Authors:
Safa Chebbi
and
Sofia Ben Jebara
Affiliation:
University of Carthage, SUP’COM, LR11TIC01 COSIM Research Lab, 2083, Ariana, Tunisia
Keyword(s):
Deception Behavior Detection, Feature Level Fusion, Feature Selection Techniques, Mutual Information.
Abstract:
Due to the increasing requirement of security and antiterrorism issues, research activities in the field of deception detection have been receiving a big attention. For this reason, many studies dealing with deception detection have been developed varying in terms of approaches, modalities, features and learning algorithms. Despite the wide range of proposed approaches in this task, there is no universal and effective system until today capable of identifying deception with a high recognition rate. In this paper, a feature level fusion approach, combining audio and video modalities, has been proposed to build an automated system that can help in decision making of honesty or lie. Thus a high feature vector size, combining verbal features (72 pitch-based ones) and nonverbal ones related to facial expressions and body gestures, is extracted. Then, a feature level fusion is applied in order to select the most relevant ones. A special interest is given to mutual information-based criteri
a that are well adapted to continuous and binary features combination. Simulation results on a realistic database of suspicious persons interrogation achieved 97% as deception/truth classification accuracy using 19 audio/video mixed features, which outperforms the state-of-the-art results.
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