Hand Gesture Recognition System Based on LBP and SVM for Mobile Devices

Lahiani, Houssem; Néji, Mahmoud

doi:10.1007/978-3-030-28377-3_23

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…Tables 1-4 present the results of recognition acquired by our system and sate of the art systems. Table 1 shows that the system created in [32] achieved the best recognition rate. Both SVM and Local Binary Pattern (LBP) features were used by this system.…”

Section: System Assessmentmentioning

confidence: 99%

See 1 more Smart Citation

Design of a Hand Pose Recognition System for Mobile and Embedded Devices

Lahiani¹,

Néji²

2022

Int. J. Recent Contrib. Eng. Sci. IT

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Today, smart devices such smart watches and smart cell phones are becoming ever-present in all fields that influence the quality of life of the modern people. These on-board systems have revolutionized the behavior of human beings and especially their way of communicating. In this context and to improve the experience of using these devices, we aim to develop a system that recognizes hand poses in the air by a smart device. In this work, the system is based on Histogram of Oriented Gradient (HOG) features and Support Vector Machine (SVM) classifier. The impact of using HOG and SVM on mobile devices is studied. To carry out this study, we used an improved version of the "NUS I" dataset and obtained a recognition rate of approximately 94%. In addition, we conducted run speed experiments on various mobile devices to study the impact of this task on this embedded platform. The main contribution of this work is to test the impact of using the HOG descriptor and the SVM classifier in terms of recognition rate and execution time on low-end smartphones.Today, smart devices such smart watches and smart cell phones are becoming ever-present in all fields that influence the quality of life of the modern people. These on-board systems have revolutionized the behavior of human beings and especially their way of communicating. In this context and to improve the experience of using these devices, we aim to develop a system that recognizes hand poses in the air by a smart device. In this work, the system is based on Histogram of Oriented Gradient (HOG) features and Support Vector Machine (SVM) classifier. The impact of using HOG and SVM on mobile devices is studied. To carry out this study, we used an improved version of the "NUS I" dataset and obtained a recognition rate of approximately 94%. In addition, we conducted run speed experiments on various mobile devices to study the impact of this task on this embedded platform. The main contribution of this work is to test the impact of using the HOG descriptor and the SVM classifier in terms of recognition rate and execution time on low-end smartphones.

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Section: System Assessmentmentioning

confidence: 99%

“…These photos, which include hand gestures, were collected from the NUS dataset-II. In addition to the outcomes produced by the HOG-SVM system, Tables 3 and 4 also shows our Pr metrics results and the results obtained by the systems created in [32,34] for comparison.…”

Section: System Assessmentmentioning

confidence: 99%

Design of a Hand Pose Recognition System for Mobile and Embedded Devices

Lahiani¹,

Néji²

2022

Int. J. Recent Contrib. Eng. Sci. IT

View full text Add to dashboard Cite

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Comparative Analysis of Hand Gesture Recognition Techniques: A Review

Shah

et al. 2020

Algorithms for Intelligent Systems

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Improved 3D-ResNet sign language recognition algorithm with enhanced hand features

Wang

Yang

et al. 2022

Sci Rep

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In sign language video, the hand region is small, the resolution is low, the motion speed is fast, and there are cross occlusion and blur phenomena, which have a great impact on sign language recognition rate and speed, and are important factors restricting sign language recognition performance. To solve these problems, this paper proposes an improved 3D-ResNet sign language recognition algorithm with enhanced hand features, aiming to highlight the features of both hands, solve the problem of missing more effective information when relying only on global features, and improve the accuracy of sign language recognition. The proposed method has two improvements. Firstly, the algorithm detects the left and right hand regions based on the improved EfficientDet network, uses the improved Bi-FPN module and dual channel and spatial attention module are used to enhance the detection ability of the network for small targets like hand. Secondly, the improved residual module is used to improve the 3D-ResNet18 network to extract sign language features. The global, the left-hand and the right-hand image sequences are divided into three branches for feature extraction and fusion, so as to strengthen the attention to hand features, strengthen the representation ability of sign language features, and achieve the purpose of improving the accuracy of sign language recognition. In order to verify the performance of this algorithm, a series of experiments are carried out on CSL dataset. For example, in the experiments of hand detection algorithm and sign language recognition algorithm, the performance indicators such as Top-N, mAP, FLOPs and Parm are applied to find the optimal algorithm framework. The experimental results show that the Top1 recognition accuracy of this algorithm reaches 91.12%, which is more than 10% higher than that of C3D, P3D and 3D-ResNet basic networks. From the performance indicators of Top-N, mAP, FLOPs, Parm and so on, the performance of the algorithm in this paper is better than several algorithms in recent three years, such as I3D+BLSTM, B3D ResNet, AM-ResC3D+RCNN and so on. The results show that the hand detection network with enhanced hand features and three-dimensional convolutional neural network proposed in this paper can achieve higher accuracy of sign language recognition.

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Hand Gesture Recognition System Based on LBP and SVM for Mobile Devices

Cited by 6 publications

References 14 publications

Design of a Hand Pose Recognition System for Mobile and Embedded Devices

Design of a Hand Pose Recognition System for Mobile and Embedded Devices

Comparative Analysis of Hand Gesture Recognition Techniques: A Review

Improved 3D-ResNet sign language recognition algorithm with enhanced hand features

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