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%.

Human Attribute Recognition (HAR) is a highly active research field in computer vision and pattern recognition domains with various applications such as surveillance or fashion. Several approaches have been proposed to tackle the particular challenges in HAR. However, these approaches have dramatically changed over the last decade, mainly due to the improvements brought by deep learning solutions. To provide insights for future algorithm design and dataset collections, in this survey, (1) we provide an in-depth analysis of existing HAR techniques, concerning the advances proposed to address the HAR’s main challenges; (2) we provide a comprehensive discussion over the publicly available datasets for the development and evaluation of novel HAR approaches; (3) we outline the applications and typical evaluation metrics used in the HAR context.

Micro-expressions (ME) are brief, fast facial movements that occur in high-stake situations when people try to conceal their feelings, as a form of either suppression or repression. They are reliable sources of deceit detection and human behavior understanding. Automatic analysis of micro-expression is challenging because of their short duration (they occur as fast as 1/15–1/25 of a second) and their low movement amplitude. In this study, we report a fast and robust micro-expression detection framework, which analyzes the subtle movement variations that occur around the most prominent facial regions using two absolute frame differences and simple classifier to predict the micro-expression frames. The robustness of the system is increased by further processing the preliminary predictions of the classifier: the appropriate predicted micro-expression intervals are merged together and the intervals that are too short are filtered out.

Over the last decade, the field of Human Attribute Recognition (HAR) has dramatically changed, mainly due to the improvements brought by deep learning solutions. This survey reviews the progress obtained in HAR, considering the transition from the traditional hand-crafted to deep-learning approaches. The most relevant works on the field are analyzed concerning the advances proposed to address the HAR's typical challenges. Furthermore, we outline the applications and typical evaluation metrics used in the HAR context. Finally, we provide a comprehensive review of the publicly available datasets for the development and evaluation of novel HAR approaches.