14,354 research outputs found
Discovering Gender Differences in Facial Emotion Recognition via Implicit Behavioral Cues
We examine the utility of implicit behavioral cues in the form of EEG brain
signals and eye movements for gender recognition (GR) and emotion recognition
(ER). Specifically, the examined cues are acquired via low-cost, off-the-shelf
sensors. We asked 28 viewers (14 female) to recognize emotions from unoccluded
(no mask) as well as partially occluded (eye and mouth masked) emotive faces.
Obtained experimental results reveal that (a) reliable GR and ER is achievable
with EEG and eye features, (b) differential cognitive processing especially for
negative emotions is observed for males and females and (c) some of these
cognitive differences manifest under partial face occlusion, as typified by the
eye and mouth mask conditions.Comment: To be published in the Proceedings of Seventh International
Conference on Affective Computing and Intelligent Interaction.201
Multimodal Polynomial Fusion for Detecting Driver Distraction
Distracted driving is deadly, claiming 3,477 lives in the U.S. in 2015 alone.
Although there has been a considerable amount of research on modeling the
distracted behavior of drivers under various conditions, accurate automatic
detection using multiple modalities and especially the contribution of using
the speech modality to improve accuracy has received little attention. This
paper introduces a new multimodal dataset for distracted driving behavior and
discusses automatic distraction detection using features from three modalities:
facial expression, speech and car signals. Detailed multimodal feature analysis
shows that adding more modalities monotonically increases the predictive
accuracy of the model. Finally, a simple and effective multimodal fusion
technique using a polynomial fusion layer shows superior distraction detection
results compared to the baseline SVM and neural network models.Comment: INTERSPEECH 201
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
Evaluating Content-centric vs User-centric Ad Affect Recognition
Despite the fact that advertisements (ads) often include strongly emotional
content, very little work has been devoted to affect recognition (AR) from ads.
This work explicitly compares content-centric and user-centric ad AR
methodologies, and evaluates the impact of enhanced AR on computational
advertising via a user study. Specifically, we (1) compile an affective ad
dataset capable of evoking coherent emotions across users; (2) explore the
efficacy of content-centric convolutional neural network (CNN) features for
encoding emotions, and show that CNN features outperform low-level emotion
descriptors; (3) examine user-centered ad AR by analyzing Electroencephalogram
(EEG) responses acquired from eleven viewers, and find that EEG signals encode
emotional information better than content descriptors; (4) investigate the
relationship between objective AR and subjective viewer experience while
watching an ad-embedded online video stream based on a study involving 12
users. To our knowledge, this is the first work to (a) expressly compare user
vs content-centered AR for ads, and (b) study the relationship between modeling
of ad emotions and its impact on a real-life advertising application.Comment: Accepted at the ACM International Conference on Multimodal Interation
(ICMI) 201
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
Eye-CU: Sleep Pose Classification for Healthcare using Multimodal Multiview Data
Manual analysis of body poses of bed-ridden patients requires staff to
continuously track and record patient poses. Two limitations in the
dissemination of pose-related therapies are scarce human resources and
unreliable automated systems. This work addresses these issues by introducing a
new method and a new system for robust automated classification of sleep poses
in an Intensive Care Unit (ICU) environment. The new method,
coupled-constrained Least-Squares (cc-LS), uses multimodal and multiview (MM)
data and finds the set of modality trust values that minimizes the difference
between expected and estimated labels. The new system, Eye-CU, is an affordable
multi-sensor modular system for unobtrusive data collection and analysis in
healthcare. Experimental results indicate that the performance of cc-LS matches
the performance of existing methods in ideal scenarios. This method outperforms
the latest techniques in challenging scenarios by 13% for those with poor
illumination and by 70% for those with both poor illumination and occlusions.
Results also show that a reduced Eye-CU configuration can classify poses
without pressure information with only a slight drop in its performance.Comment: Ten-page manuscript including references and ten figure
Fusion of facial regions using color information in a forensic scenario
Comunicación presentada en: 18th Iberoamerican Congress on Pattern Recognition, CIARP 2013; Havana; Cuba; 20-23 November 2013The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-41827-3_50This paper reports an analysis of the benefits of using color information on a region-based face recognition system. Three different color spaces are analysed (RGB, YCbCr, lαβ) in a very challenging scenario matching good quality mugshot images against video surveillance images. This scenario is of special interest for forensics, where examiners carry out a comparison of two face images using the global information of the faces, but paying special attention to each individual facial region (eyes, nose, mouth, etc.). This work analyses the discriminative power of 15 facial regions comparing both the grayscale and color information. Results show a significant improvement of performance when fusing several regions of the face compared to just using the whole face image. A further improvement of performance is achieved when color information is consideredThis work has been partially supported by contract with Spanish Guardia Civil and projects BBfor2 (FP7-ITN-238803), bio-Challenge (TEC2009-11186), Bio Shield (TEC2012-34881), Contexts (S2009/TIC-1485), TeraSense (CSD2008-00068) and "Cátedra UAM-Telefónica
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