494 research outputs found
Event-based Face Detection and Tracking in the Blink of an Eye
We present the first purely event-based method for face detection using the
high temporal resolution of an event-based camera. We will rely on a new
feature that has never been used for such a task that relies on detecting eye
blinks. Eye blinks are a unique natural dynamic signature of human faces that
is captured well by event-based sensors that rely on relative changes of
luminance. Although an eye blink can be captured with conventional cameras, we
will show that the dynamics of eye blinks combined with the fact that two eyes
act simultaneously allows to derive a robust methodology for face detection at
a low computational cost and high temporal resolution. We show that eye blinks
have a unique temporal signature over time that can be easily detected by
correlating the acquired local activity with a generic temporal model of eye
blinks that has been generated from a wide population of users. We furthermore
show that once the face is reliably detected it is possible to apply a
probabilistic framework to track the spatial position of a face for each
incoming event while updating the position of trackers. Results are shown for
several indoor and outdoor experiments. We will also release an annotated data
set that can be used for future work on the topic
Ongoing EEG artifact correction using blind source separation
Objective: Analysis of the electroencephalogram (EEG) for epileptic spike and
seizure detection or brain-computer interfaces can be severely hampered by the
presence of artifacts. The aim of this study is to describe and evaluate a fast
automatic algorithm for ongoing correction of artifacts in continuous EEG
recordings, which can be applied offline and online. Methods: The automatic
algorithm for ongoing correction of artifacts is based on fast blind source
separation. It uses a sliding window technique with overlapping epochs and
features in the spatial, temporal and frequency domain to detect and correct
ocular, cardiac, muscle and powerline artifacts. Results: The approach was
validated in an independent evaluation study on publicly available continuous
EEG data with 2035 marked artifacts. Validation confirmed that 88% of the
artifacts could be removed successfully (ocular: 81%, cardiac: 84%, muscle:
98%, powerline: 100%). It outperformed state-of-the-art algorithms both in
terms of artifact reduction rates and computation time. Conclusions: Fast
ongoing artifact correction successfully removed a good proportion of
artifacts, while preserving most of the EEG signals. Significance: The
presented algorithm may be useful for ongoing correction of artifacts, e.g., in
online systems for epileptic spike and seizure detection or brain-computer
interfaces.Comment: 16 pages, 4 figures, 3 table
Generation of realistic human behaviour
As the use of computers and robots in our everyday lives increases so does the need for better interaction with these devices. Human-computer interaction relies on the ability to understand and generate human behavioural signals such as speech, facial expressions and motion. This thesis deals with the synthesis and evaluation of such signals, focusing not only on their intelligibility but also on their realism. Since these signals are often correlated, it is common for methods to drive the generation of one signal using another. The thesis begins by tackling the problem of speech-driven facial animation and proposing models capable of producing realistic animations from a single image and an audio clip. The goal of these models is to produce a video of a target person, whose lips move in accordance with the driving audio. Particular focus is also placed on a) generating spontaneous expression such as blinks, b) achieving audio-visual synchrony and c) transferring or producing natural head motion. The second problem addressed in this thesis is that of video-driven speech reconstruction, which aims at converting a silent video into waveforms containing speech. The method proposed for solving this problem is capable of generating intelligible and accurate speech for both seen and unseen speakers. The spoken content is correctly captured thanks to a perceptual loss, which uses features from pre-trained speech-driven animation models. The ability of the video-to-speech model to run in real-time allows its use in hearing assistive devices and telecommunications. The final work proposed in this thesis is a generic domain translation system, that can be used for any translation problem including those mapping across different modalities. The framework is made up of two networks performing translations in opposite directions and can be successfully applied to solve diverse sets of translation problems, including speech-driven animation and video-driven speech reconstruction.Open Acces
Stimulating uncertainty: Amplifying the quantum vacuum with superconducting circuits
The ability to generate particles from the quantum vacuum is one of the most
profound consequences of Heisenberg's uncertainty principle. Although the
significance of vacuum fluctuations can be seen throughout physics, the
experimental realization of vacuum amplification effects has until now been
limited to a few cases. Superconducting circuit devices, driven by the goal to
achieve a viable quantum computer, have been used in the experimental
demonstration of the dynamical Casimir effect, and may soon be able to realize
the elusive verification of analogue Hawking radiation. This article describes
several mechanisms for generating photons from the quantum vacuum and
emphasizes their connection to the well-known parametric amplifier from quantum
optics. Discussed in detail is the possible realization of each mechanism, or
its analogue, in superconducting circuit systems. The ability to selectively
engineer these circuit devices highlights the relationship between the various
amplification mechanisms.Comment: 27 pages, 10 figures, version published in Rev. Mod. Phys. as a
Colloquiu
Development of a practical and mobile brain-computer communication device for profoundly paralyzed individuals
Thesis (Ph.D.)--Boston UniversityBrain-computer interface (BCI) technology has seen tremendous growth over the past several decades, with numerous groundbreaking research studies demonstrating technical viability (Sellers et al., 2010; Silvoni et al., 2011). Despite this progress, BCIs have remained primarily in controlled laboratory settings. This dissertation proffers a blueprint for translating research-grade BCI systems into real-world applications that are noninvasive and fully portable, and that employ intelligent user interfaces for communication. The proposed architecture is designed to be used by severely motor-impaired individuals, such as those with locked-in syndrome, while reducing the effort and cognitive load needed to communicate. Such a system requires the merging of two primary research fields: 1) electroencephalography (EEG)-based BCIs and 2) intelligent user interface design.
