Abstract

Automatic recognition of Pain expression has potential medical significance. In this paper we present results of the application of an automatic facial expression recognition system on sequences of spontaneous Pain expression. Twenty participants were videotaped while undergoing thermal heat stimulation at nonpainful and painful intensities. Pain was induced experimentally by use of a Peltierbased, computerized thermal stimulator with a 3 × 3 cm2 contact probe. Our aim is to automatically recognize the videos where Pain was induced. We chose a machine learning approach, previously used successfully to categorize the six basic facial expressions in posed datasets [1, 2] based on the Transferable Belief Model. For this paper, we extended this model to the recognition of sequences of spontaneous Pain expression. The originality of the proposed method is the use of the dynamic information for the recognition of spontaneous Pain expression and the combination of different sensors: facial features behavior, transient features and the context of the expression study. Experimental results show good classification rates for spontaneous Pain sequences especially when we use the contextual information. Moreover the system behaviour compares favourably to the human observer in the other case, which opens promising perspectives for the future development of the proposed system.

Highlights

  • The interpretation of facial expressions, and expressions of emotion, is critical to everyday social interactions [3]

  • Spontaneous facial expressions are often characterized by subtle changes of facial features while the acted facial expressions are characterized by exaggerated changes of facial features [7]

  • The remainder of the paper is organized as follows: first, we describe the facial expression databases we used to train and evaluate the system; second, we describe briefly the main features of our automatic facial expression system and describe the fusion process using the Transferable Belief Model (TBM); third, we present the model of temporal classification and the final fusion and decision process of Pain expressions sequences, introducing a context variable; we present the classification results both on spontaneous and acted Pain expressions, emphasizing on the good performances and on the quality of the information extracted from the video sequence

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Summary

INTRODUCTION

The interpretation of facial expressions, and expressions of emotion, is critical to everyday social interactions [3]. The remainder of the paper is organized as follows: first, we describe the facial expression databases we used to train and evaluate the system; second, we describe briefly the main features of our automatic facial expression system and describe the fusion process using the TBM; third, we present the model of temporal classification and the final fusion and decision process of Pain expressions sequences, introducing a context variable; we present the classification results both on spontaneous and acted Pain expressions, emphasizing on the good performances and on the quality of the information extracted from the video sequence

FACIAL EXPRESSION DATA
AUTOMATIC SYSTEM
FUSION PROCESS BY THE TRANSFERABLE BELIEF MODEL
The basic belief assignment of the characteristic distances
The basic belief assignment of the transient features
TEMPORAL INFORMATION FOR FACIAL EXPRESSION CLASSIFICATION
Basic belief assignment prediction of the characteristic distance states
FUSION PROCESS
Fusion of the characteristic distances information
Findings
CONCLUSION
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