Machine Understanding of Human Behavior

A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, human-like interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses a number of components of human behavior, how they might be integrated into computers, and how far we are from realizing the front end of human computing, that is, how far are we from enabling computers to understand human behavior

Affective games:a multimodal classification system

Affective gaming is a relatively new field of research that exploits human emotions to influence gameplay for an enhanced player experience. Changes in player’s psychology reflect on their behaviour and physiology, hence recognition of such variation is a core element in affective games. Complementary sources of affect offer more reliable recognition, especially in contexts where one modality is partial or unavailable. As a multimodal recognition system, affect-aware games are subject to the practical difficulties met by traditional trained classifiers. In addition, inherited game-related challenges in terms of data collection and performance arise while attempting to sustain an acceptable level of immersion. Most existing scenarios employ sensors that offer limited freedom of movement resulting in less realistic experiences. Recent advances now offer technology that allows players to communicate more freely and naturally with the game, and furthermore, control it without the use of input devices. However, the affective game industry is still in its infancy and definitely needs to catch up with the current life-like level of adaptation provided by graphics and animation

MobiBits: Multimodal Mobile Biometric Database

This paper presents a novel database comprising representations of five different biometric characteristics, collected in a mobile, unconstrained or semi-constrained setting with three different mobile devices, including characteristics previously unavailable in existing datasets, namely hand images, thermal hand images, and thermal face images, all acquired with a mobile, off-the-shelf device. In addition to this collection of data we perform an extensive set of experiments providing insight on benchmark recognition performance that can be achieved with these data, carried out with existing commercial and academic biometric solutions. This is the first known to us mobile biometric database introducing samples of biometric traits such as thermal hand images and thermal face images. We hope that this contribution will make a valuable addition to the already existing databases and enable new experiments and studies in the field of mobile authentication. The MobiBits database is made publicly available to the research community at no cost for non-commercial purposes.Comment: Submitted for the BIOSIG2018 conference on June 18, 2018. Accepted for publication on July 20, 201

Person Identification Using Multimodal Biometrics under Different Challenges

The main aims of this chapter are to show the importance and role of human identification and recognition in the field of human-robot interaction, discuss the methods of person identification systems, namely traditional and biometrics systems, and compare the most commonly used biometric traits that are used in recognition systems such as face, ear, palmprint, iris, and speech. Then, by showing and comparing the requirements, advantages, disadvantages, recognition algorithms, challenges, and experimental results for each trait, the most suitable and efficient biometric trait for human-robot interaction will be discussed. The cases of human-robot interaction that require to use the unimodal biometric system and why the multimodal biometric system is also required will be discussed. Finally, two fusion methods for the multimodal biometric system will be presented and compared