A Combined Method of Skin-and Depth-based Hand Gesture Recognition

Sokhib, Tukhtaev; Whangbo, Taeg Keun

doi:10.34028/iajit/17/1/16

Cited by 3 publications

(5 citation statements)

References 18 publications

(27 reference statements)

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“…Finger count and finger position relative to palm are prominent features in the correct recognition of gestures [27]. Fingers can be detected in many ways such as convexity defects [26], K ‐cosine corner detection [20], and colour depth [10]. The finger count and detection method encompasses calculating the centre of gravity from the segmented hand and thereafter determining the maximum distances to tangent points of fingers [9].…”

Section: Methodsmentioning

confidence: 99%

“…Positions of thumb vividly contrast between certain characters [28]. Scale‐space feature detection can find palm‐like and finger‐like structures, so that the hand gesture is determined by the palm finger configuration [10, 19]. Curvature measure for blobs is used to determine the fingertips [10, 11] in images acquired from Alice.…”

Section: Methodsmentioning

confidence: 99%

“…Scale‐space feature detection can find palm‐like and finger‐like structures, so that the hand gesture is determined by the palm finger configuration [10, 19]. Curvature measure for blobs is used to determine the fingertips [10, 11] in images acquired from Alice. Our method of determining finger position is generally by finding the finger width to palm width ratio.…”

Section: Methodsmentioning

confidence: 99%

“…Favourable illumination will result in a more informative and favourable processed image. Handshape [1, 3, 8, 9] and finger information [3, 9–11] are major features which contribute to hand gesture recognition. The approach to realise these features varies based on the nature of gesture and its environment.…”

Section: Related Workmentioning

confidence: 99%

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Two viewpoints based real‐time recognition for hand gestures

Kumar

Guo

2020

IET image process

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It is extremely challenging to accomplish excellent accuracy for gesture recognition using an approach where complexity in computation time for recognition is less. This study compares accuracy in hand gesture recognition of a single viewpoint set‐up with proposed two viewpoint set‐up for different classification techniques. The efficacy of the presented approach is verified practically with various image processing, feature extraction and classification techniques. Two camera system make geometry learning and three‐dimensional (3D) view feasible compared to a single camera system. Geometrical features from additional viewpoint contribute to 3D view estimation of the hand gesture. It also improves the classification accuracy. Experimental results demonstrate that the proposed method show escalation in recognition rate compared to the single‐camera system, and also has great performance using simple classifiers like the nearest neighbour and decision tree. Classification within 1 s is considered as real‐time in this study.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Two viewpoints based real‐time recognition for hand gestures

Kumar

Guo

2020

IET image process

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“…However, they implemented the model using only six classes of hand gestures. Other researchers 15 used the inception model for hand gesture recognition, in which they fed the model with depth image data in addition to the color image data acquired by an acquisition device known as Kinect. Using transfer learning to train the last layer of the model on a target dataset of 10 classes, they reported highly accurate results for their model.…”

Section: Introductionmentioning

confidence: 99%

Using VGG Models with Intermediate Layer Feature Maps for Static Hand Gesture Recognition

Fadhil

Mahdi²,

Abbas³

2023

Baghdad Sci.J

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A hand gesture recognition system provides a robust and innovative solution to nonverbal communication through human–computer interaction. Deep learning models have excellent potential for usage in recognition applications. To overcome related issues, most previous studies have proposed new model architectures or have fine-tuned pre-trained models. Furthermore, these studies relied on one standard dataset for both training and testing. Thus, the accuracy of these studies is reasonable. Unlike these works, the current study investigates two deep learning models with intermediate layers to recognize static hand gesture images. Both models were tested on different datasets, adjusted to suit the dataset, and then trained under different methods. First, the models were initialized with random weights and trained from scratch. Afterward, the pre-trained models were examined as feature extractors. Finally, the pre-trained models were fine-tuned with intermediate layers. Fine-tuning was conducted on three levels: the fifth, fourth, and third blocks, respectively. The models were evaluated through recognition experiments using hand gesture images in the Arabic sign language acquired under different conditions. This study also provides a new hand gesture image dataset used in these experiments, plus two other datasets. The experimental results indicated that the proposed models can be used with intermediate layers to recognize hand gesture images. Furthermore, the analysis of the results showed that fine-tuning the fifth and fourth blocks of these two models achieved the best accuracy results. In particular, the testing accuracies on the three datasets were 96.51%, 72.65%, and 55.62% when fine-tuning the fourth block and 96.50%, 67.03%, and 61.09% when fine-tuning the fifth block for the first model. The testing accuracy for the second model showed approximately similar results.

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A Review on Vision-based Hand Gesture Recognition Targeting RGB-Depth Sensors

Rawat

Kane

Goswami

et al. 2022

Int. J. Info. Tech. Dec. Mak.

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With the advancement of automation, vision-based hand gesture recognition (HGR) is gaining popularity due to its numerous uses and ability to easily communicate with machines. However, identifying hand positions is the most difficult assignment due to the fact of crowded backgrounds, sensitivity to light, form, speed, size, and self-occlusion. This review summarizes the most recent studies on hand postures and motion tracking using a vision-based approach by applying Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). The parts and subsections of this review article are organized into numerous categories, the most essential of which are picture acquisition, preprocessing, tracking and segmentation, feature extraction, collation of key gesture identification phases, and classification. At each level, the various algorithms are evaluated based on critical key points such as localization, largest blob, per pixel binary segmentation, depth information, and so on. Furthermore, the datasets and future scopes of HGR approaches are discussed considering merits, limitations, and challenges.

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A Combined Method of Skin-and Depth-based Hand Gesture Recognition

Cited by 3 publications

References 18 publications

Two viewpoints based real‐time recognition for hand gestures

Two viewpoints based real‐time recognition for hand gestures

Using VGG Models with Intermediate Layer Feature Maps for Static Hand Gesture Recognition

A Review on Vision-based Hand Gesture Recognition Targeting RGB-Depth Sensors

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