An efficient illumination normalization method for face recognition

2006 ◽  
Vol 27 (6) ◽  
pp. 609-617 ◽  
Author(s):  
Xudong Xie ◽  
Kin-Man Lam
Keyword(s):  
Face Recognition ◽  
Normalization Method ◽  
Illumination Normalization

scholarly journals A Robust Illumination Normalization Method Based on Mean Estimation for Face Recognition

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Yong Luo ◽  
Ye-Peng Guan ◽  
Chang-Qi Zhang
Keyword(s):  
Face Recognition ◽  
Estimation Method ◽  
Texture Features ◽  
Mean Value ◽  
Normalization Method ◽  
Illumination Normalization ◽  
Practical Applications ◽  
The Arts ◽  
Mean Estimation ◽  
The Impact

An illumination normalization method for face recognition has been developed since it was difficult to control lighting conditions efficiently in the practical applications. Considering that the irradiation light is of little variation in a certain area, a mean estimation method is used to simulate the illumination component of a face image. Illumination component is removed by subtracting the mean estimation from the original image. In order to highlight face texture features and suppress the impact of adjacent domains, a ratio of the quotient image and its modulus mean value is obtained. The exponent result of the ratio is closely approximate to a relative reflection component. Since the gray value of facial organs is less than that of the facial skin, postprocessing is applied to the images in order to highlight facial texture for face recognition. Experiments show that the performance by using the proposed method is superior to that of state of the arts.

scholarly journals Illumination Normalization Algorithm Combination Analysis in Face Recognition

2017 ◽  
Vol 9 (3) ◽  
pp. 334-339
Author(s):  
Rokas Semėnas
Keyword(s):  
Face Recognition ◽  
Large Scale ◽  
Real Life ◽  
Direct Approach ◽  
Small Scale ◽  
Test Results ◽  
Illumination Normalization ◽  
Recognition Algorithms ◽  
Life Problems

Face recognition programs have many practical usages in various fields, such as security or entertainment. Existing recognition algorithms must deal with various real life problems – mainly with illumination. In practice, illumination normalization models are often used only for Small-scale futures extraction, ignoring Large-scale features. In this article, new and more direct approach to this problem is offered, used algorithms and test results are given.

Sign in / Sign up

Export Citation Format

Share Document