Smart Gesture-based Control in Human Computer Interaction Applications for Special-need People

Rady, Mohamed; Youssef, Sherin M.; Fayed, Salema F.

doi:10.1109/niles.2019.8909324

Cited by 10 publications

(8 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 4 lists the average classification accuracy among the five subjects obtained from LOOCV evaluation. Some state-of-the-art methods use handcrafted features based on prior knowledge [2-7, 9, 11, 14, 16, 19, 22, 24] while others automatically learn discriminative descriptors [21,27,28,32,35,36]. The proposed DDaNet outperforms the other methods in terms of accuracy (93.53%), demonstrating the benefits of learning discriminative features related to letter signs through a deep neural network with an attention module.…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 93%

“…Deep learning techniques handle this limitation by automatically learning discriminative features from a large dataset containing scene variations. Table 1 shows that most studies have introduced convolutional neural networks (CNNs) to jointly learn hand descriptors and letter sign classifiers [21,28,32,35,36]. In some studies, deep neural networks have been designed for specific purposes, such as feature extraction [27] and classification [7].…”

Section: B Network For Fingerspelling Recognitionmentioning

confidence: 99%

“…In some studies, deep neural networks have been designed for specific purposes, such as feature extraction [27] and classification [7]. In [28], Gabor texture features are extracted for hand detection, and feature representations are learned for fingerspelling recognition. As deep learning requires a large amount of training data, data augmentation may improve the generalization ability of the trained network [8].…”

Section: B Network For Fingerspelling Recognitionmentioning

confidence: 99%

“…Feature Classifier Year [3] Scale-invariant features SCF 2012 [5] SP-EMD TM 2014 [7] Depth features DBN 2014 [9] Point cloud HMM 2019 [2] Gabor filters RF 2011 [4] Shape function descriptor RF 2013 [14] Oriented radial distribution RF 2014 [8] Depth comparison feature RF 2015 [19] Random line segments RF 2017 [6] Hand dominant line SVM 2013 [11] Shape matrix SVM 2015 [22] Local binary patterns SVM 2015 [24] 3D local shape descriptor SVM 2015 [16] Contour fragments features SVM 2016 [27] PCANet SVM 2019 [29] FuseNet * 2016 [21] CNN * 2017 [32] ResNet * 2018 [36] CNN + multi-view * 2018 [37] Two-stream CNN * 2019 [38] RFBNet * 2019 [28] Gabor texture features + CNN* 2019 [35] Deep eigenspace * 2020 * Feature representations and classifiers jointly learned using deep learning.…”

Section: Studymentioning

confidence: 99%

See 3 more Smart Citations

DDaNet: Dual-Path Depth-Aware Attention Network for Fingerspelling Recognition Using RGB-D Images

et al. 2021

View full text Add to dashboard Cite

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 93%

Section: B Network For Fingerspelling Recognitionmentioning

confidence: 99%

Section: B Network For Fingerspelling Recognitionmentioning

confidence: 99%

Section: Studymentioning

confidence: 99%

See 2 more Smart Citations

DDaNet: Dual-Path Depth-Aware Attention Network for Fingerspelling Recognition Using RGB-D Images

et al. 2021

View full text Add to dashboard Cite

“…N S (Sreekanth& Narayanan, 2017) proposed convex hull algorithm for American Sign Language (ASL) with different digits from 0-9 and obtained an accuracy of 89% to 98%. (Rady et al, 2019) proposed enhanced automatic model for hand gesture recognition using CNN method. They used both depth and color information with Kinect sensor and applied to three different datasets and obtained an accuracy of 84.67%, 99.5% and 99.85%.…”

Section: E Robot Controlmentioning

confidence: 99%

A Smart Vision Based Single Handed Gesture Recognition system using deep neural networks

Suguna¹,

Rupavathy²,

Asmetha³

2021

Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bh

View full text Add to dashboard Cite

The primary and expressive mode of human communications are gestures. Human can interact with machines using body postures and finger pointing. Advancements in human-computer interaction (HCI) has presented new innovations in technology making the users to communicate with computers in an instinctual manner. Evidences clearly state that future living space will be dominated by sensor-based devices and hence an efficient human-computer interfaces are required to exchange information. Hand gesture interfaces have been employed in multiple domains and has won social acceptance. System requirements for gesture recognition vary with the intended application areas. Responsiveness, Learnability, Cost and Accuracy are major drivers for success of hand gesture recognition systems. This paper suggest a HCI design that requires no wearable markers or gloves. A noninvasive vision based framework has been suggested for human-machine interface. Deep Neural Networks have provided promising results in vision based tasks. Convolutional Neural Networks (CNN) are claimed for image recognition problems as they learn features from images gradually and automatically. An optimal CNN architecture has been proposed to recognize single handed gestures. The images of hand gestures convey a numerical representation of ten digits. Image augmentation has been performed to increase the size of training data for deep learning. Depending on application, the interpretation of gesture can be customized. The classification performance has been analyzed with metrics reported by confusion matrix. The proposed architecture performs well both in training and testing reporting the accuracy of 98.2% and 96.2% respectively. Tuning the hyper parameter has improved test accuracy.

show abstract

Vision‐Based Gesture Recognition: A Critical Review

Harish¹,

Praveen²,

Prasanth³

et al. 2021

Artificial Intelligence and Data Mining Approaches in Security Frameworks

View full text Add to dashboard Cite

Smart Gesture-based Control in Human Computer Interaction Applications for Special-need People

Cited by 10 publications

References 21 publications

DDaNet: Dual-Path Depth-Aware Attention Network for Fingerspelling Recognition Using RGB-D Images

DDaNet: Dual-Path Depth-Aware Attention Network for Fingerspelling Recognition Using RGB-D Images

A Smart Vision Based Single Handed Gesture Recognition system using deep neural networks

Vision‐Based Gesture Recognition: A Critical Review

Contact Info

Product

Resources

About