Genetic improvements in illumination compensation by the discrete cosine transform and local normalization for face recognition

2008 ◽  
Author(s):  
Claudio A. Perez ◽  
Luis E. Castillo
Keyword(s):  
Face Recognition ◽  
Discrete Cosine Transform ◽  
Illumination Compensation ◽  
Cosine Transform

PHOTOMETRIC NORMALIZATION FOR FACE RECOGNITION USING LOCAL DISCRETE COSINE TRANSFORM

2013 ◽  
Vol 27 (03) ◽  
pp. 1360005 ◽  
Author(s):  
HEYDI MENDEZ-VÁZQUEZ ◽  
JOSEF KITTLER ◽  
CHI HO CHAN ◽  
EDEL GARCÍA-REYES
Keyword(s):  
Face Recognition ◽  
Discrete Cosine Transform ◽  
Total Error ◽  
Incident Light ◽  
Low Frequency ◽  
Recognition System ◽  
Classifier Fusion ◽  
Limiting Factors ◽  
Cosine Transform ◽  
Depth Analysis

Variations in illumination is one of major limiting factors of face recognition system performance. The effect of changes in the incident light on face images is analyzed, as well as its influence on the low frequency components of the image. Starting from this analysis, a new photometric normalization method for illumination invariant face recognition is presented. Low-frequency Discrete Cosine Transform coefficients in the logarithmic domain are used in a local way to reconstruct a slowly varying component of the face image which is caused by illumination. After smoothing, this component is subtracted from the original logarithmic image to compensate for illumination variations. Compared to other preprocessing algorithms, our method achieved a very good performance with a total error rate very similar to that produced by the best performing state-of-the-art algorithm. An in-depth analysis of the two preprocessing methods revealed notable differences in their behavior, which is exploited in a multiple classifier fusion framework to achieve further performance improvement. The superiority of the proposal is demonstrated in both face verification and identification experiments.

Face Recognition System using Discrete Cosine Transform combined with MLP and RBF Neural Networks

2012 ◽  
Vol 4 (4) ◽  
pp. 11-35 ◽  
Author(s):  
Fatma Zohra Chelali ◽  
Amar Djeradi
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Discrete Cosine Transform ◽  
Recognition Rate ◽  
Recognition System ◽  
Rbf Neural Networks ◽  
Cosine Transform ◽  
Linear Discriminant ◽  
Face Recognition System ◽  
Dct Coefficients

Proposed is an efficient face recognition algorithm using the discrete cosine transform DCT Technique for reducing dimensionality and image parameterization. These DCT coefficients are examined by a MLP (Multi-Layer Perceptron) and radial basis function RBF neural networks. Their purpose is to present a face recognition system that is a combination of discrete cosine transform (DCT) algorithm with a MLP and RBF neural networks. Neural networks have been widely applied in pattern recognition for the reason that neural-networks-based classifiers can incorporate both statistical and structural information and achieve better performance than the simple minimum distance classifiers. The authors demonstrate experimentally that when DCT coefficients are fed into a back propagation neural network for classification, a high recognition rate can be achieved by using a very small proportion of transform coefficients. Comparison with other statistical methods like Principal component Analysis (PCA) and Linear Discriminant Analysis (LDA) is presented. Their face recognition system is tested on the computer vision science research projects and the ORL database.

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