Face Recognition Using Multi-scale ICA Texture Pattern and Farthest Prototype Representation Classification

2013 ◽  
pp. 356-363
Author(s):  
Meng Wu ◽  
Jun Zhou ◽  
Jun Sun
Keyword(s):  
Face Recognition ◽  
Multi Scale ◽  
Texture Pattern

MULTI-SCALE GRADIENT INVARIANT FOR FACE RECOGNITION UNDER VARYING ILLUMINATION

2012 ◽  
Vol 26 (08) ◽  
pp. 1256016 ◽  
Author(s):  
BIN XU ◽  
YUAN YAN TANG ◽  
BIN FANG ◽  
ZHAO WEI SHANG
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Image Noise ◽  
Scale Analysis ◽  
Image Gradient ◽  
Multi Scale ◽  
Novel Approach ◽  
Gradient Domain ◽  
Inherent Structure ◽  
Multi Scale Analysis

In this paper, a novel approach derived from image gradient domain called multi-scale gradient faces (MGF) is proposed to abstract multi-scale illumination-insensitive measure for face recognition. MGF applies multi-scale analysis on image gradient information, which can discover underlying inherent structure in images and keep the details at most while removing varying lighting. The proposed approach provides state-of-the-art performance on Extended YaleB and PIE: Recognition rates of 99.11% achieved on PIE database and 99.38% achieved on YaleB which outperforms most existing approaches. Furthermore, the experimental results on noised Yale-B validate that MGF is more robust to image noise.

Low-Resolution Face Recognition of Multi-Scale Blocking CS-LBP and Weighted PCA

2016 ◽  
Vol 30 (08) ◽  
pp. 1656005 ◽  
Author(s):  
Jiadi Li ◽  
Zhenxue Chen ◽  
Chengyun Liu
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Principal Component ◽  
Low Resolution ◽  
Multi Scale ◽  
Face Features ◽  
Weighted Principal Component ◽  
Pca Algorithm

A novel method is proposed in this paper to improve the recognition accuracy of Local Binary Pattern (LBP) on low-resolution face recognition. More precise descriptors and effectively face features can be extracted by combining multi-scale blocking center symmetric local binary pattern (CS-LBP) based on Gaussian pyramids and weighted principal component analysis (PCA) on low-resolution condition. Firstly, the features statistical histograms of face images are calculated by multi-scale blocking CS-LBP operator. Secondly, the stronger classification and lower dimension features can be got by applying weighted PCA algorithm. Finally, the different classifiers are used to select the optimal classification categories of low-resolution face set and calculate the recognition rate. The results in the ORL human face databases show that recognition rate can get 89.38% when the resolution of face image drops to 12[Formula: see text]10 pixel and basically satisfy the practical requirements of recognition. The further comparison of other descriptors and experiments from videos proved that the novel algorithm can improve recognition accuracy.

A FUSION FRAMEWORK FOR FACE RECOGNITION UNDER VARYING ILLUMINATION BASED ON MULTI-SCALE ANALYSIS

2012 ◽  
Vol 10 (03) ◽  
pp. 1250029 ◽  
Author(s):  
HENGXIN CHEN ◽  
YUANYAN TANG ◽  
BIN FANG ◽  
LIFANG ZHOU
Keyword(s):  
Face Recognition ◽  
High Frequency ◽  
Frequency Component ◽  
High Frequency Component ◽  
Frequency Signal ◽  
High Frequency Signal ◽  
Signal Component ◽  
Illumination Invariance ◽  
Multi Scale ◽  
The Face

Varying illumination is a huge challenge of face recognition. The variation caused by varying illumination in the face appearance can be much larger than the variation caused by personal identity. The high frequency signal component in image represents the detail characteristic of the face, and for the reason of being influenced scarcely by varying illumination, this signal component can be used as illumination invariance features in face recognition. However, the definition of the high frequency signal component is blurry, and it is impossible to separate this component from the face image exactly. Because of using the different decomposition methods and different decomposition parameters, high frequency component has been dispersed in decomposed detail images that characterize themselves by containing different scale frequency signal component. This paper proposes a framework to fuse that high frequency signal components in multi-scale detail images using adaptive weight. This novel framework is an open structure, and any method of getting illumination invariance feature can be applied on this framework. The experiment based on three open face databases shows the framework proposed by this paper can get remarkable performance.

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