scholarly journals Affect State Classification from Face Segments using Resnet-50 and SE-Resnet-50

2020 ◽  
Vol 9 (3) ◽  
pp. 117-122
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Sufficient Information ◽  
Lower Segment ◽  
Facial Region ◽  
The Face ◽  
Full Face ◽  
The Right ◽  
Affect States ◽  
Affect State

One of the important components of an intelligent Human computer Interface system is accurate classification of the various affect states. Such interface systems are however plagued by a recurring problem of image occlusion. The challenge hence is to be able to classify the various affect states accurately from whatever portions of the face are available to the system. This paper attempts to investigate if there are segments within the facial region which carry sufficient information about the affect states. In this paper we have used two pre-defined Convolutional Neural networks (CNN). We have implemented a ResNet-50 network and a modified version of ResNet-50 which has a Squeeze and Excitation network connected to ResNet-50. This is called SE-ResNet-50. We use these two networks to classify seven basic affect states of Angry, Contempt, Disgust, Fear, Happy, Sad and Surprise from various segments of the face. We partition the face into four regions with each region comprising of only 50% of the original data. The results obtained are compared with that obtained using the full face. The validation accuracy values are obtained for full face as well as the four segments of the face. The paper also calculates precision and recall for each partitioned area for each of the affect states using the two networks. Our evaluation shows that both, ResNet-50 as well as SE-ResNet-50 are successful in accurately classifying all the 7 affect state from the Right segment, Left segment Lower segment and Upper segment of the face. While ResNet-50 performs marginally better compared to the SE-ResNet-50 in identifying the various affect states form the right, left and lower segments of the face, SE-ResNet-50 performs better in identifying the affect states from the upper segment of the face. We can thus conclude that right segment, left segment, lower segment and upper segments of the face contain sufficient information to correctly classify the seven affect states. The experimental results presented in this paper show that pre-defined Convolutional Neural Networks gives us very high accuracy, precision and recall values and hence can be used to accurately classify affect states even when there are occlusions present in the image and only certain portions of the face are available for analysis.

scholarly journals Convolutional Neural Networks for Semantic Segmentation as a Tool for Multiclass Face Analysis in Thermal Infrared

2021 ◽  
Vol 40 (1) ◽  
Author(s):  
David Müller ◽  
Andreas Ehlen ◽  
Bernd Valeske
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Thermal Infrared ◽  
Relative Temperature ◽  
Face Analysis ◽  
Image Translation ◽  
The Face ◽  
Live Image ◽  
Quantitative Thermography

AbstractConvolutional neural networks were used for multiclass segmentation in thermal infrared face analysis. The principle is based on existing image-to-image translation approaches, where each pixel in an image is assigned to a class label. We show that established networks architectures can be trained for the task of multiclass face analysis in thermal infrared. Created class annotations consisted of pixel-accurate locations of different face classes. Subsequently, the trained network can segment an acquired unknown infrared face image into the defined classes. Furthermore, face classification in live image acquisition is shown, in order to be able to display the relative temperature in real-time from the learned areas. This allows a pixel-accurate temperature face analysis e.g. for infection detection like Covid-19. At the same time our approach offers the advantage of concentrating on the relevant areas of the face. Areas of the face irrelevant for the relative temperature calculation or accessories such as glasses, masks and jewelry are not considered. A custom database was created to train the network. The results were quantitatively evaluated with the intersection over union (IoU) metric. The methodology shown can be transferred to similar problems for more quantitative thermography tasks like in materials characterization or quality control in production.

A Veiled Threat: Belcacemi and Oussar v Belgium

2018 ◽  
Vol 20 (2) ◽  
pp. 190-200
Author(s):  
Jasper Doomen
Keyword(s):  
Criminal Law ◽  
Freedom Of Conscience ◽  
Unequal Treatment ◽  
The Face ◽  
The Individual ◽  
Full Face ◽  
The Right

The freedom of the individual can easily come into conflict with his or her obligation to integrate in society. The case of Belcacemi and Oussar v Belgium provides a good example. It is evident that some restrictions of citizens’ freedoms must be accepted for a state to function and, more basically, persist; as a consequence, it is acceptable that certain demands, incorporated in criminal law, are made of citizens. The issue of the extent to which such restrictions are justified has increasingly become a topic of discussion. The present case raises a number of important questions with respect to the right to wear a full-face veil in public if the societal norm is that the face should be visible, the most salient of which are whether women should be ‘protected’ from unequal treatment against their will and to what extent society may impose values on the individual. I will argue that Belgian law places unwarranted restrictions on citizens and that the values behind it testify to an outlook that is difficult to reconcile with the freedom of conscience and religion.

