Automated analysis of human affective behavior has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and related disciplines. However, the existing methods typically handle only deliberately displayed and exaggerated expressions of prototypical emotions despite the fact that deliberate behaviour differs in visual appearance, audio profile, and timing from spontaneously occurring behaviour. To address this problem, efforts to develop algorithms that can process naturally occurring human affective behaviour have recently emerged. Moreover, an increasing number of efforts are reported toward multimodal fusion for human affect analysis including audiovisual fusion, linguistic and paralinguistic fusion, and multi-cue visual fusion based on facial expressions, head movements, and body gestures. This paper introduces and surveys these recent advances. We first discuss human emotion perception from a psychological perspective. Next we examine available approaches to solving the problem of machine understanding of human affective behavior, and discuss important issues like the collection and availability of training and test data. We finally outline some of the scientific and engineering challenges to advancing human affect sensing technology.

A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions

Automated analysis of human affective behavior has attracted increasing attention from researchers in psychology, computer science, linguistics, neuroscience, and related disciplines. Promising approaches have been reported, including automatic methods for facial and vocal affect recognition. However, the existing methods typically handle only deliberately displayed and exaggerated expressions of prototypical emotions-despite the fact that deliberate behavior differs in visual and audio expressions from spontaneously occurring behavior. Recently efforts to develop algorithms that can process naturally occurring human affective behavior have emerged. This paper surveys these efforts. We first discuss human emotion perception from a psychological perspective. Next, we examine the available approaches to solving the problem of machine understanding of human affective behavior occurring in real-world settings. We finally outline some scientific and engineering challenges for advancing human affect sensing technology.

A survey of affect recognition methods: audio, visual and spontaneous expressions

Face information processing relies on the quality of data resource From the data modality point of view, a face database can be 2D or 3D, and static or dynamic From the task point of view, the data can be used for research of computer based automatic face recognition, face expression recognition, face detection, or cognitive and psychological investigation With the advancement of 3D imaging technologies, 3D dynamic facial sequences (called 4D data) have been used for face information analysis In this paper, we focus on the modality of 3D dynamic data for the task of facial expression recognition We present a newly created high-resolution 3D dynamic facial expression database, which is made available to the scientific research community The database contains 606 3D facial expression sequences captured from 101 subjects of various ethnic backgrounds The database has been validated through our facial expression recognition experiment using an HMM based 3D spatio-temporal facial descriptor It is expected that such a database shall be used to facilitate the facial expression analysis from a static 3D space to a dynamic 3D space, with a goal of scrutinizing facial behavior at a higher level of detail in a real 3D spatio-temporal domain

A high-resolution 3D dynamic facial expression database

We report a first-of-its-kind smartphone software system, MoodScope, which infers the mood of its user based on how the smartphone is used. Compared to smartphone sensors that measure acceleration, light, and other physical properties, MoodScope is a "sensor" that measures the mental state of the user and provides mood as an important input to context-aware computing. We run a formative statistical mood study with smartphone-logged data collected from 32 participants over two months. Through the study, we find that by analyzing communication history and application usage patterns, we can statistically infer a user's daily mood average with an initial accuracy of 66%, which gradu-ally improves to an accuracy of 93% after a two-month personal-ized training period. Motivated by these results, we build a service, MoodScope, which analyzes usage history to act as a sensor of the user's mood. We provide a MoodScope API for developers to use our system to create mood-enabled applications. We further create and deploy a mood-sharing social application.

/pdf/moodscope-building-a-mood-sensor-from-smartphone-usage-4r5u4gshj4.pdf

MoodScope: building a mood sensor from smartphone usage patterns

Current methods of assessing psychopathology depend almost entirely on verbal report (clinical interview or questionnaire) of patients, their family, or caregivers. They lack systematic and efficient ways of incorporating behavioral observations that are strong indicators of psychological disorder, much of which may occur outside the awareness of either individual. We compared clinical diagnosis of major depression with automatically measured facial actions and vocal prosody in patients undergoing treatment for depression. Manual FACS coding, active appearance modeling (AAM) and pitch extraction were used to measure facial and vocal expression. Classifiers using leave-one-out validation were SVM for FACS and for AAM and logistic regression for voice. Both face and voice demonstrated moderate concurrent validity with depression. Accuracy in detecting depression was 88% for manual FACS and 79% for AAM. Accuracy for vocal prosody was 79%. These findings suggest the feasibility of automatic detection of depression, raise new issues in automated facial image analysis and machine learning, and have exciting implications for clinical theory and practice.

/pdf/detecting-depression-from-facial-actions-and-vocal-prosody-49jbvgsxov.pdf

Detecting depression from facial actions and vocal prosody

1. Human Face and Its Expression The human face is the site for major sensory inputs and major communicative outputs. It houses the majority of our sensory apparatus as well as our speech production apparatus. It is used to identify other members of our species, to gather information about age, gender, attractiveness, and personality, and to regulate conversation by gazing or nodding. Moreover, the human face is our preeminent means of communicating and understanding somebody’s affective state and intentions on the basis of the shown facial expression (Keltner & Ekman, 2000). Thus, the human face is a multi-signal input-output communicative system capable of tremendous flexibility and specificity (Ekman & Friesen, 1975). In general, the human face conveys information via four kinds of signals. (a) Static facial signals represent relatively permanent features of the face, such as the bony structure, the soft tissue, and the overall proportions of the face. These signals contribute to an individual’s appearance and are usually exploited for person identification.

/pdf/machine-analysis-of-facial-expressions-2bjqdmcvt1.pdf

Machine Analysis of Facial Expressions

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Machine Analysis of Facial Expressions