Ensemble global and local features for single-sample face recognition

2015 ◽  
pp. 431-434 ◽  
Keyword(s):  
Face Recognition ◽  
Local Features ◽  
Single Sample ◽  
Global And Local

Robust two-stage face recognition approach using global and local features

2011 ◽  
Vol 28 (11) ◽  
pp. 1085-1098 ◽  
Author(s):  
Chandan Singh ◽  
Ekta Walia ◽  
Neerja Mittal
Keyword(s):  
Face Recognition ◽  
Local Features ◽  
Two Stage ◽  
Global And Local

Canonical Correlation Analysis on Fusion of Global and Local Features of Face Recognition

2014 ◽  
Vol 926-930 ◽  
pp. 3598-3603
Author(s):  
Xiao Xiong ◽  
Guo Fa Hao ◽  
Peng Zhong
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Local Features ◽  
Global Features ◽  
Features Fusion ◽  
Local Characteristics ◽  
Face Recognition System ◽  
Global And Local ◽  
Effective Integration

Face recognition belongs to the important content of the biometric identification, which is a important method in research of image processing and pattern recognition. It can effectively overcome the traditional authentication defects Through the facial recognition technology. At present, face recognition under ideal state research made some achievements, but the changes in light, shade, expression, posture changes the interference factors such as face recognition is still exist many problems. For this, put forward the integration of global and local features of face recognition research. Practice has proved that through the effective integration of global features and local characteristics, build based on global features and local features fusion face recognition system, can improve the recognition rate of face recognition, face recognition application benefit.

Recognition of Face Biometrics

2017 ◽  
pp. 440-457
Author(s):  
Pooja Sharma
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Hybrid Approach ◽  
Local Features ◽  
Rotation Invariant ◽  
Global Features ◽  
Local Ternary Patterns ◽  
Common Domain ◽  
Global And Local ◽  
Face Biometrics

In the proposed chapter, a novel, effective, and efficient approach to face recognition is presented. It is a fusion of both global and local features of images, which significantly achieves higher recognition. Initially, the global features of images are determined using polar cosine transforms (PCTs), which exhibit very less computation complexity as compared to other global feature extractors. For local features, the rotation invariant local ternary patterns are used rather than using the existing ones, which help improving the recognition rate and are in alignment with the rotation invariant property of PCTs. The fusion of both acquired global and local features is performed by mapping their features into a common domain. Finally, the proposed hybrid approach provides a robust feature set for face recognition. The experiments are performed on benchmark face databases, representing various expressions of facial images. The results of extensive set of experiments reveal the supremacy of the proposed method over other approaches in terms of efficiency and recognition results.

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