Methods for Monitoring the Psychophysiological State of a Human Operator via Emotions Reflected in Facial Expressions and Analysis of Blinking Characteristics using Deep Convolutional Neural Networks

2021 ◽  
pp. 120-134
Author(s):  
O.N. Korsun ◽  
V.N. Yurko
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Physiological State ◽  
Human Operator ◽  
Deep Convolutional Neural Networks ◽  
Automated Recognition ◽  
The Neural Network ◽  
Psychophysiological State ◽  
The Face ◽  
Flight Parameters

We analysed two approaches to estimating the state of a human operator according to video imaging of the face. These approaches, both using deep convolutional neural networks, are as follows: 1) automated emotion recognition; 2) analysis of blinking characteristics. The study involved assessing changes in the functional state of a human operator performing a manual landing in a flight simulator. During this process, flight parameters were recorded, and the operator’s face was filmed. Then we used our custom software to perform automated recognition of emotions (blinking), synchronising the emotions (blinking) recognised to the flight parameters recorded. As a result, we detected persistent patterns linking the operator fatigue level to the number of emotions recognised by the neural network. The type of emotion depends on unique psychological characteristics of the operator. Our experiments allow for easily tracing these links when analysing the emotions of "Sadness", "Fear" and "Anger". The study revealed a correlation between blinking properties and piloting accuracy. A higher piloting accuracy meant more blinks recorded, which may be explained by a stable psycho-physiological state leading to confident piloting

scholarly journals Image recognition in the artificial agriculture officer

2018 ◽  
Vol 7 (3.3) ◽  
pp. 119
Author(s):  
B Lokesh ◽  
Ravoori Charishma ◽  
Natuva Hiranmai
Keyword(s):  
Neural Networks ◽  
Information Retrieval ◽  
Search Engine ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Crop Production ◽  
Android Application ◽  
Weather Patterns ◽  
Crop Diseases ◽  
The Right

Farmers face a multitude of problems nowadays such as lower crop production, tumultuous weather patterns, and crop infections. All of these issues can be solved if they have access to the right information. The current methods of information retrieval, such as search engine lookup and talking to an Agriculture Officer, have multiple defects. A more suitable solution, that we are proposing, is an android application, available at all times, that can give succinct answers to any question a farmer may pose. The application will include an image recognition component that will be able to recognize a variety of crop diseases in the case that the farmer does not know what he is dealing with and is unable to describe it.  Image recognition is the ability of a computer to recognize and distinguish between different objects, and is actually a much harder problem to solve than it seems. We are using Tensorflow, a tool that uses convolutional neural networks, to implement it  

scholarly journals Hybrid Convolutional Neural Networks Based Framework for Skimmed Milk Powder Price Forecasting

2021 ◽  
Vol 13 (7) ◽  
pp. 3699
Author(s):  
Jarosław Malczewski ◽  
Wawrzyniec Czubak
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Milk Powder ◽  
Training Data ◽  
Series Data ◽  
Reconstruction Algorithms ◽  
Price Series ◽  
Skimmed Milk ◽  
The Right ◽  
Skimmed Milk Powder

The latest studies have compellingly argued that Neural Networks (NN) classification and prediction are the right direction for forecasting. It has been proven that NN are suitable models for any continuous function. Moreover, these methods are superior to conventional methods, such a Box–Jenkins, AR, MA, ARMA, or ARIMA. The latter assume a linear relationship between inputs and outputs. This assumption is not valid for skimmed milk powder (SMP) forecasting, because of nonlinearities, which are supposed to be approximated. The traditional prediction methods need complete date. The non-AI-based techniques regularly handle univariate-like data only. This assumption is not sufficient, because many external factors might influence the time series. It should be noted that any Artificial Neural Network (ANN) approach can be strongly affected by the relevancy and “clarity” of its input training data. In the proposed Convolutional Neural Networks based methodology assumes price series data to be sparse and noisy. The presented procedure utilizes Compressed Sensing (CS) methodology, which assumes noisy trends are incomplete signals for them to be reconstructed using CS reconstruction algorithms. Denoised trends are more relevant in terms of NN-based forecasting models’ prediction performance. Empirical results reveal robustness of the proposed technique.

