Multi-view Cooperative Deep Convolutional Network for Facial Recognition with Small Samples Learning

2019 ◽  
pp. 207-216
Author(s):  
Amani Alfakih ◽  
Shuyuan Yang ◽  
Tao Hu
Keyword(s):  
Facial Recognition ◽  
Small Samples ◽  
Convolutional Network ◽  
Deep Convolutional Network

scholarly journals Facial Expression Recognition Based on Multi-Features Cooperative Deep Convolutional Network

2021 ◽  
Vol 11 (4) ◽  
pp. 1428
Author(s):  
Haopeng Wu ◽  
Zhiying Lu ◽  
Jianfeng Zhang ◽  
Xin Li ◽  
Mingyue Zhao ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Video Data ◽  
Expression Recognition ◽  
Convolutional Network ◽  
Facial Movements ◽  
The Face ◽  
Deep Convolutional Network ◽  
Selection Of

This paper addresses the problem of Facial Expression Recognition (FER), focusing on unobvious facial movements. Traditional methods often cause overfitting problems or incomplete information due to insufficient data and manual selection of features. Instead, our proposed network, which is called the Multi-features Cooperative Deep Convolutional Network (MC-DCN), maintains focus on the overall feature of the face and the trend of key parts. The processing of video data is the first stage. The method of ensemble of regression trees (ERT) is used to obtain the overall contour of the face. Then, the attention model is used to pick up the parts of face that are more susceptible to expressions. Under the combined effect of these two methods, the image which can be called a local feature map is obtained. After that, the video data are sent to MC-DCN, containing parallel sub-networks. While the overall spatiotemporal characteristics of facial expressions are obtained through the sequence of images, the selection of keys parts can better learn the changes in facial expressions brought about by subtle facial movements. By combining local features and global features, the proposed method can acquire more information, leading to better performance. The experimental results show that MC-DCN can achieve recognition rates of 95%, 78.6% and 78.3% on the three datasets SAVEE, MMI, and edited GEMEP, respectively.

scholarly journals Deep Convolution Neural Networks Learned Image Classification for Early Cancer Detection Using Lightweight

2021 ◽  
Author(s):  
Seshadri Ramana K ◽  
Bala Chowdappa K ◽  
Obulesu ooruchintala ◽  
Deena Babu Mandru ◽  
kallam suresh
Keyword(s):  
Cancer Detection ◽  
Early Cancer ◽  
The Body ◽  
Early Cancer Detection ◽  
Detection Accuracy ◽  
Convolutional Network ◽  
Max Pooling ◽  
Input Layer ◽  
Deep Convolutional Network ◽  
Fully Connected

Abstract Cancer is uncontrolled cell growth in any part of the body. Early cancer detection aims to identify patients who exhibit symptoms early on in order to maximise their chances of a successful treatment. Cancer disease mortality is decreased through early detection and treatment. Numerous researchers proposed a variety of image processing and machine learning approaches for cancer detection. However, existing systems did not improve detection accuracy or efficiency. A Deep Convolutional Neural Learning Classifier Model based on the Least Mean Square Filterative Ricker Wavelet Transform (L-DCNLC) is proposed to address the aforementioned issues. The L-DCNLC Model's primary objective is to detect cancer earlier by utilising a fully connected max pooling deep convolutional network with increased accuracy and reduced time consumption. The fully connected max pooling deep convolutional network is composed of one input layer, three hidden layers, and one output layer. Initially, the input layer of the L-DCNLC Model considers the number of patient images in the database as input.

scholarly journals Detection and Recognition of Text From Natural Camera Image using Deep Convolutional Network

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1043-1047
Keyword(s):  
Amino Acids ◽  
Life Science ◽  
Convolutional Network ◽  
Physiochemical Properties ◽  
Camera Image ◽  
Antigenic Properties ◽  
Deep Convolutional Network ◽  
Detection And Recognition

Amino acids are little bio-particles with different properties. The capacity to ascertain the physiochemical properties of proteins is pivotal in many research regions, for example, tranquilize plan, protein displaying and basic bioinformatics. The physiochemical properties of the protein decides its collaboration with different atoms and subsequently its capacity. Foreseeing the physiochemical properties of protein and translating its capacity is of extraordinary significance in the field of medication and life science. The point of this work is to create python based programming with graphical UI for anticipating the physiochemical and antigenic properties of protein. Thus the instrument was named as ASAP-Analysis of protein succession and antigenicity expectation. ASAP predicts the antigenicity of the protein succession from its amino corrosive arrangement, in light of Chou Fasman turns and antigenic file. ASAP computes different physiochemical properties that is required for invitro tests. ASAP utilizes standardization esteems that expansion the affectability of the apparatus.

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