Facial Recognition using Convolutional Neural Networks and Implementation on Smart Glasses

Khan, Sameer; Javed, Mahed; Ahmed, Ehtasham; Shah, Syed Atif Ali; Ali, Syed Umaid

doi:10.1109/cisct.2019.8777442

Cited by 57 publications

(28 citation statements)

References 12 publications

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“…Khan et al [ 9 ] stated that facial recognition is important for biometric authentication, which is utilized in a variety of applications, including security. To accomplish this goal, image processing techniques are used to change a stored database of the individuals.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Modification in traditional architecture of convolutional neural network leads to better solutions of several problems that could not be catered by existing traditional models. Some of the architectures that are designed using traditional CNN models are LeNet, AlexNet, and GoogleNet [ 9 ]. These architectures are the result of modifications done in the traditional model of CNN by increasing or decreasing the number of layers used in convolutional neural networks.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhancement of Patient Facial Recognition through Deep Learning Algorithm: ConvNet

Onyema

Shukla

Dalal

et al. 2021

Journal of Healthcare Engineering

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The use of machine learning algorithms for facial expression recognition and patient monitoring is a growing area of research interest. In this study, we present a technique for facial expression recognition based on deep learning algorithm: convolutional neural network (ConvNet). Data were collected from the FER2013 dataset that contains samples of seven universal facial expressions for training. The results show that the presented technique improves facial expression recognition accuracy without encoding several layers of CNN that lead to a computationally costly model. This study proffers solutions to the issues of high computational cost due to the implementation of facial expression recognition by providing a model close to the accuracy of the state-of-the-art model. The study concludes that deep l\earning-enabled facial expression recognition techniques enhance accuracy, better facial recognition, and interpretation of facial expressions and features that promote efficiency and prediction in the health sector.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhancement of Patient Facial Recognition through Deep Learning Algorithm: ConvNet

Onyema

Shukla

Dalal

et al. 2021

Journal of Healthcare Engineering

View full text Add to dashboard Cite

show abstract

“…The features in greyscale are applied one by one and compared with pixel values. The CNN models give high accuracy than past techniques by overcoming the problems, like light intensity and expressions, with the help of trained models using more training samples [31][32][33].…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Data Processing Using Artificial Neural Networks

Alaloul¹,

Qureshi²

2020

Dynamic Data Assimilation - Beating the Uncertainties

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The artificial neural network (ANN) is a machine learning (ML) methodology that evolved and developed from the scheme of imitating the human brain. Artificial intelligence (AI) pyramid illustrates the evolution of ML approach to ANN and leading to deep learning (DL). Nowadays, researchers are very much attracted to DL processes due to its ability to overcome the selectivity-invariance problem. In this chapter, ANN has been explained by discussing the network topology and development parameters (number of nodes, number of hidden layers, learning rules and activated function). The basic concept of node and neutron has been explained, with the help of diagrams, leading to the ANN model and its operation. All the topics have been discussed in such a scheme to give the reader the basic concept and clarity in a sequential way from ANN perceptron model to deep learning models and underlying types.

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“…Image classification is an image processing method that separates categories of objects according to the image information. Common image classification methods include K – NN, 17 SVM, 18 BPNN, 19 CNN 20 and migration learning. 21…”

Section: Image Recognition and Classification Based On Sphere Descripmentioning

confidence: 99%

Spherical object recognition based on the number of contour edges extracted by fitting and convex hull processing

Zou

Qiu

2020

Proceedings of the Institution of Mechanical Engineers, Part D:

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The detection of spherical targets in workpiece shape clustering and fruit classification tasks is challenging. Spherical targets produce low detection accuracy in complex fields, and single-feature processing cannot accurately recognize spheres. Therefore, a novel spherical descriptor (SD) for contour fitting and convex hull processing is proposed. The SD achieves image de-noising by combining flooding processing and morphological operations. The number of polygon-fitted edges is obtained by convex hull processing based on contour extraction and fitting, and two RGB images of the same group of objects are obtained from different directions. The two fitted edges of the same target object obtained at two RGB images are extracted to form a two-dimensional array. The target object is defined as a sphere if the two values of the array are greater than a custom threshold. The first classification result is obtained by an improved K-NN algorithm. Circle detection is then performed on the results using improved Hough circle detection. We abbreviate it as a new Hough transform sphere descriptor (HSD). Experiments demonstrate that recognition of spherical objects is obtained with 98.8% accuracy. Therefore, experimental results show that our method is compared with other latest methods, HSD has higher identification accuracy than other methods.

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Facial Recognition using Convolutional Neural Networks and Implementation on Smart Glasses

Cited by 57 publications

References 12 publications

Enhancement of Patient Facial Recognition through Deep Learning Algorithm: ConvNet

Enhancement of Patient Facial Recognition through Deep Learning Algorithm: ConvNet

Data Processing Using Artificial Neural Networks

Spherical object recognition based on the number of contour edges extracted by fitting and convex hull processing

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