared to other state-of-the-art methods in the affect recognition sub-challenge with a good performance
A Novel Apex-Time Network for Cross-Dataset Micro-Expression Recognition
The automatic recognition of micro-expression has been boosted ever since the
successful introduction of deep learning approaches. As researchers working on
such topics are moving to learn from the nature of micro-expression, the
practice of using deep learning techniques has evolved from processing the
entire video clip of micro-expression to the recognition on apex frame. Using
the apex frame is able to get rid of redundant video frames, but the relevant
temporal evidence of micro-expression would be thereby left out. This paper
proposes a novel Apex-Time Network (ATNet) to recognize micro-expression based
on spatial information from the apex frame as well as on temporal information
from the respective-adjacent frames. Through extensive experiments on three
benchmarks, we demonstrate the improvement achieved by learning such temporal
information. Specially, the model with such temporal information is more robust
in cross-dataset validations.Comment: 6 pages, 3 figures, 3 tables, code available, accepted in ACII 201
Recognising facial expressions in video sequences
We introduce a system that processes a sequence of images of a front-facing human face and recognises a set of facial expressions. We use an efficient appearance-based face tracker to locate the face in the image sequence and estimate the deformation of its non-rigid components. The tracker works in real-time. It is robust to strong illumination changes and factors out changes in appearance caused by illumination from changes due to face deformation. We adopt a model-based approach for facial expression recognition. In our model, an image of a face is represented by a point in a deformation space. The variability of the classes of images associated to facial expressions are represented by a set of samples which model a low-dimensional manifold in the space of deformations. We introduce a probabilistic procedure based on a nearest-neighbour approach to combine the information provided by the incoming image sequence with the prior information stored in the expression manifold in order to compute a posterior probability associated to a facial expression. In the experiments conducted we show that this system is able to work in an unconstrained environment with strong changes in illumination and face location. It achieves an 89\% recognition rate in a set of 333 sequences from the Cohn-Kanade data base
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