MRCS: multi-radii circular signature based feature descriptor for hand gesture recognition

Sahana, Taniya; Basu, Subhadip; Nasipuri, Mita; Mollah, Ayatullah Faruk

doi:10.1007/s11042-021-11743-w

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…Conversely, dynamic gesture detection needs to consider both the temporal and spatial features of the gesture since it varies over period. Hence, dynamic gesture detection is highly sophisticated compared to static gesture detection; however, the utilization of dynamic gestures is broader (Sahana et al, 2022). This study offers a lightweight gesture action detection network for real-time HCI and control.…”

Section: Introductionmentioning

confidence: 99%

Automated Gesture Recognition Using African Vulture Optimization with Deep Learning for Visually Impaired People on Sensory Modality Data

Maashi¹,

Al-Hagery²,

Rizwanullah³

et al. 2023

Journal of Disability Research

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Gesture recognition for visually impaired persons (VIPs) is a useful technology for enhancing their communications and increasing accessibility. It is vital to understand the specific needs and challenges faced by VIPs when planning a gesture recognition model. But, typical gesture recognition methods frequently depend on the visual input (for instance, cameras); it can be vital to discover other sensory modalities for input. The deep learning (DL)-based gesture recognition method is effective for the interaction of VIPs with their devices. It offers a further intuitive and natural way of relating with technology, creating it more available for everybody. Therefore, this study presents an African Vulture Optimization with Deep Learning-based Gesture Recognition for Visually Impaired People on Sensory Modality Data (AVODL-GRSMD) technique. The AVODL-GRSMD technique mainly focuses on the utilization of the DL model with hyperparameter tuning strategy for a productive and accurate gesture detection and classification process. The AVODL-GRSMD technique utilizes the primary data preprocessing stage to normalize the input sensor data. The AVODL-GRSMD technique uses a multi-head attention-based bidirectional gated recurrent unit (MHA-BGRU) method for accurate gesture recognition. Finally, the hyperparameter optimization of the MHA-BGRU method can be performed by the use of African Vulture Optimization with Deep Learning (AVO) approach. A series of simulation analyses were performed to demonstrate the superior performance of the AVODL-GRSMD technique. The experimental values demonstrate the better recognition rate of the AVODL-GRSMD technique compared to that of the state-of-the-art models.

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Section: Introductionmentioning

confidence: 99%

Automated Gesture Recognition Using African Vulture Optimization with Deep Learning for Visually Impaired People on Sensory Modality Data

Maashi¹,

Al-Hagery²,

Rizwanullah³

et al. 2023

Journal of Disability Research

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“…A Convolution Neural Network + Recurrent Neural Network approach is proposed in [7] to extract spatial-temporal information to recognize dynamic hand gestures using depth and body joint information. Newer learning techniques involving an ensemble of different models [8], Temporal Convolution Networks [9], [10], multi-radii circular signature [11], 3-D CNNs [12], feature fusion RCNN [13], and elastic semantic network [14] have been proposed for gesture/action recognition.…”

Section: Introductionmentioning

confidence: 99%

Hand Gesture Classification on Praxis Dataset: Trading Accuracy for Expense

Islam

Lai

Yanushkevich

2022

2022 International Joint Conference on Neural Networks (IJCNN)

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In this paper, we investigate hand gesture classifiers that rely upon the abstracted 'skeletal' data recorded using the RGB-Depth sensor. We focus on 'skeletal' data represented by the body joint coordinates, from the Praxis dataset. The PRAXIS dataset contains recordings of patients with cortical pathologies such as Alzheimer's disease, performing a Praxis test under the direction of a clinician. In this paper, we propose hand gesture classifiers that are more effective with the PRAXIS dataset than previously proposed models. Body joint data offers a compressed form of data that can be analyzed specifically for hand gesture recognition. Using a combination of windowing techniques with deep learning architecture such as a Recurrent Neural Network (RNN), we achieved an overall accuracy of 70.8% using only body joint data. In addition, we investigated a long-short-term-memory (LSTM) to extract and analyze the movement of the joints through time to recognize the hand gestures being performed and achieved a gesture recognition rate of 74.3% and 67.3% for static and dynamic gestures, respectively. The proposed approach contributed to the task of developing an automated, accurate, and inexpensive approach to diagnosing cortical pathologies for multiple healthcare applications.

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An Effective Pipeline for Depth Image-Based Hand Gesture Recognition

Sahana,

Mollah

2023

Lecture Notes in Networks and Systems

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MRCS: multi-radii circular signature based feature descriptor for hand gesture recognition

Cited by 7 publications

References 19 publications

Automated Gesture Recognition Using African Vulture Optimization with Deep Learning for Visually Impaired People on Sensory Modality Data

Automated Gesture Recognition Using African Vulture Optimization with Deep Learning for Visually Impaired People on Sensory Modality Data

Hand Gesture Classification on Praxis Dataset: Trading Accuracy for Expense

An Effective Pipeline for Depth Image-Based Hand Gesture Recognition

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