Determination of human skin optical properties from hyper spectral data with deep-learning neural networks (Conference Presentation)

2018 ◽  
Author(s):  
Alexander Doronin ◽  
Igor Meglinski ◽  
Alexander V. Bykov ◽  
Holly Rushmeier
Keyword(s):  
Neural Networks ◽  
Optical Properties ◽  
Deep Learning ◽  
Spectral Data ◽  
Human Skin ◽  
Hyper Spectral

Human Skin Detection in Color Images Using Deep Learning

2015 ◽  
Vol 5 (2) ◽  
pp. 1-13 ◽  
Author(s):  
Mohammadreza Hajiarbabi ◽  
Arvin Agah
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Human Skin ◽  
Skin Color ◽  
Color Image ◽  
Gaussian Model ◽  
Color Images ◽  
Skin Detection ◽  
Learning Methods ◽  
Rule Based

Human skin detection is an important and challenging problem in computer vision. Skin detection can be used as the first phase in face detection when using color images. The differences in illumination and ranges of skin colors have made skin detection a challenging task. Gaussian model, rule based methods, and artificial neural networks are methods that have been used for human skin color detection. Deep learning methods are new techniques in learning that have shown improved classification power compared to neural networks. In this paper the authors use deep learning methods in order to enhance the capabilities of skin detection algorithms. Several experiments have been performed using auto encoders and different color spaces. The proposed technique is evaluated compare with other available methods in this domain using two color image databases. The results show that skin detection utilizing deep learning has better results compared to other methods such as rule-based, Gaussian model and feed forward neural network.

Human Skin Detection in Color Images Using Deep Learning

2020 ◽  
pp. 1310-1322
Author(s):  
Mohammadreza Hajiarbabi ◽  
Arvin Agah
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Human Skin ◽  
Gaussian Model ◽  
Color Images ◽  
Skin Detection ◽  
Feed Forward Neural Network ◽  
Learning Methods ◽  
Rule Based ◽  
Different Color

Human skin detection is an important and challenging problem in computer vision. Skin detection can be used as the first phase in face detection when using color images. The differences in illumination and ranges of skin colors have made skin detection a challenging task. Gaussian model, rule based methods, and artificial neural networks are methods that have been used for human skin color detection. Deep learning methods are new techniques in learning that have shown improved classification power compared to neural networks. In this paper the authors use deep learning methods in order to enhance the capabilities of skin detection algorithms. Several experiments have been performed using auto encoders and different color spaces. The proposed technique is evaluated compare with other available methods in this domain using two color image databases. The results show that skin detection utilizing deep learning has better results compared to other methods such as rule-based, Gaussian model and feed forward neural network.

scholarly journals Determination of human skin optical properties from spectrophotometric measurements based on optimization by genetic algorithms

2005 ◽  
Vol 10 (2) ◽  
pp. 024030 ◽  
Author(s):  
Rong Zhang ◽  
Wim Verkruysse ◽  
Bernard Choi ◽  
John A. Viator ◽  
Byungjo Jung ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Optical Properties ◽  
Human Skin

DEEP LEARNING TECHNOLOGY OF CONVOLUTIONAL NEURAL NETWORKS FOR FACIAL EXPRESSION RECOGNITION

2021 ◽  
Vol 4 (2) ◽  
pp. 192-201
Author(s):  
Denys Valeriiovych Petrosiuk ◽  
Olena Oleksandrivna Arsirii ◽  
Oksana Yurievna Babilunha ◽  
Anatolii Oleksandrovych Nikolenko
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Facial Expression ◽  
Facial Expressions ◽  
Convolutional Neural Networks ◽  
Facial Expression Recognition ◽  
Motor Units ◽  
Learning Technology ◽  
Expression Recognition

The application of deep learning convolutional neural networks for solving the problem of automated facial expression recognition and determination of emotions of a person is analyzed. It is proposed to use the advantages of the transfer approach to deep learning convolutional neural networks training to solve the problem of insufficient data volume in sets of images with different facial expressions. Most of these datasets are labeled in accordance with a facial coding system based on the units of human facial movement. The developed technology of transfer learning of the public deep learning convolutional neural networks families DenseNet and MobileNet, with the subsequent “fine tuning” of the network parameters, allowed to reduce the training time and computational resources when solving the problem of facial expression recognition without losing the reliability of recognition of motor units. During the development of deep learning technology for convolutional neural networks, the following tasks were solved. Firstly, the choice of publicly available convolutional neural networks of the DenseNet and MobileNet families pre-trained on the ImageNet dataset was substantiated, taking into account the peculiarities of transfer learning for the task of recognizing facial expressions and determining emotions. Secondary, a model of a deep convolutional neural network and a method for its training have been developed for solving problems of recognizing facial expressions and determining human emotions, taking into account the specifics of the selected pretrained convolutional neural networks. Thirdly, the developed deep learning technology was tested, and finally, the resource intensity and reliability of recognition of motor units on the DISFA set were assessed. The proposed technology of deep learning of convolutional neural networks can be used in the development of systems for automatic recognition of facial expressions and determination of human emotions for both stationary and mobile devices. Further modification of the systems for recognizing motor units of human facial activity in order to increase the reliability of recognition is possible using of the augmentation technique.

Sign in / Sign up

Export Citation Format

Share Document