Hand Gesture Recognition Using Local Histogram Feature Descriptor

Reddy, Dandu Amarnatha; Sahoo, Jaya Prakash; Ari, Samit

doi:10.1109/icoei.2018.8553849

Cited by 15 publications

(4 citation statements)

References 12 publications

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“…The LHFD feature extraction method proposed by Reddy et al [33] involved dividing the pre-processed image of hand gestures into 16 blocks, calculating the histogram for each block, and concatenating the histograms to form a feature vector. However, LHFD was sensitive to image scaling and rotation, limiting its effectiveness for images with different sizes and orientations.…”

Section: Related Workmentioning

confidence: 99%

SDViT: Stacking of Distilled Vision Transformers for Hand Gesture Recognition

Tan,

Lim,

Lee

et al. 2023

Applied Sciences

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Hand gesture recognition (HGR) is a rapidly evolving field with the potential to revolutionize human–computer interactions by enabling machines to interpret and understand human gestures for intuitive communication and control. However, HGR faces challenges such as the high similarity of hand gestures, real-time performance, and model generalization. To address these challenges, this paper proposes the stacking of distilled vision transformers, referred to as SDViT, for hand gesture recognition. An initially pretrained vision transformer (ViT) featuring a self-attention mechanism is introduced to effectively capture intricate connections among image patches, thereby enhancing its capability to handle the challenge of high similarity between hand gestures. Subsequently, knowledge distillation is proposed to compress the ViT model and improve model generalization. Multiple distilled ViTs are then stacked to achieve higher predictive performance and reduce overfitting. The proposed SDViT model achieves a promising performance on three benchmark datasets for hand gesture recognition: the American Sign Language (ASL) dataset, the ASL with digits dataset, and the National University of Singapore (NUS) hand gesture dataset. The accuracies achieved on these datasets are 100.00%, 99.60%, and 100.00%, respectively.

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Section: Related Workmentioning

confidence: 99%

SDViT: Stacking of Distilled Vision Transformers for Hand Gesture Recognition

Tan,

Lim,

Lee

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…The local descriptors [20,21,29] are used for object recognition and identification [30] like SIFT, SURF. Various techniques such as Zernike moments, Hu moments, HOG, SIFT, ED, FD, DWT, ANN, CNN, Fuzzy, GA have been employed for feature extraction in vision-based gesture recognition system [24,25,[31][32][33][34][35].…”

Section: Feature Extraction In Isl and Its Techniquesmentioning

confidence: 99%

“…Hamda and Mahmoudi [66] uses HOG for vision-based gesture recognition. Reddy et al [35] extracts global descriptors of image by local histogram feature descriptor (LHFD). Evolutionary algorithm is now marking a trend in field of HCI, hence in ISL recognition they are very convenient for use.…”

Section: Soft Computing Based Feature Extractionsmentioning

confidence: 99%

Feature Extraction Technique for Vision-Based Indian Sign Language Recognition System: A Review

Tyagi

Bansal

2020

Advances in Intelligent Systems and Computing

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Vision-based sign language recognition system is an emerging research area in human-computer interaction (HCI). It has been proved as a powerful communication tool for deaf and mute society, irrespective of geographical differences. Automatic sign language recognition system works broadly in three phases namely image pre-processing, feature extraction and classification of gestures. The output of the feature extraction phase is crucial for various classifiers. This paper presents a comprehensive review of feature extraction techniques used in vision-based sign language recognition system. A taxonomy of currently used techniques for feature extraction has been presented. The paper concludes by presenting future direction in feature extraction technique for Indian sign language (ISL).

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“…Moreover, some researchers utilized histogram of oriented gradients (HOG) [33], local binary patterns (LBP) [34], HOG, means, variances and Haar [35] features, and AdaBoost classifiers [36][37][38][39] to classify hand postures. Some researchers used spatial histogram coding of nonsubsampled contourlet trans-form coefficients [23], Gabor [40], local histogram [41], multiple kernels [42], saliency map, Gabor and pyramid histogram of oriented gradients [43], and support vector machine (SVM) [44,45] to classify hand postures. These existing methods consistently apply machine leaning methods to train a model using the data which is prepared in advance.…”

Section: Introductionmentioning

confidence: 99%

Approach to hand posture recognition based on hand shape features for human–robot interaction

Ding

2021

Complex Intell. Syst.

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Hand segmentation is the initial step for hand posture recognition. To reduce the effect of variable illumination in hand segmentation step, a new CbCr-I component Gaussian mixture model (GMM) is proposed to detect the skin region. The hand region is selected as a region of interest from the image using the skin detection technique based on the presented CbCr-I component GMM and a new adaptive threshold. A new hand shape distribution feature described in polar coordinates is proposed to extract hand contour features to solve the false recognition problem in some shape-based methods and effectively recognize the hand posture in cases when different hand postures have the same number of outstretched fingers. A multiclass support vector machine classifier is utilized to recognize the hand posture. Experiments were carried out on our data set to verify the feasibility of the proposed method. The results showed the effectiveness of the proposed approach compared with other methods.

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Hand Gesture Recognition Using Local Histogram Feature Descriptor

Cited by 15 publications

References 12 publications

SDViT: Stacking of Distilled Vision Transformers for Hand Gesture Recognition

SDViT: Stacking of Distilled Vision Transformers for Hand Gesture Recognition

Feature Extraction Technique for Vision-Based Indian Sign Language Recognition System: A Review

Approach to hand posture recognition based on hand shape features for human–robot interaction

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