Robust Face Recognition Against Eyeglasses Interference by Integrating Local and Global Facial Features

2017 ◽  
pp. 51-61
Author(s):  
Hansheng Fang ◽  
Jie Wen ◽  
Yong Xu
Keyword(s):  
Face Recognition ◽  
Facial Features ◽  
Robust Face Recognition

Illumination Invariant Face Recognition Using Nonlocal Total Variation in Logarithmic Domain

2012 ◽  
Vol 241-244 ◽  
pp. 1652-1658 ◽  
Author(s):  
Cheng Zhe Xu
Keyword(s):  
Face Recognition ◽  
Total Variation ◽  
Facial Features ◽  
Face Database ◽  
Illumination Normalization ◽  
Illumination Invariant ◽  
Lighting Conditions ◽  
Total Variation Model ◽  
Nonlocal Total Variation ◽  
Robust Face Recognition

This paper presents a new illumination normalization method for robust face recognition under varying lighting conditions. In the proposed method, the illumination component is estimated by applying nonlocal total variation model in the logarithmic domain, and then the reflectance component is obtained based on reflectance model. The proposed method restrains the halo effect effectively while preserves the adequate texture information on the reflectance images. As an illumination invariant facial features, the reflectance images are directly utilized for face recognition. Experimental results on Yale face database B and CMU PIE database show that the performance of proposed method is robust and reliable in illumination invariant face recognition.

HUMAN FACE RECOGNITION FROM A SINGLE FRONT VIEW

1992 ◽  
Vol 06 (04) ◽  
pp. 571-593 ◽  
Author(s):  
CHING-WEN CHEN ◽  
CHUNG-LIN HUANG
Keyword(s):  
Face Recognition ◽  
Active Contour Model ◽  
Facial Feature ◽  
Recognition System ◽  
Facial Features ◽  
Human Face Recognition ◽  
Front View ◽  
Deformable Template ◽  
Face Recognition System ◽  
Feature Values

This paper presents a face recognition system which can identify the unknown identity effectively using the front-view facial features. In front-view facial feature extractions, we can capture the contours of eyes and mouth by the deformable template model because of their analytically describable shapes. However, the shapes of eyebrows, nostrils and face are difficult to model using a deformable template. We extract them by using the active contour model (snake). After the contours of all facial features have been captured, we calculate effective feature values from these extracted contours and construct databases for unknown identities classification. In the database generation phase, 12 models are photographed, and feature vectors are calculated for each portrait. In the identification phase if any one of these 12 persons has his picture taken again, the system can recognize his identity.

Towards Mask-robust Face Recognition

2021 ◽  
Author(s):  
Tao Feng ◽  
Liangpeng Xu ◽  
Hangjie Yuan ◽  
Yongfei Zhao ◽  
Mingqian Tang ◽  
...  
Keyword(s):  
Face Recognition ◽  
Robust Face Recognition
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