Using Neural Networks with Differential Evolution Learning for Face Recognition

2014 ◽  
Author(s):  
Shih Yen Huang ◽  
Cheng Jian Lin
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Differential Evolution ◽  
Evolution Learning

scholarly journals Prediction of Nanofluid Characteristics and Flow Pattern on Artificial Differential Evolution Learning Nodes and Fuzzy Framework

ACS Omega ◽  
2020 ◽  
Vol 5 (35) ◽  
pp. 22091-22098 ◽  
Author(s):  
Meisam Babanezhad ◽  
Samyar Zabihi ◽  
Ali Taghvaie Nakhjiri ◽  
Azam Marjani ◽  
Iman Behroyan ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Flow Pattern ◽  
Fuzzy Framework ◽  
Evolution Learning

Classification of slopes and prediction of factor of safety using differential evolution neural networks

2011 ◽  
Vol 64 (1) ◽  
pp. 201-210 ◽  
Author(s):  
Sarat Kumar Das ◽  
Rajani Kanta Biswal ◽  
N. Sivakugan ◽  
Bitanjaya Das
Keyword(s):  
Neural Networks ◽  
Differential Evolution ◽  
Factor Of Safety

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.

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