HANFIS: A New Fast and Robust Approach for Face Recognition and Facial Image Classification

2018 ◽  
pp. 81-99 ◽  
Author(s):  
R. Senthilkumar ◽  
R. K. Gnanamurthy
Keyword(s):  
Face Recognition ◽  
Image Classification ◽  
Facial Image ◽  
Robust Approach

scholarly journals A syncretic representation for image classification and face recognition

2016 ◽  
Vol 1 (2) ◽  
pp. 173-178 ◽  
Author(s):  
Zhongli Ma ◽  
Quanyong Liu ◽  
Kai Sun ◽  
Sui Zhan
Keyword(s):  
Face Recognition ◽  
Image Classification

scholarly journals Human Face Recognition from Part of a Facial Image based on Image Stitching

2021 ◽  
Vol 12 (12) ◽  
Author(s):  
Osama R. Shahin ◽  
Rami Ayedi ◽  
Alanazi Rayan ◽  
Rasha M. Abd El-Aziz ◽  
Ahmed I. Taloba
Keyword(s):  
Face Recognition ◽  
Image Stitching ◽  
Human Face ◽  
Facial Image ◽  
Human Face Recognition

scholarly journals Application Research of Deep Convolutional Neural Network in Computer Vision

2020 ◽  
Vol 2 (2) ◽  
pp. 23
Author(s):  
Lei Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Vision ◽  
Face Recognition ◽  
Human Brain ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Convolution Neural Network ◽  
Data Set ◽  
Deep Convolution Neural Network

<p>As an important research achievement in the field of brain like computing, deep convolution neural network has been widely used in many fields such as computer vision, natural language processing, information retrieval, speech recognition, semantic understanding and so on. It has set off a wave of neural network research in industry and academia and promoted the development of artificial intelligence. At present, the deep convolution neural network mainly simulates the complex hierarchical cognitive laws of the human brain by increasing the number of layers of the network, using a larger training data set, and improving the network structure or training learning algorithm of the existing neural network, so as to narrow the gap with the visual system of the human brain and enable the machine to acquire the capability of "abstract concepts". Deep convolution neural network has achieved great success in many computer vision tasks such as image classification, target detection, face recognition, pedestrian recognition, etc. Firstly, this paper reviews the development history of convolutional neural networks. Then, the working principle of the deep convolution neural network is analyzed in detail. Then, this paper mainly introduces the representative achievements of convolution neural network from the following two aspects, and shows the improvement effect of various technical methods on image classification accuracy through examples. From the aspect of adding network layers, the structures of classical convolutional neural networks such as AlexNet, ZF-Net, VGG, GoogLeNet and ResNet are discussed and analyzed. From the aspect of increasing the size of data set, the difficulties of manually adding labeled samples and the effect of using data amplification technology on improving the performance of neural network are introduced. This paper focuses on the latest research progress of convolution neural network in image classification and face recognition. Finally, the problems and challenges to be solved in future brain-like intelligence research based on deep convolution neural network are proposed.</p>

Facial Image Classification for Obstructive Sleep Apnea Pre-Screening

2021 ◽  
Author(s):  
Weihao Lin ◽  
Qinyan Zhang ◽  
Jijiang Yang ◽  
Fangfang ◽  
Qing Wang ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Image Classification ◽  
Facial Image ◽  
Obstructive Sleep

2D-PCA Representation and Sparse Representation for Image Recognition

2017 ◽  
Vol 14 (1) ◽  
pp. 829-834 ◽  
Author(s):  
Chunwei Tian ◽  
Qi Zhang ◽  
Jian Zhang ◽  
Guanglu Sun ◽  
Yuan Sun
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Image Classification ◽  
Principal Component ◽  
Face Image ◽  
Theoretical Research ◽  
Optimal Weights ◽  
Face Images ◽  
Pca Method ◽  
Pca Algorithm

The two-dimensional principal component analysis (2D-PCA) method has been widely applied in fields of image classification, computer vision, signal processing and pattern recognition. The 2D-PCA algorithm also has a satisfactory performance in both theoretical research and real-world applications. It not only retains main information of the original face images, but also decreases the dimension of original face images. In this paper, we integrate the 2D-PCA and spare representation classification (SRC) method to distinguish face images, which has great performance in face recognition. The novel representation of original face image obtained using 2D-PCA is complementary with original face image, so that the fusion of them can obviously improve the accuracy of face recognition. This is also attributed to the fact the features obtained using 2D-PCA are usually more robust than original face image matrices. The experiments of face recognition demonstrate that the combination of original face images and new representations of the original face images is more effective than the only original images. Especially, the simultaneous use of the 2D-PCA method and sparse representation can extremely improve accuracy in image classification. In this paper, the adaptive weighted fusion scheme automatically obtains optimal weights and it has no any parameter. The proposed method is not only simple and easy to achieve, but also obtains high accuracy in face recognition.

scholarly journals Enhancing ELM-based Facial Image Classification by Exploiting Multiple Facial Views

2015 ◽  
Vol 51 ◽  
pp. 2814-2821 ◽  
Author(s):  
Alexandros Iosifidis ◽  
Anastasios Tefas ◽  
Ioannis Pitas
Keyword(s):  
Image Classification ◽  
Facial Image
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