Automatic Personality Assessment through Movement Analysis

Obtaining accurate and objective assessments of an individual’s personality is vital in many areas including education, medicine, sports and management. Currently, most personality assessments are conducted using scales and questionnaires. Unfortunately, it has been observed that both scales and questionnaires present various drawbacks. Their limitations include the lack of veracity in the answers, limitations in the number of times they can be administered, or cultural biases. To solve these problems, several articles have been published in recent years proposing the use of movements that participants make during their evaluation as personality predictors. In this work, a multiple linear regression model was developed to assess the examinee’s personality based on their movements. Movements were captured with the low-cost Microsoft Kinect camera, which facilitates its acceptance and implementation. To evaluate the performance of the proposed system, a pilot study was conducted aimed at assessing the personality traits defined by the Big-Five Personality Model. It was observed that the traits that best fit the model are Extroversion and Conscientiousness. In addition, several patterns that characterize the five personality traits were identified. These results show that it is feasible to assess an individual’s personality through his or her movements and open up pathways for several research.This research was partially funded by the Spanish National Project, grant number RTI2018- 101857-B-I00. Additionally, by Instituto Salud Carlos III, grant number DTS21/00091. It has been also partially supported by Ministerio de Ciencia e Innovación PID2020-114911GB-I00

Recognising Complex Mental States from Naturalistic Human-Computer Interactions

New advances in computer vision techniques will revolutionize the way we interact with computers, as they, together with other improvements, will help us build machines that understand us better. The face is the main non-verbal channel for human-human communication and contains valuable information about emotion, mood, and mental state. Affective computing researchers have investigated widely how facial expressions can be used for automatically recognizing affect and mental states. Nowadays, physiological signals can be measured by video-based techniques, which can also be utilised for emotion detection. Physiological signals, are an important indicator of internal feelings, and are more robust against social masking. This thesis focuses on computer vision techniques to detect facial expression and physiological changes for recognizing non-basic and natural emotions during human-computer interaction. It covers all stages of the research process from data acquisition, integration and application. Most previous studies focused on acquiring data from prototypic basic emotions acted out under laboratory conditions. To evaluate the proposed method under more practical conditions, two different scenarios were used for data collection. In the first scenario, a set of controlled stimulus was used to trigger the user’s emotion. The second scenario aimed at capturing more naturalistic emotions that might occur during a writing activity. In the second scenario, the engagement level of the participants with other affective states was the target of the system. For the first time this thesis explores how video-based physiological measures can be used in affect detection. Video-based measuring of physiological signals is a new technique that needs more improvement to be used in practical applications. A machine learning approach is proposed and evaluated to improve the accuracy of heart rate (HR) measurement using an ordinary camera during a naturalistic interaction with computer