A Comprehensive Review on Features Extraction and Features Matching Techniques for Deception Detection

Abstract

Over a few decades, a remarkable amount of research has been conducted in the field of speech signal processing particularly on deception detection for security applications. In this study, a comprehensive review on recent machine learning approaches using verbal and non-verbal features is presented for deception detection. A brief overview on different feature extraction techniques, the results of recognition rate, and computational time based on machine learning methods are summarized in a tabular format. In addition, numerous datasets used as primary sources of deception detection in the review articles are also presented in this work. Key findings from the reviewed articles are summarized and a few major issues related to deception detection approaches are examined. A statistical analysis which conducted by extracting the significant information from the eighty-eight (88) scientific papers over the last thirty (30) years are provided in this review paper. The results emphasize on the trends of research in deception detection as well as further research opportunities for researchers as a part of continuous progress.

Keywords

• Deception detection
• machine learning
• non-verbal features
• principal component analysis
• verbal features