Stereo Vision-based Hand Gesture Recognition under 3D Environment

Laskar, Mohidul Alam; Das, Amlan; Talukdar, Anjan Kumar; Sarma, Kandarpa Kumar

doi:10.1016/j.procs.2015.08.053

Cited by 15 publications

(4 citation statements)

References 14 publications

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“…They suggested two-pipeline architecture and worked on three datasets called SHAPE, HANDS and OUHANDS to achieved accuracy of 94%, 94% and 98% respectively. To get front and back gestures, a stereo vision-based HGR in a 3D environment is discussed in [16]. Use of color-based segmentation for background removal and to get disparity map, stereo vision techniques are applied.…”

Section: Related Workmentioning

confidence: 99%

Use of Key Points and Transfer Learning Techniques in Recognition of Handedness Indian Sign Language

Ingoley,

Bakal

2023

IJRITCC

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The most expressive way of communication for individuals who have trouble speaking or hearing is sign language. Normal people are unable to comprehend sign language. As a result, communication barriers are put up. Majority of people are right-handed. Statistics say that, an average population of left-handed person in the world is about 10%, where they use left hand as their dominating hand. In case of hand written text recognition, if the text is written by left-handed or right-handed person, then there would not be any problem in recognition neither for human and nor for computer. But same thing is not true for sign language and its detection using computer. When the detection is performed using computer vision and if it falls into the category of detection by appearance, then it might not detect correctly. In machine and deep learning, if the model is trained using just one dominating hand, let’s say right hand, then the predictions can go wrong if same sign is performed by left-handed person. This paper addresses this issue. It takes into account the signs performed by any type of signer: left-handed, right-handed or ambidexter. In proposed work is on Indian Sign Language (ISL). Two models are trained: Model I, is trained on one dominating hand and Model II, is trained on both the hands. Model II gives correct predictions regardless of any type of signer. It recognizes alphabets and numbers in ISL. We used the concept of Key points and Transfer Learning techniques for implementation. Using this approach, models get trained quickly and we could achieve validation accuracy of 99%.

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Section: Related Workmentioning

confidence: 99%

Use of Key Points and Transfer Learning Techniques in Recognition of Handedness Indian Sign Language

Ingoley,

Bakal

2023

IJRITCC

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“…It is worth mentioning that some RGB-D cameras, based on the time-of-flight principle, operate even in a dark room [40]. Solutions using stereovision and multi-camera systems have been known in the literature for a long time, e.g., [41][42][43]. Currently, most methods use RGB-D cameras, e.g., [44][45][46][47].…”

Section: Recent Workmentioning

confidence: 99%

Recognition of Signed Expressions in an Experimental System Supporting Deaf Clients in the City Office

Kapuściński

Wysocki

2020

Sensors

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The paper addresses the recognition of dynamic Polish Sign Language expressions in an experimental system supporting deaf people in an office when applying for an ID card. A method of processing a continuous stream of RGB-D data and a feature vector are proposed. The classification is carried out using the k-nearest neighbors algorithm with dynamic time warping, hidden Markov models, and bidirectional long short-term memory. The leave-one-subject-out protocol is used for the dataset containing 121 Polish Sign Language sentences performed five times by four deaf people. A data augmentation method is also proposed and tested. Preliminary observations and conclusions from the use of the system in a laboratory, as well as in real conditions with an experimental installation in the Office of Civil Affairs are given.

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“…This can be done using helping tools such as depth cameras [20] or stereoscopic vision [21]. The most common method is to use skin colour detection in RGB cameras [21][22][23][24][25][26][27][28][29]. Hand segmentation can be done in a non-controlled or a controlled environment.…”

Section: Introductionmentioning

confidence: 99%

Hand features extractor using hand contour – a case study

et al. 2019

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Hand gesture recognition is an important topic in natural user interfaces (NUI). Hand features extraction is the first step for hand gesture recognition. This work proposes a novel real time method for hand features recognition. In our framework we use three cameras and the hand region is extracted with the background subtraction method. Features like arm angle and fingers positions are calculated using Y variations in the vertical contour image. Wrist detection is obtained by calculating the bigger distance from a base line and the hand contour, giving the main features for the hand gesture recognition. Experiments on our own data-set of about 1800 images show that our method performs well and is highly efficient.

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Stereo Vision-based Hand Gesture Recognition under 3D Environment

Cited by 15 publications

References 14 publications

Use of Key Points and Transfer Learning Techniques in Recognition of Handedness Indian Sign Language

Use of Key Points and Transfer Learning Techniques in Recognition of Handedness Indian Sign Language

Recognition of Signed Expressions in an Experimental System Supporting Deaf Clients in the City Office

Hand features extractor using hand contour – a case study

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