42,573 research outputs found
Robust Modeling of Epistemic Mental States
This work identifies and advances some research challenges in the analysis of
facial features and their temporal dynamics with epistemic mental states in
dyadic conversations. Epistemic states are: Agreement, Concentration,
Thoughtful, Certain, and Interest. In this paper, we perform a number of
statistical analyses and simulations to identify the relationship between
facial features and epistemic states. Non-linear relations are found to be more
prevalent, while temporal features derived from original facial features have
demonstrated a strong correlation with intensity changes. Then, we propose a
novel prediction framework that takes facial features and their nonlinear
relation scores as input and predict different epistemic states in videos. The
prediction of epistemic states is boosted when the classification of emotion
changing regions such as rising, falling, or steady-state are incorporated with
the temporal features. The proposed predictive models can predict the epistemic
states with significantly improved accuracy: correlation coefficient (CoERR)
for Agreement is 0.827, for Concentration 0.901, for Thoughtful 0.794, for
Certain 0.854, and for Interest 0.913.Comment: Accepted for Publication in Multimedia Tools and Application, Special
Issue: Socio-Affective Technologie
Holistic gaze strategy to categorize facial expression of varying intensities
Using faces representing exaggerated emotional expressions, recent behaviour and eye-tracking studies have suggested a
dominant role of individual facial features in transmitting diagnostic cues for decoding facial expressions. Considering that in everyday life we frequently view low-intensity expressive faces in which local facial cues are more ambiguous, we probably need to combine expressive cues from more than one facial feature to reliably decode naturalistic facial affects. In this study we applied a morphing technique to systematically vary intensities of six basic facial expressions of emotion, and employed a self-paced expression categorization task to measure participants’ categorization performance and associated
gaze patterns. The analysis of pooled data from all expressions showed that increasing expression intensity would improve categorization accuracy, shorten reaction time and reduce number of fixations directed at faces. The proportion of fixations and viewing time directed at internal facial features (eyes, nose and mouth region), however, was not affected by varying levels of intensity. Further comparison between individual facial expressions revealed that although proportional gaze allocation at individual facial features was quantitatively modulated by the viewed expressions, the overall gaze distribution
in face viewing was qualitatively similar across different facial expressions and different intensities. It seems that we adopt a holistic viewing strategy to extract expressive cues from all internal facial features in processing of naturalistic facial expressions
Machine Analysis of Facial Expressions
No abstract
Change blindness: eradication of gestalt strategies
Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task
NEW shared & interconnected ASL resources: SignStream® 3 Software; DAI 2 for web access to linguistically annotated video corpora; and a sign bank
2017 marked the release of a new version of SignStream® software, designed to facilitate linguistic analysis of ASL video. SignStream® provides an intuitive interface for labeling and time-aligning manual and non-manual components of the signing. Version 3 has many new features. For example, it enables representation of morpho-phonological information, including display of handshapes. An expanding ASL video corpus, annotated through use of SignStream®, is shared publicly on the Web. This corpus (video plus annotations) is Web-accessible—browsable, searchable, and downloadable—thanks to a new, improved version of our Data Access Interface: DAI 2. DAI 2 also offers Web access to a brand new Sign Bank, containing about 10,000 examples of about 3,000 distinct signs, as produced by up to 9 different ASL signers. This Sign Bank is also directly accessible from within SignStream®, thereby boosting the efficiency and consistency of annotation; new items can also be added to the Sign Bank. Soon to be integrated into SignStream® 3 and DAI 2 are visualizations of computer-generated analyses of the video: graphical display of eyebrow height, eye aperture, an
Machine Learning
Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience
Face Detection and Recognition Based E-Learning for Students Authentication: Study Literature Review
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