Finding Facial Emotions From the Clutter Scenes Using Zernike Moments-Based Convolutional Neural Networks

2021 ◽  
pp. 241-265
Author(s):  
Wencan Zhong ◽  
Vijayalakshmi G. V. Mahesh ◽  
Alex Noel Joseph Raj ◽  
Nersisson Ruban
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Face Recognition ◽  
Facial Expression ◽  
Convolutional Neural Networks ◽  
Zernike Moments ◽  
The Other ◽  
Recognition Systems ◽  
Facial Images ◽  
Surveillance Applications

Finding faces in the clutter scenes is a challenging task in automatic face recognition systems as facial images are subjected to changes in the illumination, facial expression, orientation, and occlusions. Also, in the cluttered scenes, faces are not completely visible and detecting them is essential as it is significant in surveillance applications to study the mood of the crowd. This chapter utilizes the deep learning methods to understand the cluttered scenes to find the faces and discriminate them into partial and full faces. The work proves that MTCNN used for detecting the faces and Zernike moments-based kernels employed in CNN for classifying the faces into partial and full takes advantage in delivering a notable performance as compared to the other techniques. Considering the limitation of recognition on partial face emotions, only the full faces are preserved, and further, the KDEF dataset is modified by MTCNN to detect only faces and classify them into four emotions. PatternNet is utilized to train and test the modified dataset to improve the accuracy of the results.

scholarly journals INFERRING THE SCALE AND CONTENT OF A MAP USING DEEP LEARNING

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 17-24
Author(s):  
G. Touya ◽  
F. Brisebard ◽  
F. Quinton ◽  
A. Courtial
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Visually Impaired ◽  
The Other ◽  
Visually Impaired People ◽  
Impaired People ◽  
Tactile Maps

Abstract. Visually impaired people cannot use classical maps but can learn to use tactile relief maps. These tactile maps are crucial at school to learn geography and history as well as the other students. They are produced manually by professional transcriptors in a very long and costly process. A platform able to generate tactile maps from maps scanned from geography textbooks could be extremely useful to these transcriptors, to fasten their production. As a first step towards such a platform, this paper proposes a method to infer the scale and the content of the map from its image. We used convolutional neural networks trained with a few hundred maps from French geography textbooks, and the results show promising results to infer labels about the content of the map (e.g. ”there are roads, cities and administrative boundaries”), and to infer the extent of the map (e.g. a map of France or of Europe).

scholarly journals A Study of The Convolutional Neural Networks Applications

2019 ◽  
Vol 3 (2) ◽  
pp. 31-40 ◽  
Author(s):  
Ahmed Shamsaldin ◽  
Polla Fattah ◽  
Tarik Rashid ◽  
Nawzad Al-Salihi
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Face Recognition ◽  
Convolutional Neural Networks ◽  
Language Processing ◽  
Text Classification ◽  
Scene Labeling ◽  
Real World Problems

At present, deep learning is widely used in a broad range of arenas. A convolutional neural networks (CNN) is becoming the star of deep learning as it gives the best and most precise results when cracking real-world problems. In this work, a brief description of the applications of CNNs in two areas will be presented: First, in computer vision, generally, that is, scene labeling, face recognition, action recognition, and image classification; Second, in natural language processing, that is, the fields of speech recognition and text classification.

scholarly journals Face Recognition with CNN and Inception Deep Learning Models

2019 ◽  
Vol 8 (3) ◽  
pp. 1932-1938
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Training Image ◽  
Learning Models ◽  
Training Images ◽  
Testing Data ◽  
Facial Images

In this work, deep learning methods are used to classify the facial images. ORL Database is used for the purpose of training the models and for testing. Three kinds of models are developed and their performances are measured. Convolutional Neural Networks (CNN), Convolutional Neural Network Based Inception Model with single training image per class (CNN-INC) and Convolutional Neural Network Based Inception Model with several training images per class (CNN-INC-MEAN) are developed. The ORL database has ten facial images for each person. Five images are used for training purpose and remaining 5 images are used for testing. The five images for the training are chosen randomly so that two sets of training and testing data is generated. The models are trained and tested on the two sets that are drawn from the same population. The results are presented for accuracy of face recognition

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.

scholarly journals Residual Compensation Networks for Heterogeneous Face Recognition

2019 ◽  
Vol 33 ◽  
pp. 8239-8246 ◽  
Author(s):  
Zhongying Deng ◽  
Xiaojiang Peng ◽  
Yu Qiao
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Learning Strategies ◽  
Convolutional Neural Networks ◽  
Network Architecture ◽  
State Of The Art ◽  
The Other ◽  
Training Images ◽  
Heterogeneous Face Recognition ◽  
Cosine Distance

Heterogeneous Face Recognition (HFR) is a challenging task due to large modality discrepancy as well as insufficient training images in certain modalities. In this paper, we propose a new two-branch network architecture, termed as Residual Compensation Networks (RCN), to learn separated features for different modalities in HFR. The RCN incorporates a residual compensation (RC) module and a modality discrepancy loss (MD loss) into traditional convolutional neural networks. The RC module reduces modal discrepancy by adding compensation to one of the modalities so that its representation can be close to the other modality. The MD loss alleviates modal discrepancy by minimizing the cosine distance between different modalities. In addition, we explore different architectures and positions for the RC module, and evaluate different transfer learning strategies for HFR. Extensive experiments on IIIT-D Viewed Sketch, Forensic Sketch, CASIA NIR-VIS 2.0 and CUHK NIR-VIS show that our RCN outperforms other state-of-the-art methods significantly.

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.

scholarly journals Convolutional neural networks framework for human hand gesture recognition

2021 ◽  
Vol 10 (4) ◽  
pp. 2223-2230
Author(s):  
Aseel Ghazi Mahmoud ◽  
Ahmed Mudheher Hasan ◽  
Nadia Moqbel Hassan
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Human Hand ◽  
Language Recognition ◽  
Hand Gestures ◽  
Recognition Systems

Recently, the recognition of human hand gestures is becoming a valuable technology for various applications like sign language recognition, virtual games and robotics control, video surveillance, and home automation. Owing to the recent development of deep learning and its excellent performance, deep learning-based hand gesture recognition systems can provide promising results. However, accurate recognition of hand gestures remains a substantial challenge that faces most of the recently existing recognition systems. In this paper, convolutional neural networks (CNN) framework with multiple layers for accurate, effective, and less complex human hand gesture recognition has been proposed. Since the images of the infrared hand gestures can provide accurate gesture information through the low illumination environment, the proposed system is tested and evaluated on a database of hand-based near-infrared which including ten gesture poses. Extensive experiments prove that the proposed system provides excellent results of accuracy, precision, sensitivity (recall), and F1-score. Furthermore, a comparison with recently existing systems is reported.

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