Illumination Quality Assessment for Face Images: A Benchmark and a Convolutional Neural Networks Based Model

2017 ◽  
pp. 583-593 ◽  
Author(s):  
Lijun Zhang ◽  
Lin Zhang ◽  
Lida Li
Keyword(s):  
Neural Networks ◽  
Quality Assessment ◽  
Convolutional Neural Networks ◽  
Face Images

scholarly journals Age estimation based on face images and pre-trained convolutional neural networks

2017 ◽  
Author(s):  
Abhinav Anand ◽  
Ruggero Donida Labati ◽  
Angelo Genovese ◽  
Enrique Munoz ◽  
Vincenzo Piuri ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Age Estimation ◽  
Face Images

Automatic quality assessment of apical four-chamber echocardiograms using deep convolutional neural networks

2017 ◽  
Author(s):  
Amir H. Abdi ◽  
Christina Luong ◽  
Teresa Tsang ◽  
Gregory Allan ◽  
Saman Nouranian ◽  
...  
Keyword(s):  
Neural Networks ◽  
Quality Assessment ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks

scholarly journals Avaliação de dor em expressão facial neonatal por meio de redes neurais profundas

2020 ◽  
Author(s):  
◽  
L. F. Buzuti
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Pain Assessment ◽  
Neural Models ◽  
External Variable ◽  
Neonatal Pain ◽  
Face Images ◽  
Average Accuracy ◽  
Late Intervention ◽  
Quantitative Results

Neonatal pain assessment might suffer variation among health professionals, leading to late intervention and flimsy treatment of pain in several occasions. Therefore, it is essential to develop computational tools of pain assessment, less subjective and susceptible to external variable influences. Deep learning models, especially Convolutional Neural Networks, have gained ground in the last decade, due to many successful applications in image analysis, object recognitions and human emotion recognitions. In this context, the general aim this dissertation was analyse quantitatively and qualitatively models of Convolutional Neural Networks in the task neonatal pain classification through a computacional framework based in face images of two distinct databases (an international, named COPE, and other national, named UNIFESP). How specific aims were implemented, evaluated and compared the performance of three existent models used in literature: Neonatal Convolutional Neural Network (N-CNN) and two type of ResNet50 models. The quantitative results showed the excellence of N-CNN to neonatal pain assessment automatic, with average accuracy of 87.2% and 78.7% for the databases COPE and UNIFESP, respectively. However, the quantitative analysis showed that all neural models evaluated, including N-CNN models, can learn artifacts from the imagens and not variation discriminating in faces, thus showed the necessity more studies to apply this models in clinical practice

Sign in / Sign up

Export Citation Format

Share Document