Abstract
Deceit occurs in daily life and, even from an early age, children can successfully deceive their parents. Therefore, numerous book and psychological studies have been published to help people decipher the facial cues to deceit. In this study, we tackle the problem of deceit detection by analyzing eye movements: blinks, saccades and gaze direction. Recent psychological studies have shown that the non-visual saccadic eye movement rate is higher when people lie. We propose a fast and accurate framework for eye tracking and eye movement recognition and analysis. The proposed system tracks the position of the iris, as well as the eye corners (the outer shape of the eye). Next, in an offline analysis stage, the trajectory of these eye features is analyzed in order to recognize and measure various cues which can be used as an indicator of deception: the blink rate, the gaze direction and the saccadic eye movement rate. On the task of iris center localization, the method achieves within pupil localization in 91.47% of the cases. For blink localization, we obtained an accuracy of 99.3% on the difficult EyeBlink8 dataset. In addition, we proposed a novel metric, the normalized blink rate deviation to stop deceitful behavior based on blink rate. Using this metric and a simple decision stump, the deceitful answers from the Silesian Face database were recognized with an accuracy of 96.15%.
Highlights
Deceit, the distortion or omission of the truth, is a frequent and important aspect of human communication
We propose a simple yet robust, appearance based algorithm for blink detection, which combines the response of two eye state classifiers: the first classifier uses the detected fiducial points in order to estimate the eye state, while the latter is a convolutional neural network (CNN) which operates on periocular image regions to detect blinks
We noticed that our method detects false positives in image sequences in which the subjects perform other facial expressions in which the eyes are almost closed, such as smiling or laughing. We argue that this problem could be addressed by increasing the training set of the CNN with periocular images in which the subjects laugh or smile, as the training set contains only images from the Closed Eyes In The Wild (CEW) dataset in which the participants have an almost neutral facial expression
Summary
The distortion or omission of the (complete) truth, is a frequent and important aspect of human communication. The traditional tools for deceit detection (i.e., the polygraph tests) are based on several responses of the autonomic nervous system (ANS)— blood pressure, breathing pattern, skin resistance, etc.—correlated with the interrogation of the suspect. Technique(CQT)—largely used in the United States, which aims at detecting psychological responses to the questions, and the Concealed Information Test [3] (CIT)—used in Japan, which is designed to detect concealed crime-related knowledge. Besides these classical methods, other cues of deceit detection have been considered [4]: emblems, illustrators, micro-expressions and eye movements. The eyes are perhaps the most expressive and salient features of the human face, as they convey tremendous amounts of information: cognitive workload, (visual) attention, neurological processes, just to name a few
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