CNNSFR: A Convolutional Neural Network System for Face Detection and Recognition

Deffo, Lionel Landry Sop; Fute, Elie Tagne; Tonyé, Emmanuel

doi:10.14569/ijacsa.2018.091235

Cited by 8 publications

(3 citation statements)

References 4 publications

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“…Concatenated Rectifier Linear Unit (CReLU) activation function has been shown to further improve classification capability after introducing a margin layer [ 39 , 40 ]. Thus, four situations were encountered: (1) training a softmax layer under standard conditions and parameters; (2) training with the addition of a new layer type, the margin layer.…”

Section: Implementation and Resultsmentioning

confidence: 99%

RoFace: A robust face representation approach for accurate classification

Fute

Deffo

Tonyé

2023

Heliyon

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Section: Implementation and Resultsmentioning

confidence: 99%

RoFace: A robust face representation approach for accurate classification

Fute

Deffo

Tonyé

2023

Heliyon

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“…VGG16 is CNN architecture, which was used to win the ImageNet large scale visual recognition challenge (ILSVRC) in 2014. 20 The promising and promoting result of the deep learning model is that it can detect COVID-19 from radiographic images. Therefore, it has a more significant role in combating the current pandemic.…”

Section: Resultsmentioning

confidence: 99%

COVID-19 Severity based on Deep Convolutional Neural Networks Chest X-Ray Image in Aceh, Indonesia

Yanti

Nurdin

Geumpana

2023

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Introduction: Every area of our lives has been devastated by the worldwide Coronavirus disease 2019 (COVID-19) epidemic. However, the development of artificial intelligence has made it possible to build advanced applications that can fulfill this level of clinical accuracy. This study aimed to create a deep learning model that can detect COVID-19 from a chest image dataset of confirmed patients treated at the provincial hospital in Aceh. Methods: Eight hundred confirmed COVID-19 patients' chest X-ray photos were gathered locally from Dr. Zainoel Abidin General Hospital, Banda Aceh. Performance was evaluated in several ways. First, the dataset was used for training and testing. Second, the data was used to train and test the model. VGG16 is a robust network adapted to an enhanced dataset constructed from a confirmed COVID-19 chest X-ray pool. To artificially produce a huge number of chest X-ray pictures, this study used data augmentation techniques such as random rotation at an angle between 10 and 10°, random noise, and horizontal flips. Results: The experimental results were encouraging: the proposed models classified chest X-ray pictures as normal or COVID-19 with an accuracy of 97.20% for Resnet50, 98.10% for InceptionV3, and 98.30% for VGG16. The results showed the outstanding performance of straightforward COVID-19 diagnosis with the classification of COVID-19 severity, such as mild, severe, and very severe. Conclusion: These made it possible to automate the X-ray image interpretation process accurately and could also be applied when materials and reverse transcription polymerase chain reaction (RT-PCR) tests are scarce.

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“…Human face is a main biological feature that distinguishes each person. Compared with biometrics such as fingerprints and iris, collection methods and equipment of human face are relatively simple, and no human behavior is required [2]. Some information characteristics, such as gender, age, skin colour, and emotions, can be collected by observing the human face, so it has gradually become an important research object in the field of vision [3].…”

Section: Introductionmentioning

confidence: 99%

Full-convolution Siamese network algorithm under deep learning used in tracking of facial video image in newborns

Wang¹,

Yee

2022

J Supercomput

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This study was carried out with the aim of exploring the full-convolution Siamese network (SiamFC) in the application of neonatal facial video image tracking, achieving accurate recognition of neonatal pain and helping doctors evaluate neonatal emotions in an automatic manner. The current technology shows low accuracy on facial image recognition of newborns, so the SiamFC algorithm under the deep learning was optimized in this study. Besides, a newborn facial video image tracking model (FVIT model) was constructed based on the SiamFC algorithm in combination with the attention mechanism with face tracking algorithm, and the facial features of newborns were tracked and recognized. In addition, a newborn face database was constructed based on the adult face database to evaluate performance of the FVIT model. It was found that the accuracy of the improved algorithm is 0.889, higher by 0.036 in contrast to other models; the area under the curve (AUC) of success rate reaches 0.748, higher by 0.075 compared with other algorithms. What's more, the improved algorithm shows good performance in tracking the facial occlusion, facial expression changes, and scale conversion of newborns. Therefore, the improved algorithm shows higher accuracy and success rate and has good effect in capturing and tracking the facial images of newborns, thereby providing an experimental basis for facial recognition and pain assessment of newborns in the later stage.

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CNNSFR: A Convolutional Neural Network System for Face Detection and Recognition

Cited by 8 publications

References 4 publications

RoFace: A robust face representation approach for accurate classification

RoFace: A robust face representation approach for accurate classification

COVID-19 Severity based on Deep Convolutional Neural Networks Chest X-Ray Image in Aceh, Indonesia

Full-convolution Siamese network algorithm under deep learning used in tracking of facial video image in newborns

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