The EEG-based BCI portion of this dissertation provides a history of the field, details of our software and hardware implementation, and results from an experimental study aimed at verifying the utility of a BCI based on the steady-state visual evoked potential (SSVEP), a robust brain response to visual stimulation at controlled frequencies. The visual stimulation, feature extraction, and classification algorithms for the BCI were specially designed to achieve successful real-time performance on a laptop computer. Also, the BCI was developed in Python, an open-source programming language that combines programming ease with effective handling of hardware and software requirements. The result of this work was The Unlock Project app software for BCI development. Using it, a four-choice SSVEP BCI setup was implemented and tested with five severely motor-impaired and fourteen control participants. The system showed a wide range of usability across participants, with classification rates ranging from 25-95%.
The second portion of the dissertation discusses the viability of intelligent user interface design as a method for obtaining a more user-focused vocal output communication aid tailored to motor-impaired individuals. A proposed blueprint of this communication "app" was developed in this dissertation. It would make use of readily available laptop sensors to perform facial recognition, speech-to-text decoding, and geo-location. The ultimate goal is to couple sensor information with natural language processing to construct an intelligent user interface that shapes communication in a practical SSVEP-based BCI
A study of the temporal relationship between eye actions and facial expressions
A dissertation submitted in ful llment of the requirements for the
degree of Master of Science
in the
School of Computer Science and Applied Mathematics
Faculty of Science
August 15, 2017Facial expression recognition is one of the most common means of communication used
for complementing spoken word. However, people have grown to master ways of ex-
hibiting deceptive expressions. Hence, it is imperative to understand di erences in
expressions mostly for security purposes among others. Traditional methods employ
machine learning techniques in di erentiating real and fake expressions. However, this
approach does not always work as human subjects can easily mimic real expressions with
a bit of practice. This study presents an approach that evaluates the time related dis-
tance that exists between eye actions and an exhibited expression. The approach gives
insights on some of the most fundamental characteristics of expressions. The study fo-
cuses on nding and understanding the temporal relationship that exists between eye
blinks and smiles. It further looks at the relationship that exits between eye closure and
pain expressions. The study incorporates active appearance models (AAM) for feature
extraction and support vector machines (SVM) for classi cation. It tests extreme learn-
ing machines (ELM) in both smile and pain studies, which in turn, attains excellent
results than predominant algorithms like the SVM. The study shows that eye blinks
are highly correlated with the beginning of a smile in posed smiles while eye blinks are
highly correlated with the end of a smile in spontaneous smiles. A high correlation is
observed between eye closure and pain in spontaneous pain expressions. Furthermore,
this study brings about ideas that lead to potential applications such as lie detection
systems, robust health care monitoring systems and enhanced animation design systems
among others.MT 201
The brain’s router : a cortical network model of serial processing in the primate brain
The human brain efficiently solves certain operations such as object recognition and categorization through a massively parallel network of dedicated processors. However, human cognition also relies on the ability to perform an arbitrarily large set of tasks by flexibly recombining different processors into a novel chain. This flexibility comes at the cost of a severe slowing down and a seriality of operations (100–500 ms per step). A limit on parallel processing is demonstrated in experimental setups such as the psychological refractory period (PRP) and the attentional blink (AB) in which the processing of an element either significantly delays (PRP) or impedes conscious access (AB) of a second, rapidly presented element. Here we present a spiking-neuron implementation of a cognitive architecture where a large number of local parallel processors assemble together to produce goal-driven behavior. The precise mapping of incoming sensory stimuli onto motor representations relies on a ‘‘router’’ network capable of flexibly interconnecting processors and rapidly changing its configuration from one task to another. Simulations show that, when presented with dual-task stimuli, the network exhibits parallel processing at peripheral sensory levels, a memory buffer capable of keeping the result of sensory processing on hold, and a slow serial performance at the router stage, resulting in a performance bottleneck. The network captures the detailed dynamics of human behavior during dual-task-performance, including both mean RTs and RT distributions, and establishes concrete predictions on neuronal dynamics during dual-task experiments in humans and non-human primates.Fil: Zylberberg, Ariel. Laboratory of Integrative Neuroscience, Physics Department, University of Buenos Aires, Buenos Aires, Argentina. Institute of Biomedical Engineering, Faculty of Engineering, University of Buenos Aires, Buenos Aires, Argentina
A study of the temporal relationship between eye actions and facial expressions
A dissertation submitted in ful llment of the requirements for the
degree of Master of Science
in the
School of Computer Science and Applied Mathematics
Faculty of Science
August 15, 2017Facial expression recognition is one of the most common means of communication used
for complementing spoken word. However, people have grown to master ways of ex-
hibiting deceptive expressions. Hence, it is imperative to understand di erences in
expressions mostly for security purposes among others. Traditional methods employ
machine learning techniques in di erentiating real and fake expressions. However, this
approach does not always work as human subjects can easily mimic real expressions with
a bit of practice. This study presents an approach that evaluates the time related dis-
tance that exists between eye actions and an exhibited expression. The approach gives
insights on some of the most fundamental characteristics of expressions. The study fo-
cuses on nding and understanding the temporal relationship that exists between eye
blinks and smiles. It further looks at the relationship that exits between eye closure and
pain expressions. The study incorporates active appearance models (AAM) for feature
extraction and support vector machines (SVM) for classi cation. It tests extreme learn-
ing machines (ELM) in both smile and pain studies, which in turn, attains excellent
results than predominant algorithms like the SVM. The study shows that eye blinks
are highly correlated with the beginning of a smile in posed smiles while eye blinks are
highly correlated with the end of a smile in spontaneous smiles. A high correlation is
observed between eye closure and pain in spontaneous pain expressions. Furthermore,
this study brings about ideas that lead to potential applications such as lie detection
systems, robust health care monitoring systems and enhanced animation design systems
among others.MT 201
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