scholarly journals A Result Analysis of Mask Detection based Notification System

2021 ◽  
Vol 9 (VI) ◽  
pp. 15-17
Author(s):  
Gaurav Kumar D. K. Singh
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Detection System ◽  
Face Mask ◽  
Research Paper ◽  
Public Places ◽  
Notification System ◽  
Full Face ◽  
Result Analysis

Face mask detection system will be the best option for preventing covid-19 spread at public places. Those models are mainly required for ensuring safety and hygiene in a public premises .The research paper consist of full face scan using a pre-trained model such that all the facial characters can be imprinted on the pixel basis by the pre-trained model that takes input from the camera associated with the program. The whole of the program is based on convolutional neural networks which extract features and associate them in the form of neurons

scholarly journals Robotic Stroking on the Face and Forearm: Touch Satiety and Effects on Mechanical Pain

2021 ◽  
Vol 2 ◽  
Author(s):  
Pankaj Taneja ◽  
Lene Baad-Hansen ◽  
Sumaiya Shaikh ◽  
Peter Svensson ◽  
Håkan Olausson
Keyword(s):  
Pain Sensitivity ◽  
Pain Modulation ◽  
Pain Thresholds ◽  
Facial Region ◽  
Thermal Pain ◽  
Tactile Stimuli ◽  
Pressure Pain Thresholds ◽  
The Face ◽  
The Right ◽  
Over Time

Background: Slow stroking touch is generally perceived as pleasant and reduces thermal pain. However, the tactile stimuli applied tend to be short-lasting and typically applied to the forearm. This study aimed to compare the effects of a long-lasting brushing stimulus applied to the facial region and the forearm on pressure pain thresholds (PPTs) taken on the hand. Outcome measurements were touch satiety and concurrent mechanical pain thresholds of the hand.Methods: A total of 24 participants were recruited and randomized to receive continuous stroking, utilizing a robotic stimulator, at C-tactile (CT) favorable (3 cm/s) and non-favorable (30 cm/s) velocities applied to the right face or forearm. Ratings of touch pleasantness and unpleasantness and PPTs from the hypothenar muscle of the right hand were collected at the start of stroking and once per minute for 5 min.Results: A reduction in PPTs (increased pain sensitivity) was observed over time (P < 0.001). However, the increase in pain sensitivity was less prominent when the face was stroked compared to the forearm (P = 0.001). Continuous stroking resulted in a significant interaction between region and time (P = 0.008) on pleasantness ratings, with a decline in ratings observed over time for the forearm, but not on the face. Unpleasantness ratings were generally low.Conclusion: We observed touch satiety for 5 min of continuous robotic brushing on the forearm confirming previous studies. However, we did not observe any touch satiety for brushing the face. Mechanical pain sensitivity, measured in the hand, increased over the 5-min period but less so when paired with brushing on the face than with brushing on the forearm. The differential effects of brushing on the face and forearm on touch satiety and pain modulation may be by the differences in the emotional relevance and neuronal pathways involved.

scholarly journals Face Recognition Based on Lightweight Convolutional Neural Networks

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 191
Author(s):  
Wenting Liu ◽  
Li Zhou ◽  
Jie Chen
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
Recognition Task ◽  
Training Method ◽  
The Face ◽  
And Storage ◽  
And Training

Face recognition algorithms based on deep learning methods have become increasingly popular. Most of these are based on highly precise but complex convolutional neural networks (CNNs), which require significant computing resources and storage, and are difficult to deploy on mobile devices or embedded terminals. In this paper, we propose several methods to improve the algorithms for face recognition based on a lightweight CNN, which is further optimized in terms of the network architecture and training pattern on the basis of MobileFaceNet. Regarding the network architecture, we introduce the Squeeze-and-Excitation (SE) block and propose three improved structures via a channel attention mechanism—the depthwise SE module, the depthwise separable SE module, and the linear SE module—which are able to learn the correlation of information between channels and assign them different weights. In addition, a novel training method for the face recognition task combined with an additive angular margin loss function is proposed that performs the compression and knowledge transfer of the deep network for face recognition. Finally, we obtained high-precision and lightweight face recognition models with fewer parameters and calculations that are more suitable for applications. Through extensive experiments and analysis, we demonstrate the effectiveness of the proposed methods.

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