Rapid and efficient hand gestures recognizer based on classes discriminator wavelet networks

Bouchrika, Tahani; Jemai, Olfa; Zaied, Mourad; Amar, Chokri Ben

doi:10.1007/s11042-017-4510-7

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…Tahani Bouchrika et al [10] work output is based upon making amendments in the Wavelet Network classification phase of the algorithm by making separated Wavelet Networks discriminating classes (n − 1) with the purpose of training each image which results in less time required to complete the testing phase. The proposed Wavelet Network has new architecture composed of stages that learns quickly and recognizes actions by avoiding unnecessary hand movements.…”

Section: Literature Reviewmentioning

confidence: 99%

Mini gesture detection using neural networks algorithms

Alnaim

Abbod

2019

Eleventh International Conference on Machine Vision (ICMV 2018)

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Gesture recognition is defined as non-verbal motions used as a means of communication in Human Computer Interaction. It is one of the significant aspects of HCI, both in the device interfaces and interpersonally. In a virtual reality system, gestures can be used to navigate, control or interact with a computer. The aim of gesture recognition is to capture gestures that are formed in a certain way and are detected by a device such as a camera. Hand gesture recognition is one of the logical ways to generate a convenient and high adaptability interface between devices and users. In this paper, a system is created for hand gesture recognition using image processing tools, namely Wavelets Transform (WT), Empirical Mode Decomposition (EMD) methods, Artificial Neural Networks (ANN) and Convolutional Neural Network (CNN), for gesture classification. These methods are evaluated based on many factors such as execution time, accuracy, sensitivity, specificity, positive and negative predictive value, likelihood, receiver operating characteristic, area under roc curve and root mean square. Preliminary results indicate that WT had less execution time than EMD and CNN. CNN had the ability to extract distinct features and classify data accurately while EMD and WT were less effective. Hence, the classification accuracy is improved dramatically.

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Section: Literature Reviewmentioning

confidence: 99%

Mini gesture detection using neural networks algorithms

Alnaim

Abbod

2019

Eleventh International Conference on Machine Vision (ICMV 2018)

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“…The description of the two datasets used in this work is presented in this section. Dataset‐I (The Jochen Triesch static hand posture database) : The Jochen Triesch Static Hand posture Database 39,40 contains 10 hand signs in three backgrounds that are performed by 24 people. The 10 hand signs are shown in the following Figure 3.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

An efficient hand gesture recognition based on optimal deep embedded hybrid convolutional neural network‐long short term memory network model

Palanisamy¹,

Thangaswamy²

2022

Concurrency and Computation

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Hand gestures are the nonverbal communication done by individuals who cannot represent their thoughts in form of words. It is mainly used during human-computer interaction (HCI), deaf and mute people interaction, and other robotic interface applications. Gesture recognition is a field of computer science mainly focused on improving the HCI via touch screens, cameras, and kinetic devices. The state-of-art systems mainly used computer vision-based techniques that utilize both the motion sensor and camera to capture the hand gestures in real-time and interprets them via the usage of the machine learning algorithms. Conventional machine learning algorithms often suffer from the different complexities present in the visible hand gesture images such as skin color, distance, light, hand direction, position, and background. In this article, an adaptive weighted multi-scale resolution (AWMSR) network with a deep embedded hybrid convolutional neural network and long short term memory network (hybrid CNN-LSTM) is proposed for identifying the different hand gesture signs with higher recognition accuracy. The proposed methodology is formulated using three steps: input preprocessing, feature extraction, and classification. To improve the complex visual effects present in the input images, a histogram equalization technique is used which improves the size of the gray level pixel in the image and also their occurrence probability. The multi-block local binary pattern (MB-LBP) algorithm is employed for feature extraction which extracts the crucial features present in the image such as hand shape structure feature, curvature feature, and invariant movements. The AWMSR with the deep embedded hybrid CNN-LSTM network is applied in the two-benchmark datasets namely Jochen Triesch static hand posture and NUS hand posture dataset-II to detect its stability in identifying different hand gestures. The weight function of the deep embedded CNN-LSTM architecture is optimized using the puzzle optimization algorithm. The efficiency of the proposed methodology is verified in terms of different performance evaluation metrics such as accuracy, loss, confusion matrix, Intersection over the union, and execution time. The proposed methodology offers recognition accuracy of 97.86% and 98.32% for both datasets.

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“…WNC was used for its effective classification results. An approach also proposed by Bouchrika et al [12] made amendments to the Wavelet Network classification phase by making separated Wavelet Networks discriminating classes (n − 1) with the purpose of training each image. This resulted in less time required to complete the testing phase.…”

Section: Literature Reviewmentioning

confidence: 99%