36,473 research outputs found
Intelligent Approaches to interact with Machines using Hand Gesture Recognition in Natural way: A Survey
Hand gestures recognition (HGR) is one of the main areas of research for the
engineers, scientists and bioinformatics. HGR is the natural way of Human
Machine interaction and today many researchers in the academia and industry are
working on different application to make interactions more easy, natural and
convenient without wearing any extra device. HGR can be applied from games
control to vision enabled robot control, from virtual reality to smart home
systems. In this paper we are discussing work done in the area of hand gesture
recognition where focus is on the intelligent approaches including soft
computing based methods like artificial neural network, fuzzy logic, genetic
algorithms etc. The methods in the preprocessing of image for segmentation and
hand image construction also taken into study. Most researchers used fingertips
for hand detection in appearance based modeling. Finally the comparison of
results given by different researchers is also presented
Real-Time Human-Computer Interaction Based on Face and Hand Gesture Recognition
At the present time, hand gestures recognition system could be used as a more
expected and useable approach for human computer interaction. Automatic hand
gesture recognition system provides us a new tactic for interactive with the
virtual environment. In this paper, a face and hand gesture recognition system
which is able to control computer media player is offered. Hand gesture and
human face are the key element to interact with the smart system. We used the
face recognition scheme for viewer verification and the hand gesture
recognition in mechanism of computer media player, for instance, volume
down/up, next music and etc. In the proposed technique, first, the hand gesture
and face location is extracted from the main image by combination of skin and
cascade detector and then is sent to recognition stage. In recognition stage,
first, the threshold condition is inspected then the extracted face and gesture
will be recognized. In the result stage, the proposed technique is applied on
the video dataset and the high precision ratio acquired. Additional the
recommended hand gesture recognition method is applied on static American Sign
Language (ASL) database and the correctness rate achieved nearby 99.40%. also
the planned method could be used in gesture based computer games and virtual
reality
Kinect Sensor Based Gesture Recognition for Surveillance Application
Hand gesture recognition has been granted as one of the emerging fields in
research today providing a natural way of communication between man and a
machine. Gestures are some forms of body motions which a person expresses when
doing a work or giving a reply. Human body tracking is a well studied topic in
todays era of Human Computer Interaction and it can be formed by the virtue of
human skeleton structures. These skeleton structures have been detected
successfully due to the smart progress of some devices, used to measure depth.
Human body movements have been viewed using these depth sensors which can
provide sufficient accuracy while tracking full body in real time mode with low
cost. In reality action and reaction activities are hardly periodic in a multi
person perspective situation. Also recognizing their complex a-periodic
gestures are highly challenging for detection in surveillance system
A Novel Human Computer Interaction Platform based College Mathematical Education Methodology
This article proposes the analysis on novel human computer interaction (HCI)
platform based college mathematical education methodology. Above for the
application of virtual reality technology in teaching the problems in the
study, only through the organization focus on the professional and technical
personnel, and constantly improve researchers in development process of
professional knowledge, close to the actual needs of the teaching can we
achieve the satisfactory result. To obtain better education output, we combine
the Kinect to form the HCI based teaching environment. We firstly review the
latest HCI technique and principles of college math courses, then we introduce
basic components of the Kinect including the gesture segmentation, systematic
implementation and the primary characteristics of the platform. As the further
step, we implement the system with the re-write of script code to build up the
personalized HCI assisted education scenario. The verification and simulation
proves the feasibility of our method
A Gaze-Assisted Multimodal Approach to Rich and Accessible Human-Computer Interaction
Recent advancements in eye tracking technology are driving the adoption of
gaze-assisted interaction as a rich and accessible human-computer interaction
paradigm. Gaze-assisted interaction serves as a contextual, non-invasive, and
explicit control method for users without disabilities; for users with motor or
speech impairments, text entry by gaze serves as the primary means of
communication. Despite significant advantages, gaze-assisted interaction is
still not widely accepted because of its inherent limitations: 1) Midas touch,
2) low accuracy for mouse-like interactions, 3) need for repeated calibration,
4) visual fatigue with prolonged usage, 5) lower gaze typing speed, and so on.
This dissertation research proposes a gaze-assisted, multimodal, interaction
paradigm, and related frameworks and their applications that effectively enable
gaze-assisted interactions while addressing many of the current limitations. In
this regard, we present four systems that leverage gaze-assisted interaction:
1) a gaze- and foot-operated system for precise point-and-click interactions,
2) a dwell-free, foot-operated gaze typing system. 3) a gaze gesture-based
authentication system, and 4) a gaze gesture-based interaction toolkit. In
addition, we also present the goals to be achieved, technical approach, and
overall contributions of this dissertation research.Comment: 4 pages, 5 figures, ACM Richard Tapia Conference, Atlanta, 201
IPN Hand: A Video Dataset and Benchmark for Real-Time Continuous Hand Gesture Recognition
In the research community of continuous hand gesture recognition (HGR), the
current publicly available datasets lack real-world elements needed to build
responsive and efficient HGR systems. In this paper, we introduce a new
benchmark dataset named IPN Hand with sufficient size, variation, and
real-world elements able to train and evaluate deep neural networks. This
dataset contains more than 4 000 gesture samples and 800 000 RGB frames from 50
distinct subjects. We design 13 different static and dynamic gestures focused
on interaction with touchless screens. We especially consider the scenario when
continuous gestures are performed without transition states, and when subjects
perform natural movements with their hands as non-gesture actions. Gestures
were collected from about 30 diverse scenes, with real-world variation in
background and illumination. With our dataset, the performance of three 3D-CNN
models is evaluated on the tasks of isolated and continuous real-time HGR.
