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

AbstractIn this paper, the pth moment exponential stability for a class of impulsive delayed Hopfield neural networks is investigated. Some concise algebraic criteria are provided by a new method concerned with impulsive integral inequalities. Our discussion neither requires a complicated Lyapunov function nor the differentiability of the delay function. In addition, we also summarize a new result on the exponential stability of a class of impulsive integral inequalities. Finally, one example is given to illustrate the effectiveness of the obtained results.

Face Recognition Using Principal Component Analysis and RBF Neural Networks

2008 First International Conference on Emerging Trends in Engineering and Technology ◽

10.1109/icetet.2008.104 ◽

2008 ◽

Cited By ~ 15

Author(s):

S. Thakur ◽

J.K. Sing ◽

D.K. Basu ◽

M. Nasipuri ◽

M. Kundu

Keyword(s):

Neural Networks ◽

Principal Component Analysis ◽

Face Recognition ◽

Principal Component ◽

Component Analysis ◽

Rbf Neural Networks

AcFR: Active Face Recognition Using Convolutional Neural Networks

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) ◽

10.1109/cvprw.2017.11 ◽

2017 ◽

Cited By ~ 5

Author(s):

Masaki Nakada ◽

Han Wang ◽

Demetri Terzopoulos

Keyword(s):

Neural Networks ◽

Face Recognition ◽

Convolutional Neural Networks

Multicore and GPU Parallelization of Neural Networks for Face Recognition

Procedia Computer Science ◽

10.1016/j.procs.2013.05.198 ◽

2013 ◽

Vol 18 ◽

pp. 349-358 ◽

Cited By ~ 30

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

Lecture Notes in Computer Science - Foundations of Intelligent Systems ◽

10.1007/978-3-030-01851-1_10 ◽

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

Deep CNNs with Robust LBP Guiding Pooling for Face Recognition

Sensors ◽

10.3390/s18113876 ◽

2018 ◽

Vol 18 (11) ◽

pp. 3876 ◽

Cited By ~ 5

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.

A New Method Based on Bilateral-Filtering for Illumination Invariant Face Recognition

2010 Third International Conference on Information and Computing ◽

10.1109/icic.2010.332 ◽

2010 ◽

Cited By ~ 1

Author(s):

Huan Dai ◽

Xiaofeng Gu

Keyword(s):

Face Recognition ◽

New Method ◽

Bilateral Filtering ◽

Illumination Invariant

Dairy Cow Tiny Face Recognition Based on Convolutional Neural Networks

Biometric Recognition - Lecture Notes in Computer Science ◽

10.1007/978-3-030-31456-9_24 ◽

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

Real World of Artificial Intelligence - A Review

Journal of Technology Management for Growing Economies ◽

10.15415/jtmge.2020.112005 ◽

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.