Dense Feature Based Face Recognition from Surveillance Video using Convolutional Neural Network

Suma, K.

doi:10.17762/turcomat.v12i5.2040

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…Hand gestures are now being developed and used, which will have repercussions for the management, observation, and comprehension of process. In this article, we apply the models by making use of a number of different convolutional neural networks [12]. The dataset that is used by this technique provides information regarding the development of a number of different convolutional designs.…”

Section: Literature Surveymentioning

confidence: 99%

Deep Convolutional Neural Network Recognition of Hand Gestures

Kumar¹,

Rajitha²,

G.Lohitha³

2023

IJSMIEN

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Hand gestures are becoming more common in HCI. No matter the project, the majority of the task will involve interaction with other parts of the body, the environment, etc. We demonstrate how to locate a user’s hand and movements, apply this information in a home situation, and interact with the system in this work. Using this information is also discussed. Sign language communicates visually using hand motions. Hand gestures may help hearing-impaired people communicate. Processing hand gestures lets people use their hands to communicate. Words can be made by combining signs and digits on the palms of one’s hands. Sightbased and glove-based hand motions are used nowadays. In this paper, we introduce deep convolutional neural networks for image classification and demonstrate their utility in hand gesture classification. In particular, we examine how these networks can recognise hand motions. Deep convolutional hand gesture can uncover many hand motions. Hand gestures can express many emotions. Time is needed to comprehend the hand motions Using a convolutional neural network to separate items or photographs saves time and effort. Hand actions demand more time to assess. In this case, the deep convolutional neural network speeds up the procedure. The system’s precision, sensitivity, and accuracy will improve with deep CNN. CNN, which divides the dataset into chunks, can be utilised to get high accuracy, specificity, and sensitivity from the dataset. Once the camera transmits the hand gesture image, the system will stop.

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Section: Literature Surveymentioning

confidence: 99%

Deep Convolutional Neural Network Recognition of Hand Gestures

Kumar¹,

Rajitha²,

G.Lohitha³

2023

IJSMIEN

View full text Add to dashboard Cite

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[Retracted] Empirical Analysis of Financial Depth and Width Based on Convolutional Neural Network

Zhang

2021

Computational Intelligence and Neuroscience

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There are great differences in financial and economic development in different regions. In different time series and different regions, the effects of financial depth and width on economic development are also different. This paper selects neural network to establish the economic benefit model of financial depth and breadth, which can deeply explore the relationship between financial data and economic data. In order to determine the optimal convolutional neural network parameters, the optimal convolutional neural network parameters are determined through comparative simulation analysis. The convolutional neural network model based on the optimal parameters is applied to the empirical analysis of the effect of financial and economic development in X region. In order to obtain the optimal convolutional neural network parameters, different convolution layers, convolution core size, and convolution core number are compared and simulated. The convolutional neural network model with optimal parameters is used to simulate the financial and economic data of X region. The simulation results show that the density of financial personnel has a certain impact on economic development, so it is necessary to improve the comprehensive quality of financial personnel and promote regional economic development. Therefore, this paper seeks an effective method to study the effect of financial breadth and depth on economic development which can provide a feasible idea for the in-depth research method of financial and economic development.

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Dense Feature Based Face Recognition from Surveillance Video using Convolutional Neural Network

Cited by 2 publications

References 10 publications

Deep Convolutional Neural Network Recognition of Hand Gestures

Deep Convolutional Neural Network Recognition of Hand Gestures

[Retracted] Empirical Analysis of Financial Depth and Width Based on Convolutional Neural Network

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