477,998 research outputs found
Recommended from our members
Weightless neural networks for face recognition
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The interface with the real-world has proved to be extremely challenging throughout the past 70 years in which computer technology has been developing. The problem initially is assumed to be somewhat trivial, as humans are exceptionally skilled at interpreting real-world data, for example pictures and sounds. Traditional analytical methods have so far not provided the complete answer to what will be termed pattern recognition.
Biological inspiration has motivated pattern recognition researchers since the early days of the subject, and the idea of a neural network which has self-evolving properties has always been seen to be a potential solution to this endeavour. Unlike the development of computer technology in which successive generations of improved devices have been developed, the neural network approach has been less successful, with major setbacks occurring in its development. However, the fact that natural processing in animals and humans is a voltage-based process, devoid of software, and self-evolving, provides an on-going motivation for pattern recognition in artificial neural networks. This thesis addresses the application of weightless neural networks using a ranking pre-processor to implement general pattern recognition with specific reference to face processing. The evaluation of the system will be carried out on open source databases in order to obtain a direct comparison of the efficacy of the method, in particular considerable use will be made of the MIT-CBCL face database. The methodology is cost effective in both software and hardware forms, offers real-time video processing, and can be implemented on all computer platforms. The results of this research show significant improvements over published results, and provide a viable commercial methodology for general pattern recognition
DMD: A Large-Scale Multi-Modal Driver Monitoring Dataset for Attention and Alertness Analysis
Vision is the richest and most cost-effective technology for Driver
Monitoring Systems (DMS), especially after the recent success of Deep Learning
(DL) methods. The lack of sufficiently large and comprehensive datasets is
currently a bottleneck for the progress of DMS development, crucial for the
transition of automated driving from SAE Level-2 to SAE Level-3. In this paper,
we introduce the Driver Monitoring Dataset (DMD), an extensive dataset which
includes real and simulated driving scenarios: distraction, gaze allocation,
drowsiness, hands-wheel interaction and context data, in 41 hours of RGB, depth
and IR videos from 3 cameras capturing face, body and hands of 37 drivers. A
comparison with existing similar datasets is included, which shows the DMD is
more extensive, diverse, and multi-purpose. The usage of the DMD is illustrated
by extracting a subset of it, the dBehaviourMD dataset, containing 13
distraction activities, prepared to be used in DL training processes.
Furthermore, we propose a robust and real-time driver behaviour recognition
system targeting a real-world application that can run on cost-efficient
CPU-only platforms, based on the dBehaviourMD. Its performance is evaluated
with different types of fusion strategies, which all reach enhanced accuracy
still providing real-time response.Comment: Accepted to ECCV 2020 workshop - Assistive Computer Vision and
Robotic
Emotions in context: examining pervasive affective sensing systems, applications, and analyses
Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely; âsensingâ, âanalysisâ, and âapplicationâ. Sensing investigates the different sensing modalities that are used in existing real-time affective applications, Analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and Application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing
The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism
Computer vision and other biometrics data science applications have commenced
a new project of profiling people. Rather than using 'transaction generated
information', these systems measure the 'real world' and produce an assessment
of the 'world state' - in this case an assessment of some individual trait.
Instead of using proxies or scores to evaluate people, they increasingly deploy
a logic of revealing the truth about reality and the people within it. While
these profiling knowledge claims are sometimes tentative, they increasingly
suggest that only through computation can these excesses of reality be captured
and understood. This article explores the bases of those claims in the systems
of measurement, representation, and classification deployed in computer vision.
It asks if there is something new in this type of knowledge claim, sketches an
account of a new form of computational empiricism being operationalised, and
questions what kind of human subject is being constructed by these
technological systems and practices. Finally, the article explores legal
mechanisms for contesting the emergence of computational empiricism as the
dominant knowledge platform for understanding the world and the people within
it
Shopping For Privacy: How Technology in Brick-and-Mortar Retail Stores Poses Privacy Risks for Shoppers
As technology continues to rapidly advance, the American legal system has failed to protect individual shoppers from the technology implemented into retail stores, which poses significant privacy risks but does not violate the law. In particular, I examine the technologies implemented into many brick-and-mortar stores today, many of which the average everyday shopper has no idea exists. This Article criticizes these technologies, suggesting that many, if not all of them, are questionable in their legality taking advantage of their status in a legal gray zone. Because the American judicial system cannot adequately protect the individual shopper from these questionable privacy practices, I call upon the Federal Trade Commission, the de facto privacy regulator in the United States, to increase its policing of physical retail stores to protect the shopper from any further harm
DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translation
There is an undeniable communication barrier between deaf people and people
with normal hearing ability. Although innovations in sign language translation
technology aim to tear down this communication barrier, the majority of
existing sign language translation systems are either intrusive or constrained
by resolution or ambient lighting conditions. Moreover, these existing systems
can only perform single-sign ASL translation rather than sentence-level
translation, making them much less useful in daily-life communication
scenarios. In this work, we fill this critical gap by presenting DeepASL, a
transformative deep learning-based sign language translation technology that
enables ubiquitous and non-intrusive American Sign Language (ASL) translation
at both word and sentence levels. DeepASL uses infrared light as its sensing
mechanism to non-intrusively capture the ASL signs. It incorporates a novel
hierarchical bidirectional deep recurrent neural network (HB-RNN) and a
probabilistic framework based on Connectionist Temporal Classification (CTC)
for word-level and sentence-level ASL translation respectively. To evaluate its
performance, we have collected 7,306 samples from 11 participants, covering 56
commonly used ASL words and 100 ASL sentences. DeepASL achieves an average
94.5% word-level translation accuracy and an average 8.2% word error rate on
translating unseen ASL sentences. Given its promising performance, we believe
DeepASL represents a significant step towards breaking the communication
barrier between deaf people and hearing majority, and thus has the significant
potential to fundamentally change deaf people's lives
- âŠ