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Direct classification of normalized and flattened 3D facial landmarks reconstructed from 2D images is proposed in this paper for recognizing eight types of facial expressions depicting the emotions of- sadness, anger, contempt, disgust, fear, happiness, neutral and surprised. The first stage is the 3D projection of 2D facial landmarks. The pre-trai...
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Facial expression plays an important role in conveying the non-verbal cues of any person. Recognizing the facial expression is reffered to as the identification of emotional state. In this research, a real-time detection of emotions has been performed by training the model into different data sets and then emotional state of a person is displayed....
Citations
... After the study, results show that when applying the mask, the accuracy of emotion recognition decreases. Kalapala et al. [18] categorized eight different facial expressions that signify the emotions of grief, fury, contempt, disgust, fear, happiness, neutral, and startled; it was suggested that normalised, flattened 3D face landmarks that were created from 2D photos be used. This was accomplished by using the pre-trained convolutional Face Alignment Network (FAN) for 2D/3D face alignment. ...
The identification of human emotions from facial expressions is intriguing and challenging research given the subtle differences between certain emotions. Face masks are nowadays strongly recommended to minimize infection transmission due to Covid-19. Successful emotion identification from masked faces is challenging since the lower part of the face contributes significant cues for emotion identification. In this work, we investigate transfer learning using deep pre-trained networks for emotion recognition from masked faces. Specifically, we fine-tune the pre-trained models: - EfficientNet-B0, ResNet-50, Inception-v3, Xception and AlexNet, on the benchmark Facial Expression Recognition (FER) 2013 dataset containing seven categories of emotions, namely, angry, disgust, fear, happy, sad, surprise and neutral. The experiments reveal that the Inception-v3 model outperformed all other deep learning models and the machine learning models Support Vector Machine (SVM) and Artificial Neural Network (ANN), for facial emotion recognition from masked faces.
Face recognition is a technique for recognizing or authenticating someone’s identification based on a quick glance at their face. After that, this application can employ computer vision to discover a potential face inside its stream. Facial recognition is been used in a various routine operation, from mobile phone unlocking to ATMs. Individuals and businesses use automated teller machines (ATMs) to conduct a spectrum of financial activities, includes banking, for both individuals and organizations. There seem to be ATMs everywhere, such as in restaurants, supermarkets, convenience stores, malls, schools, gas stations, hotels, workplaces, banking facilities, airports, entertainment venues, transportation facilities, and numerous other locations. Consumers often have access to ATMs on a continuous basis, allowing them to conduct financial transactions at any time of day or week. In this project, face recognition and a tiered security mechanism are used. Machine learning, OpenCV, and Python are used to implement face recognition. In this situation, Face embeddings are used to extract characteristics from the face. A neural network uses a picture of a person’s face as input and generates vectors representing the most important face attributes. This vector is called an We refer to it as face embedding since it occurs in machine learning. The project aims to reduce the risks associated with remote ATMs and the problems associated with fraudulent transactions, such as misusing someone else’s card to withdraw money. Therefore, to overcome this problem, we developed a solution using ML to limit card use to just authorized individuals who can be recognized using face recognition software.
Although deep neural networks have achieved reasonable accuracy in solving face alignment, it is still a challenging task, specifically when we deal with facial images, under occlusion, or extreme head poses. Heatmap-based Regression (HBR) and Coordinate-based Regression (CBR) are among the two mainly used methods for face alignment. CBR methods require less computer memory, though their performance is less than HBR methods. In this paper, we propose an Adaptive Coordinate-based Regression (ACR) loss to improve the accuracy of CBR for face alignment. Inspired by the Active Shape Model (ASM), we generate Smooth-Face objects, a set of facial landmark points with less variations compared to the ground truth landmark points. We then introduce a method to estimate the level of difficulty in predicting each landmark point for the network by comparing the distribution of the ground truth landmark points and the corresponding Smooth-Face objects. Our proposed ACR Loss can adaptively modify its curvature and the influence of the loss based on the difficulty level of predicting each landmark point in a face. Accordingly, the ACR Loss guides the network toward challenging points than easier points, which improves the accuracy of the face alignment task. Our extensive evaluation shows the capabilities of the proposed ACR Loss in predicting facial landmark points in various facial images.
