10,722 research outputs found
Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data
Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives
Over the past few years, adversarial training has become an extremely active
research topic and has been successfully applied to various Artificial
Intelligence (AI) domains. As a potentially crucial technique for the
development of the next generation of emotional AI systems, we herein provide a
comprehensive overview of the application of adversarial training to affective
computing and sentiment analysis. Various representative adversarial training
algorithms are explained and discussed accordingly, aimed at tackling diverse
challenges associated with emotional AI systems. Further, we highlight a range
of potential future research directions. We expect that this overview will help
facilitate the development of adversarial training for affective computing and
sentiment analysis in both the academic and industrial communities
Machine Analysis of Facial Expressions
No abstract
Cultural-based visual expression: Emotional analysis of human face via Peking Opera Painted Faces (POPF)
© 2015 The Author(s) Peking Opera as a branch of Chinese traditional cultures and arts has a very distinct colourful facial make-up for all actors in the stage performance. Such make-up is stylised in nonverbal symbolic semantics which all combined together to form the painted faces to describe and symbolise the background, the characteristic and the emotional status of specific roles. A study of Peking Opera Painted Faces (POPF) was taken as an example to see how information and meanings can be effectively expressed through the change of facial expressions based on the facial motion within natural and emotional aspects. The study found that POPF provides exaggerated features of facial motion through images, and the symbolic semantics of POPF provides a high-level expression of human facial information. The study has presented and proved a creative structure of information analysis and expression based on POPF to improve the understanding of human facial motion and emotion
Exploring the Affective Loop
Research in psychology and neurology shows that both body and mind are
involved when experiencing emotions (Damasio 1994, Davidson et al.
2003). People are also very physical when they try to communicate their
emotions. Somewhere in between beings consciously and unconsciously
aware of it ourselves, we produce both verbal and physical signs to make
other people understand how we feel. Simultaneously, this production of
signs involves us in a stronger personal experience of the emotions we
express.
Emotions are also communicated in the digital world, but there is little
focus on users' personal as well as physical experience of emotions in
the available digital media. In order to explore whether and how we can
expand existing media, we have designed, implemented and evaluated
/eMoto/, a mobile service for sending affective messages to others. With
eMoto, we explicitly aim to address both cognitive and physical
experiences of human emotions. Through combining affective gestures for
input with affective expressions that make use of colors, shapes and
animations for the background of messages, the interaction "pulls" the
user into an /affective loop/. In this thesis we define what we mean by
affective loop and present a user-centered design approach expressed
through four design principles inspired by previous work within Human
Computer Interaction (HCI) but adjusted to our purposes; /embodiment/
(Dourish 2001) as a means to address how people communicate emotions in
real life, /flow/ (Csikszentmihalyi 1990) to reach a state of
involvement that goes further than the current context, /ambiguity/ of
the designed expressions (Gaver et al. 2003) to allow for open-ended
interpretation by the end-users instead of simplistic, one-emotion
one-expression pairs and /natural but designed expressions/ to address
people's natural couplings between cognitively and physically
experienced emotions. We also present results from an end-user study of
eMoto that indicates that subjects got both physically and emotionally
involved in the interaction and that the designed "openness" and
ambiguity of the expressions, was appreciated and understood by our
subjects. Through the user study, we identified four potential design
problems that have to be tackled in order to achieve an affective loop
effect; the extent to which users' /feel in control/ of the interaction,
/harmony and coherence/ between cognitive and physical expressions/,/
/timing/ of expressions and feedback in a communicational setting, and
effects of users' /personality/ on their emotional expressions and
experiences of the interaction
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