Furthermore, we analyze the possibility of increasing the recognition accuracy
by adding multiple modalities derived from RGB frames, i.e., optical flow and
semantic segmentation, while keeping the real-time performance of the 3D-CNN
model. Our empirical study also provides a comparison with the publicly
available nvGesture (NVIDIA) dataset. The experimental results show that the
state-of-the-art ResNext-101 model decreases about 30% accuracy when using our
real-world dataset, demonstrating that the IPN Hand dataset can be used as a
benchmark, and may help the community to step forward in the continuous HGR.
Our dataset and pre-trained models used in the evaluation are publicly
available at https://github.com/GibranBenitez/IPN-hand.Comment: Under revie
Fingertip Detection and Tracking for Recognition of Air-Writing in Videos
Air-writing is the process of writing characters or words in free space using
finger or hand movements without the aid of any hand-held device. In this work,
we address the problem of mid-air finger writing using web-cam video as input.
In spite of recent advances in object detection and tracking, accurate and
robust detection and tracking of the fingertip remains a challenging task,
primarily due to small dimension of the fingertip. Moreover, the initialization
and termination of mid-air finger writing is also challenging due to the
absence of any standard delimiting criterion. To solve these problems, we
propose a new writing hand pose detection algorithm for initialization of
air-writing using the Faster R-CNN framework for accurate hand detection
followed by hand segmentation and finally counting the number of raised fingers
based on geometrical properties of the hand. Further, we propose a robust
fingertip detection and tracking approach using a new signature function called
distance-weighted curvature entropy. Finally, a fingertip velocity-based
termination criterion is used as a delimiter to mark the completion of the
air-writing gesture. Experiments show the superiority of the proposed fingertip
detection and tracking algorithm over state-of-the-art approaches giving a mean
precision of 73.1 % while achieving real-time performance at 18.5 fps, a
condition which is of vital importance to air-writing. Character recognition
experiments give a mean accuracy of 96.11 % using the proposed air-writing
system, a result which is comparable to that of existing handwritten character
recognition systems.Comment: 32 pages, 10 figures, 2 tables. Submitted to Journal of Expert
Systems with Application
BeCAPTCHA: Behavioral Bot Detection using Touchscreen and Mobile Sensors benchmarked on HuMIdb
In this paper we study the suitability of a new generation of CAPTCHA methods
based on smartphone interactions. The heterogeneous flow of data generated
during the interaction with the smartphones can be used to model human behavior
when interacting with the technology and improve bot detection algorithms. For
this, we propose BeCAPTCHA, a CAPTCHA method based on the analysis of the
touchscreen information obtained during a single drag and drop task in
combination with the accelerometer data. The goal of BeCAPTCHA is to determine
whether the drag and drop task was realized by a human or a bot. We evaluate
the method by generating fake samples synthesized with Generative Adversarial
Neural Networks and handcrafted methods. Our results suggest the potential of
mobile sensors to characterize the human behavior and develop a new generation
of CAPTCHAs. The experiments are evaluated with HuMIdb (Human Mobile
Interaction database), a novel multimodal mobile database that comprises 14
mobile sensors acquired from 600 users. HuMIdb is freely available to the
research community.Comment: arXiv admin note: substantial text overlap with arXiv:2002.0091
Recent Advances and Challenges in Ubiquitous Sensing
Ubiquitous sensing is tightly coupled with activity recognition. This survey
reviews recent advances in Ubiquitous sensing and looks ahead on promising
future directions. In particular, Ubiquitous sensing crosses new barriers
giving us new ways to interact with the environment or to inspect our psyche.
Through sensing paradigms that parasitically utilise stimuli from the noise of
environmental, third-party pre-installed systems, sensing leaves the boundaries
of the personal domain. Compared to previous environmental sensing approaches,
these new systems mitigate high installation and placement cost by providing a
robustness towards process noise. On the other hand, sensing focuses inward and
attempts to capture mental activities such as cognitive load, fatigue or
emotion through advances in, for instance, eye-gaze sensing systems or
interpretation of body gesture or pose. This survey summarises these
developments and discusses current research questions and promising future
directions.Comment: Submitted to PIEE
Doppler-Radar Based Hand Gesture Recognition System Using Convolutional Neural Networks
Hand gesture recognition has long been a hot topic in human computer
interaction. Traditional camera-based hand gesture recognition systems cannot
work properly under dark circumstances. In this paper, a Doppler Radar based
hand gesture recognition system using convolutional neural networks is
proposed. A cost-effective Doppler radar sensor with dual receiving channels at
5.8GHz is used to acquire a big database of four standard gestures. The
received hand gesture signals are then processed with time-frequency analysis.
Convolutional neural networks are used to classify different gestures.
Experimental results verify the effectiveness of the system with an accuracy of
98%. Besides, related factors such as recognition distance and gesture scale
are investigated.Comment: Best Paper Award of International Conference on Communications,
Signal Processing, and Systems 201
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