Automated Deception Detection of Males and Females From Non-Verbal Facial Micro-Gestures

Gender bias within Artificial intelligence driven systems is currently a hot topic and is one of a number of areas where the data used to train, validate and test machine learning algorithms is under more scrutiny than ever before. In this paper we investigate if there is a difference between the nonverbal cues to deception generated by males and females through the use of an automated deception detection system. The system uses hierarchical neural networks to extract 36 channels of non-verbal head and facial behaviors whilst male and female participants are engaged in either a deceptive or truthful roleplaying task. An Image Vector dataset, comprising of 86584 vectors, is collated which uses a fixed sliding window slot of 1 second to record deceptive or truthful slots. Experiments were conducted on three variants of the dataset, all males, all females and mixed in order to examine if the differences in cues generated by males and females lead to differences in the accuracies of machine learning algorithms which classify their behavior. Results showed differences in nonverbal cues between males and females, with both genders at a disadvantage when treated by classifiers trained on both genders rather than classifiers specifically trained for each gender. However, there was no striking disadvantageous effect beyond the influence of their relative frequency of occurrence in the dataset.