Performance analysis of static hand gesture recognition approaches using artificial neural network, support vector machine and two stream based transfer learning approach

Patil, Anjali R.; Subbaraman, Shaila

doi:10.1007/s41870-021-00831-7

Cited by 15 publications

(3 citation statements)

References 30 publications

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“…These deep learning techniques play an important role in gesture recognition, improving the accuracy and robustness of gesture recognition [54]. With the continuous development of deep learning technology, we can also expect more innovative deep learning models to be applied to gesture recognition.…”

Section: Figure 3 the Working Steps Of Transfer Learningmentioning

confidence: 99%

Gesture Recognition Based on Deep Learning: A Review

2024

EAI Endorsed Trans e-Learn

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Gesture recognition is an important and inevitable technology in modern times, its appearance and improvement greatly improve the convenience of people's lives, but also enrich people's lives. It has a wide range of applications in various fields. In daily life, it can carry out human-computer interaction and the use of smart home. In terms of medical treatment, it can help patients to recover and assist doctors to carry out experiments. In terms of entertainment, it allows users to interact with the game in an immersive manner. This paper chooses three technologies that deep learning plays a more prominent role in gesture recognition, namely CNNs, LSTM and transfer learning based on deep learning. They each have their own advantages and disadvantages. Because of the different principles of use, different techniques have different roles, such as CNNs can carry out feature extraction, LSTM can deal with long time series, transfer learning can transfer what is learned from another task to this task. Select different practical technologies according to different application scenarios, and make improvements in real time in practical applications. Gesture recognition based on deep learning has the advantages of good accuracy, robustness and real-time implementation, but it also bears the disadvantages of huge economic and time costs and high hardware requirements. Despite some challenges, researchers continue to optimize and improve the technology, and believe that in the future, gesture recognition technology will be more mature and valuable.

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Section: Figure 3 the Working Steps Of Transfer Learningmentioning

confidence: 99%

Gesture Recognition Based on Deep Learning: A Review

2024

EAI Endorsed Trans e-Learn

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“…Layers are trained in stages by unfreezing layers one at a time because of data scarcity in SLR. Followed by this approach another method proposed by Sarhan et al 168 for static sign recognition using two stream transfer learning approach which provided the recognition accuracy by 99.6%. Though various other techniques transfer learning models were implemented and achieved state‐of‐art results in SLR, there still exists certain limitations in locating the hand gestures on basis of diverse signing styles, motion blurring in dynamic sign videos, finger occlusions.…”

Section: Deep Learning Approaches In Slrmentioning

confidence: 99%

A review on computational methods based automated sign language recognition system for hearing and speech impaired community

Robert

Hemanth

2023

Concurrency and Computation

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Summary The recent advancements in computer vision and deep learning have led to promising progress in various motion detection and gesture recognition methods. Thriving efforts in the field of sign language recognition (SLR) during recent years led to interaction between humans and computer systems. Contributing a real‐time automated Sign Language recognition system will be a remarkable treasure for hearing and speech‐impaired people that will break the barriers of interaction with the real world. Albeit several research works are accomplished in sign language recognition, there is still a demand for developing a real‐time automated sign language recognition system. Compared to other methodologies, the techniques adopted may have advantages and disadvantages, and they vary from researcher to researcher. There are still some issues with employing these SLR models and procedures regularly, even though numerous research studies have been conducted to determine the most acceptable methods and models for sign language recognition. It gets more expensive and difficult in terms of resources when the developed automated SLR system becomes available as a product. Hence, for the welfare of the hearing and speech‐impaired community, the researchers are still endeavoring way to find a cost‐effective method. This work brings forth the challenges faced by scientists in developing a cost‐effective commercial prototype for the hearing and speech‐impaired community. This paper also explores and analyses various deep learning techniques and methods used in developing a sign language recognition system. The objective behind this work is to identify the best method which produces high accuracy in developing a cost‐effective sign language recognition system aiding communication between signers and non‐signers so that they can be a part of growing technology.

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“…Accuracy requires near-true positive and false negative rates in a dataset. Thus, while assessing your model's efficacy, you must consider more factors [39].…”

Section: Performance Parametersmentioning

confidence: 99%

Experimental Analysis of Heart Disease Prediction Using Machine Learning with Emphasis on Hyper Parameter Tuning and Recursive Feature Elimination

2023

IJIES

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Cardiovascular disease is any illness that makes the heart work less well. Researchers are working on making smart systems that can correctly detect heart conditions from electronic health data using machine learning algorithms. This is because heart conditions can be very serious. In this study, data from patients and key clinical factors are used to identify cardiovascular disease using machine learning. The main goal of the suggested model is to improve the accuracy and reliability of predicting cardiac disease by focusing on parameter tuning, ensemble methods, and recursive feature removal approaches. Our methods for making predictions included logistic regression, decision trees, K-nearest neighbour (KNN), support vector machine (SVM), naive bayes (NB) machine learning (ML) approaches, ensemble technique approaches, and artificial neural networks (ANN) with stress on regularisation. Compared to the other ways, it was found that using a KNN model gave the most accurate results for the model. A number of factors, such as accuracy, precision, memory, and F1-score, were used to judge the models. The KNN model is the most accurate, at 97.8%.

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Performance analysis of static hand gesture recognition approaches using artificial neural network, support vector machine and two stream based transfer learning approach

Cited by 15 publications

References 30 publications

Gesture Recognition Based on Deep Learning: A Review

Gesture Recognition Based on Deep Learning: A Review

A review on computational methods based automated sign language recognition system for hearing and speech impaired community

Experimental Analysis of Heart Disease Prediction Using Machine Learning with Emphasis on Hyper Parameter Tuning and Recursive Feature Elimination

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