Bare Skin Image Classification using Convolution Neural Network

Image classification is critical and significant research problems in computer vision applications such as facial expression classification, satellite image classification, and plant classification based on images. Here in the paper, the image classification model is applied for identifying the display of daunting pictures on the internet. The proposed model uses Convolution neural network to identify these images and filter them through different blocks of the network, so that it can be classified accurately. The model will work as an extension to the web browser and will work on all websites when activated. The extension will be blurring the images and deactivating the links on web pages. This means that it will scan the entire web page and find all the daunting images present on that page. Then we will blur those images before they are loaded and the children could see them. Keywords— Activation Function, CNN, Images Classification , Optimizers, VGG-19

Enhanced Expectation of External Sensations of the Chest Regulates the Emotional Perception of Fearful Faces

Emotional perception can be shaped by inferences about bodily states. Here, we investigated whether exteroceptive inferences about bodily sensations in the chest area influence the perception of fearful faces. Twenty-two participants received pseudo-electrical acupuncture stimulation at three different acupoints: CV17 (chest), CV23 (chin), and PC6 (left forearm). All stimuli were delivered with corresponding visual cues, and the control condition included visual cues that did not match the stimulated body sites. After the stimulation, the participants were shown images with one of five morphed facial expressions, ranging from 100% fear to 100% disgust, and asked to classify them as fearful or disgusted. Brain activity was measured using functional magnetic resonance imaging during the facial expression classification task. When the participants expected that they would receive stimulation of the chest (CV17), the ratio of fearful to non-fearful classifications decreased compared to the control condition, and brain activities within the periaqueductal gray and the default mode network decreased when they viewed fearful faces. Our findings suggest that bodily sensations around the chest, but not the other tested body parts, were selectively associated with fear perception and that altering external inferences inhibited the perception of fearful faces.

Vision-Based Road Rage Detection Framework in Automotive Safety Applications

Drivers’ road rage is among the main causes of road accidents. Each year, it contributes to more deaths and injuries globally. In this context, it is important to implement systems that can supervise drivers by monitoring their level of concentration during the entire driving process. In this paper, a module for Advanced Driver Assistance System is used to minimise the accidents caused by road rage, alerting the driver when a predetermined level of rage is reached, thus increasing the transportation safety. To create a system that is independent of both the orientation of the driver’s face and the lighting conditions of the cabin, the proposed algorithmic pipeline integrates face detection and facial expression classification algorithms capable of handling such non-ideal situations. Moreover, road rage of the driver is estimated through a decision-making strategy based on the temporal consistency of facial expressions classified as “anger” and “disgust”. Several experiments were executed to assess the performance on both a real context and three standard benchmark datasets, two of which containing non-frontal-view facial expression and one which includes facial expression recorded from participants during driving. Results obtained show that the proposed module is competent for road rage estimation through facial expression recognition on the condition of multi-pose and changing in lighting conditions, with the recognition rates that achieve state-of-art results on the selected datasets.

Genetic Programming-Based Feature Learning for Facial Expression Classification

© 2020 IEEE. Facia1 expression classification is an important but challenging task in artificial intelligence and computer vision. To effectively solve facial expression classification, it is necessary to detect/locate the face and extract features from the face. However, these two tasks are often conducted separately and manually in a traditional facial expression classification system. Genetic programming (GP) can automatically evolve solutions for a task without rich human intervention. However, very few GP-based methods have been specifically developed for facial expression classification. Therefore, this paper proposes a GP-based feature learning approach to facial expression classification. The proposed approach can automatically select small regions of a face and extract appearance features from the small regions. The experimental results on four different facial expression classification data sets show that the proposed approach achieves significantly better results in almost all the comparisons. To further show the effectiveness of the proposed approach, different numbers of training images are used in the experiments. The results indicate that the proposed approach achieves significantly better performance than any of the baseline methods using a small number of training images. Further analysis shows that the proposed approach not only selects informative regions of the face but also finds a good combination of various features to obtain a high classification accuracy.

Genetic Programming-Based Feature Learning for Facial Expression Classification

© 2020 IEEE. Facia1 expression classification is an important but challenging task in artificial intelligence and computer vision. To effectively solve facial expression classification, it is necessary to detect/locate the face and extract features from the face. However, these two tasks are often conducted separately and manually in a traditional facial expression classification system. Genetic programming (GP) can automatically evolve solutions for a task without rich human intervention. However, very few GP-based methods have been specifically developed for facial expression classification. Therefore, this paper proposes a GP-based feature learning approach to facial expression classification. The proposed approach can automatically select small regions of a face and extract appearance features from the small regions. The experimental results on four different facial expression classification data sets show that the proposed approach achieves significantly better results in almost all the comparisons. To further show the effectiveness of the proposed approach, different numbers of training images are used in the experiments. The results indicate that the proposed approach achieves significantly better performance than any of the baseline methods using a small number of training images. Further analysis shows that the proposed approach not only selects informative regions of the face but also finds a good combination of various features to obtain a high classification accuracy.

A study on the evaluation method of foreign language classroom teaching process from the perspective of neuroeducation

The paper, with Neuroeducation as theoretical support, has explored the application of the neuroscientific assessment method to the assessment of classroom teaching process. Firstly, the scientific idea of monitoring and evaluating classroom teaching process is put forward. Secondly, the experiments of comparison of attention index and emotional index of different students in the course of learning,evaluation of teaching effect of two teachers in different teaching part and ERPs analysis of facial expression classification by teachers at different levels are conducted respectively. These sample experiments are used to illustrate the application of neuroscience to the assessment of classroom teaching process. This method can evaluate teachers’ teaching ability, students’ learning ability, students’ learning effect, course content selection and other aspects related to teaching. And more importantly, it can provide objective evaluation basis for classroom teaching process. It is expected that this method can efficiently help solve the problems in the existing evaluation methods of classroom teaching process.