Face recognition using neural networks

2002 ◽  
Author(s):  
S.C. Delipersad ◽  
A.D. Broadhurst
Keyword(s):  
Neural Networks ◽  
Face Recognition

A new method of NIR face recognition using kernel projection DCV and neural networks

2013 ◽  
Author(s):  
Ya Qiao ◽  
Yuan Lu ◽  
Yun-song Feng ◽  
Feng Li ◽  
Yongshun Ling
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
New Method

scholarly journals Multicore and GPU Parallelization of Neural Networks for Face Recognition

2013 ◽  
Vol 18 ◽  
pp. 349-358 ◽  
Author(s):  
Altaf Ahmad Huqqani ◽  
Erich Schikuta ◽  
Sicen Ye ◽  
Peng Chen
Keyword(s):  
Neural Networks ◽  
Face Recognition

Deep Neural Networks for Face Recognition: Pairwise Optimisation

2018 ◽  
pp. 103-110
Author(s):  
Elitsa Popova ◽  
Athanasios Athanasopoulos ◽  
Efraim Ie ◽  
Nikolaos Christou ◽  
Ndifreke Nyah
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Deep Neural Networks

scholarly journals Deep CNNs with Robust LBP Guiding Pooling for Face Recognition

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3876 ◽  
Author(s):  
Zhongjian Ma ◽  
Yuanyuan Ding ◽  
Baoqing Li ◽  
Xiaobing Yuan
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Computational Complexity ◽  
Local Binary Pattern ◽  
Weighted Average ◽  
Sliding Window ◽  
Performance Gain ◽  
Feature Maps ◽  
Input Feature ◽  
Noise Impact

Pooling layer in Convolutional Neural Networks (CNNs) is designed to reduce dimensions and computational complexity. Unfortunately, CNN is easily disturbed by noise in images when extracting features from input images. The traditional pooling layer directly samples the input feature maps without considering whether they are affected by noise, which brings about accumulated noise in the subsequent feature maps as well as undesirable network outputs. To address this issue, a robust Local Binary Pattern (LBP) Guiding Pooling (G-RLBP) mechanism is proposed in this paper to down sample the input feature maps and lower the noise impact simultaneously. The proposed G-RLBP method calculates the weighted average of all pixels in the sliding window of this pooling layer as the final results based on their corresponding probabilities of being affected by noise, thus lowers the noise impact from input images at the first several layers of the CNNs. The experimental results show that the carefully designed G-RLBP layer can successfully lower the noise impact and improve the recognition rates of the CNN models over the traditional pooling layer. The performance gain of the G-RLBP is quite remarkable when the images are severely affected by noise.

Dairy Cow Tiny Face Recognition Based on Convolutional Neural Networks

2019 ◽  
pp. 216-222
Author(s):  
Zehao Yang ◽  
Hao Xiong ◽  
Xiaolang Chen ◽  
Hanxing Liu ◽  
Yingjie Kuang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Convolutional Neural Networks ◽  
Dairy Cow

scholarly journals Real World of Artificial Intelligence - A Review

2020 ◽  
Vol 11 (2) ◽  
pp. 41-47
Author(s):  
Amandeep Kaur ◽  
Madhu Dhiman ◽  
Mansi Tonk ◽  
Ramneet Kaur
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Face Recognition ◽  
Language Processing ◽  
Real World ◽  
Review Paper ◽  
General Concept ◽  
Research Trends ◽  
Human Intelligence

Artificial Intelligence is the combination of machine and human intelligence, which are in research trends from the last many years. Different Artificial Intelligence programs have become capable of challenging humans by providing Expert Systems, Neural Networks, Robotics, Natural Language Processing, Face Recognition and Speech Recognition. Artificial Intelligence brings a bright future for different technical inventions in various fields. This review paper shows the general concept of Artificial Intelligence and presents an impact of Artificial Intelligence in the present and future world.

scholarly journals Illumination-robust face recognition based on deep convolutional neural networks architectures

2020 ◽  
Vol 18 (2) ◽  
pp. 1015
Author(s):  
Ridha Ilyas Bendjillali ◽  
Mohammed Beladgham ◽  
Khaled Merit ◽  
Abdelmalik Taleb-Ahmed
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Convolutional Neural Networks ◽  
Feature Learning ◽  
Histogram Equalization ◽  
Detection Algorithm ◽  
Deep Convolutional Neural Networks ◽  
Biometric Technology ◽  
Equalization Algorithm ◽  
Robust Face Recognition

<p><span>In the last decade, facial recognition techniques are considered the most important fields of research in biometric technology. In this research paper, we present a Face Recognition (FR) system divided into three steps: The Viola-Jones face detection algorithm, facial image enhancement using Modified Contrast Limited Adaptive Histogram Equalization algorithm (M-CLAHE), and feature learning for classification. For learning the features followed by classification we used VGG16, ResNet50 and Inception-v3 Convolutional Neural Networks (CNN) architectures for the proposed system. Our experimental work was performed on the Extended Yale B database and CMU PIE face database. Finally, the comparison with the other methods on both databases shows the robustness and effectiveness of the proposed approach. Where the Inception-v3 architecture has achieved a rate of 99, 44% and 99, 89% respectively.</span></p>

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