31 research outputs found
Emotion Recognition using Fisher Face-based Viola-Jones Algorithm
In the form of the image integral, this primitive feature accelerates the performance of the Viola-Jones algorithm. However, the robust feature is necessary to optimize the results of emotion recognition. Previous research [11] has shown that fisher face optimized projection matrix in the low dimensional features. This feature reduction approach is expected to balance time-consuming and accuracy. Thus we proposed emotion recognition using fisher face-based Viola-Jones Algorithm. In this study, PCA and LDA are extracted to get the fisher face value. Then fisher face is filtered using Cascading AdaBoost algorithm to obtain face area. In the facial area, the Cascading AdaBoost algorithm re-employed to recognize emotions. We compared the performance of the original viola jones and fisher face-based viola jones using 50 images on the State University of Malang dataset by measuring the accuracy and time-consuming in the fps. The accuracy and time-consuming of the Viola-Jones algorithm reach 0.78 and 15 fps, whereas our proposed methods reach 0.82 and 1 fps. It can conclude that the fisher face-based viola-jones algorithm recognizes facial emotion as more accurate than the viola-jones algorithm
Multimodality in Online Education: A Comparative Study
The commencement of the decade brought along with it a grave pandemic and in
response the movement of education forums predominantly into the online world.
With a surge in the usage of online video conferencing platforms and tools to
better gauge student understanding, there needs to be a mechanism to assess
whether instructors can grasp the extent to which students understand the
subject and their response to the educational stimuli. The current systems
consider only a single cue with a lack of focus in the educational domain.
Thus, there is a necessity for the measurement of an all-encompassing holistic
overview of the students' reaction to the subject matter. This paper highlights
the need for a multimodal approach to affect recognition and its deployment in
the online classroom while considering four cues, posture and gesture, facial,
eye tracking and verbal recognition. It compares the various machine learning
models available for each cue and provides the most suitable approach given the
available dataset and parameters of classroom footage. A multimodal approach
derived from weighted majority voting is proposed by combining the most fitting
models from this analysis of individual cues based on accuracy, ease of
procuring data corpus, sensitivity and any major drawbacks
Out-of-plane action unit recognition using recurrent neural networks
A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science. Johannesburg, 2015.The face is a fundamental tool to assist in interpersonal communication and interaction between people.
Humans use facial expressions to consciously or subconsciously express their emotional states, such as
anger or surprise. As humans, we are able to easily identify changes in facial expressions even in complicated
scenarios, but the task of facial expression recognition and analysis is complex and challenging
to a computer. The automatic analysis of facial expressions by computers has applications in several scientific
subjects such as psychology, neurology, pain assessment, lie detection, intelligent environments,
psychiatry, and emotion and paralinguistic communication. We look at methods of facial expression
recognition, and in particular, the recognition of Facial Action Coding System’s (FACS) Action Units
(AUs). Movements of individual muscles on the face are encoded by FACS from slightly different, instant
changes in facial appearance. Contractions of specific facial muscles are related to a set of units
called AUs. We make use of Speeded Up Robust Features (SURF) to extract keypoints from the face and
use the SURF descriptors to create feature vectors. SURF provides smaller sized feature vectors than
other commonly used feature extraction techniques. SURF is comparable to or outperforms other methods
with respect to distinctiveness, robustness, and repeatability. It is also much faster than other feature
detectors and descriptors. The SURF descriptor is scale and rotation invariant and is unaffected by small
viewpoint changes or illumination changes. We use the SURF feature vectors to train a recurrent neural
network (RNN) to recognize AUs from the Cohn-Kanade database. An RNN is able to handle temporal
data received from image sequences in which an AU or combination of AUs are shown to develop from
a neutral face. We are recognizing AUs as they provide a more fine-grained means of measurement that
is independent of age, ethnicity, gender and different expression appearance. In addition to recognizing
FACS AUs from the Cohn-Kanade database, we use our trained RNNs to recognize the development
of pain in human subjects. We make use of the UNBC-McMaster pain database which contains image
sequences of people experiencing pain. In some cases, the pain results in their face moving out-of-plane
or some degree of in-plane movement. The temporal processing ability of RNNs can assist in classifying
AUs where the face is occluded and not facing frontally for some part of the sequence. Results are
promising when tested on the Cohn-Kanade database. We see higher overall recognition rates for upper
face AUs than lower face AUs. Since keypoints are globally extracted from the face in our system, local
feature extraction could provide improved recognition results in future work. We also see satisfactory
recognition results when tested on samples with out-of-plane head movement, showing the temporal
processing ability of RNNs
Emotion and Stress Recognition Related Sensors and Machine Learning Technologies
This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective
Ubiquitous Technologies for Emotion Recognition
Emotions play a very important role in how we think and behave. As such, the emotions we feel every day can compel us to act and influence the decisions and plans we make about our lives. Being able to measure, analyze, and better comprehend how or why our emotions may change is thus of much relevance to understand human behavior and its consequences. Despite the great efforts made in the past in the study of human emotions, it is only now, with the advent of wearable, mobile, and ubiquitous technologies, that we can aim to sense and recognize emotions, continuously and in real time. This book brings together the latest experiences, findings, and developments regarding ubiquitous sensing, modeling, and the recognition of human emotions
Shortest Route at Dynamic Location with Node Combination-Dijkstra Algorithm
Abstract— Online transportation has become a basic
requirement of the general public in support of all activities to go
to work, school or vacation to the sights. Public transportation
services compete to provide the best service so that consumers
feel comfortable using the services offered, so that all activities
are noticed, one of them is the search for the shortest route in
picking the buyer or delivering to the destination. Node
Combination method can minimize memory usage and this
methode is more optimal when compared to A* and Ant Colony
in the shortest route search like Dijkstra algorithm, but can’t
store the history node that has been passed. Therefore, using
node combination algorithm is very good in searching the
shortest distance is not the shortest route. This paper is
structured to modify the node combination algorithm to solve the
problem of finding the shortest route at the dynamic location
obtained from the transport fleet by displaying the nodes that
have the shortest distance and will be implemented in the
geographic information system in the form of map to facilitate
the use of the system.
Keywords— Shortest Path, Algorithm Dijkstra, Node
Combination, Dynamic Location (key words
Brain-Computer Interfaces for Non-clinical (Home, Sports, Art, Entertainment, Education, Well-being) Applications
HCI researchers interest in BCI is increasing because the technology industry is expanding into application areas where efficiency is not the main goal of concern. Domestic or public space use of information and communication technology raise awareness of the importance of affect, comfort, family, community, or playfulness, rather than efficiency. Therefore, in addition to non-clinical BCI applications that require efficiency and precision, this Research Topic also addresses the use of BCI for various types of domestic, entertainment, educational, sports, and well-being applications. These applications can relate to an individual user as well as to multiple cooperating or competing users. We also see a renewed interest of artists to make use of such devices to design interactive art installations that know about the brain activity of an individual user or the collective brain activity of a group of users, for example, an audience. Hence, this Research Topic also addresses how BCI technology influences artistic creation and practice, and the use of BCI technology to manipulate and control sound, video, and virtual and augmented reality (VR/AR)