Personality analysis is an important area of research in several fields, including psychology, psychiatry, and neuroscience. With the recent dramatic improvements in machine learning, it has also become a popular research area in computer science. While the current computational methods are able to interpret behavioral cues (e.g., facial expressions, gesture, and voice) to estimate the level of (apparent) personality traits, accessible assessment tools are still substandard for practical use, not to mention the need for fast and accurate methods for such analyses. In this study, we present multimodal deep architectures to estimate the Big Five personality traits from (temporal) audio-visual cues and transcribed speech. Furthermore, for a detailed analysis of personality traits, we have collected a new audio-visual dataset, namely: Self-presentation and Induced Behavior Archive for Personality Analysis (SIAP). In contrast to the available datasets, SIAP introduces recordings of induced behavior in addition to self-presentation (speech) videos. With thorough experiments on SIAP and ChaLearn LAP First Impressions datasets, we systematically assess the reliability of different behavioral modalities and their combined use. Furthermore, we investigate the characteristics and discriminative power of induced behavior for personality analysis, showing that the induced behavior indeed includes signs of personality traits.

Deception is a common phenomenon in society, both in our private and professional lives. However, humans are notoriously bad at accurate deception detection. Based on the literature, human accuracy of distinguishing between lies and truthful statements is 54% on average, in other words, it is slightly better than a random guess. While people do not much care about this issue, in high-stakes situations such as interrogations for series crimes and for evaluating the testimonies in court cases, accurate deception detection methods are highly desirable. To achieve a reliable, covert, and non-invasive deception detection, we propose a novel method that disentangles facial expression and head pose related features using 2D-to-3D face reconstruction technique from a video sequence and uses them to learn characteristics of deceptive behavior. We evaluate the proposed method on the Real-Life Trial (RLT) dataset that contains high-stakes deceits recorded in courtrooms. Our results show that the proposed method (with an accuracy of 68%) improves the state of the art. Besides, a new dataset has been collected, for the first time, for low-stake deceit detection. In addition, we compare high-stake deceit detection methods on the newly collected low-stake deceits.