Masked face detection via a modified LeNet

Lin, Shaohui; Cai, Ling; Lin, Xianming; Ji, Rongrong

doi:10.1016/j.neucom.2016.08.056

Cited by 54 publications

(28 citation statements)

References 17 publications

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“…LeNet-5 was applied as the CNN algorithm for classifying the pressure distribution data according to the sitting posture. LeNet-5 is the CNN architecture generally used for handwritten digit recognition and was successfully used to solve other visual related problems [48,49]. LeNet-5 comprises seven layers that do not include the input.…”

Section: Sitting Posture Classification Model: Lenet-5mentioning

confidence: 99%

Classification of Children’s Sitting Postures Using Machine Learning Algorithms

et al. 2018

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Sitting on a chair in an awkward posture or sitting for a long period of time is a risk factor for musculoskeletal disorders. A postural habit that has been formed cannot be changed easily. It is important to form a proper postural habit from childhood as the lumbar disease during childhood caused by their improper posture is most likely to recur. Thus, there is a need for a monitoring system that classifies children's sitting postures. The purpose of this paper is to develop a system for classifying sitting postures for children using machine learning algorithms. The convolutional neural network (CNN) algorithm was used in addition to the conventional algorithms: Naïve Bayes classifier (NB), decision tree (DT), neural network (NN), multinomial logistic regression (MLR), and support vector machine (SVM). To collect data for classifying sitting postures, a sensing cushion was developed by mounting a pressure sensor mat (8 × 8) inside children's chair seat cushion. Ten children participated, and sensor data was collected by taking a static posture for the five prescribed postures. The accuracy of CNN was found to be the highest as compared with those of the other algorithms. It is expected that the comprehensive posture monitoring system would be established through future research on enhancing the classification algorithm and providing an effective feedback system.

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Section: Sitting Posture Classification Model: Lenet-5mentioning

confidence: 99%

Classification of Children’s Sitting Postures Using Machine Learning Algorithms

et al. 2018

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“…In addition, the system model of this paper is provided. As it is shown in Figure 1, the LeNet network contains three convolutional layers with the size 5 × 5, two pooling layers with the size 2 × 2, and three fully connected layers [18]. The AlexNet network contains five convolutional layers, three pooling layers with the size 3 × 3, and three fully connected layers [19].…”

Section: Related Workmentioning

confidence: 99%

“…After that, the LeNet [18], AlexNet [19], VGG-16 [20], and a novel network are trained based on the extended data. Finally, the simulations are made to validate the effectiveness of the proposed schemes.…”

Section: Introductionmentioning

confidence: 99%

CNN and DCGAN for Spectrum Sensors over Rayleigh Fading Channel

Tan

Xie

et al. 2021

Wireless Communications and Mobile Computing

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Spectrum sensing (SS) has attracted much attention in the field of Internet of things (IoT) due to its capacity of discovering the available spectrum holes and improving the spectrum efficiency. However, the limited sensing time leads to insufficient sampling data due to the tradeoff between sensing time and communication time. In this paper, deep learning (DL) is applied to SS to achieve a better balance between sensing performance and sensing complexity. More specifically, the two-dimensional dataset of the received signal is established under the various signal-to-noise ratio (SNR) conditions firstly. Then, an improved deep convolutional generative adversarial network (DCGAN) is proposed to expand the training set so as to address the issue of data shortage. Moreover, the LeNet, AlexNet, VGG-16, and the proposed CNN-1 network are trained on the expanded dataset. Finally, the false alarm probability and detection probability are obtained under the various SNR scenarios to validate the effectiveness of the proposed schemes. Simulation results state that the sensing accuracy of the proposed scheme is greatly improved.

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“…Con la aparición del Aprendizaje Profundo (Deep Learning) se han comenzado a utilizar Redes Neuronales Convolucionales a la detección de rostros, con muy buenos resultados [3,13,14].…”

Section: Estado Del Arteunclassified

Comparación de métodos de detección de rostros en imágenes digitales

Prado¹,

Castro²,

Gutiérrez³

et al. 2020

XXXVIII Jornadas De Automática: Gijón, 6, 7, Y 8 De Septiembre De 2017

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En este trabajo se realiza la evaluación de tres métodos de detección de rostros con cuatro conjuntos de imágenes utilizados en la literatura relacionada con este problema. Los métodos evaluados son el método de detección de objetos de Viola & Jones, un método basado en una modificación de HOG implementado en la librería DLib, y un método basado en Redes Neuronales Convolucionales llamado Multi-task Cascaded Convolutional Neural Networks (MTCNN). Los resultados obtenidos con los conjuntos de datos evaluados indican que el método que mejores resultados globales ha obtenido ha sido MTCNN, tanto en términos de FScore como en recall, i.e. tasa de verdaderos positivos.

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Masked face detection via a modified LeNet

Cited by 54 publications

References 17 publications

Classification of Children’s Sitting Postures Using Machine Learning Algorithms

Classification of Children’s Sitting Postures Using Machine Learning Algorithms

CNN and DCGAN for Spectrum Sensors over Rayleigh Fading Channel

Comparación de métodos de detección de rostros en imágenes digitales

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