Eye spy a liar: assessing the utility of eye fixations and confidence judgments for detecting concealed recognition of faces, scenes and objects

The promise of eye-tracking in the detection of concealed memories

There is a need for validated, theory-driven methods for memory detection. We review how physiological, neurophysiological, and oculomotor measures can be utilized to reveal concealed memories. Recent advances and future directions are discussed in light of the potential of eye-tracking to improve detection efficiency and resolve problems in real-world settings.

A Deep Audiovisual Approach for Human Confidence Classification

Research on self-efficacy and confidence has spread across several subfields of psychology and neuroscience. The role of one’s confidence is very crucial in the formation of attitude and communication skills. The importance of differentiating the levels of confidence is quite visible in this domain. With the recent advances in extracting behavioral insight from a signal in multiple applications, detecting confidence is found to have great importance. One such prominent application is detecting confidence in interview conversations. We have collected an audiovisual data set of interview conversations with 34 candidates. Every response (from each of the candidate) of this data set is labeled with three levels of confidence: high, medium, and low. Furthermore, we have also developed algorithms to efficiently compute such behavioral confidence from speech and video. A deep learning architecture is proposed for detecting confidence levels (high, medium, and low) from an audiovisual clip recorded during an interview. The achieved unweighted average recall (UAR) reaches 85.9% on audio data and 73.6% on video data captured from an interview session.