Feature based sliding window technique for face recognition

2010 ◽  
Author(s):  
Muhammad Younus Javed ◽  
Syed Maajid Mohsin ◽  
Muhammad Almas Anjum
Keyword(s):  
Face Recognition ◽  
Sliding Window ◽  
Window Technique ◽  
Feature Based

Overestimation of Low-Dose Radiation in Intensity-Modulated Radiotherapy with Sliding-Window Technique

2009 ◽  
Vol 185 (12) ◽  
pp. 821-829 ◽  
Author(s):  
Hilke Vorwerk ◽  
Daniela Wagner ◽  
Björn Seitz ◽  
Hans Christiansen ◽  
Hendrik A. Wolff ◽  
...  
Keyword(s):  
Low Dose ◽  
Intensity Modulated Radiotherapy ◽  
Sliding Window ◽  
Low Dose Radiation ◽  
Intensity Modulated ◽  
Window Technique ◽  
Dose Radiation

scholarly journals Face Recognition from Still Images to Video Sequences: A Local-Feature-Based Framework

2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
Shaokang Chen ◽  
Sandra Mau ◽  
Mehrtash T. Harandi ◽  
Conrad Sanderson ◽  
Abbas Bigdeli ◽  
...  
Keyword(s):  
Face Recognition ◽  
Local Feature ◽  
Video Sequences ◽  
Still Images ◽  
Feature Based

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.

Sign in / Sign up

Export Citation Format

Share Document