Color Local Texture Features for Color Face Recognition

2012 ◽  
Vol 21 (3) ◽  
pp. 1366-1380 ◽  
Author(s):  
Jae Young Choi ◽  
Yong Man Ro ◽  
K. N. Plataniotis
Keyword(s):  
Face Recognition ◽  
Texture Features ◽  
Local Texture ◽  
Color Face Recognition

A novel face recognition in uncontrolled environment based on block 2D-CS-LBP features and deep residual network

2020 ◽  
Vol 13 (2) ◽  
pp. 207-221
Author(s):  
Minghua Wei
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Texture Features ◽  
Recognition Algorithm ◽  
Residual Network ◽  
Content Type ◽  
Identity Recognition ◽  
Local Texture ◽  
Computation Efficiency ◽  
The Face

PurposeIn order to solve the problem that the performance of the existing local feature descriptors in uncontrolled environment is greatly affected by illumination, background, occlusion and other factors, we propose a novel face recognition algorithm in uncontrolled environment which combines the block central symmetry local binary pattern (CS-LBP) and deep residual network (DRN) model.Design/methodology/approachThe algorithm first extracts the block CSP-LBP features of the face image, then incorporates the extracted features into the DRN model, and gives the face recognition results by using a well-trained DRN model. The features obtained by the proposed algorithm have the characteristics of both local texture features and deep features that robust to illumination.FindingsCompared with the direct usage of the original image, the usage of local texture features of the image as the input of DRN model significantly improves the computation efficiency. Experimental results on the face datasets of FERET, YALE-B and CMU-PIE have shown that the recognition rate of the proposed algorithm is significantly higher than that of other compared algorithms.Originality/valueThe proposed algorithm fundamentally solves the problem of face identity recognition in uncontrolled environment, and it is particularly robust to the change of illumination, which proves its superiority.

scholarly journals HUMAN IDENTIFICATION BASED ON FACE RECOGNITION SYSTEM

2021 ◽  
Vol 25 (01) ◽  
pp. 80-91
Author(s):  
Saba K. Naji ◽  
◽  
Muthana H. Hamd ◽  
Keyword(s):  
Face Recognition ◽  
Euclidean Distance ◽  
Local Binary Pattern ◽  
Texture Features ◽  
Recognition System ◽  
Manhattan Distance ◽  
Distance Measurements ◽  
Local Ternary Pattern ◽  
Face Recognition System ◽  
Cosine Distance

Due to, the great electronic development, which reinforced the need to define people's identities, different methods, and databases to identification people's identities have emerged. In this paper, we compare the results of two texture analysis methods: Local Binary Pattern (LBP) and Local Ternary Pattern (LTP). The comparison based on comparing the extracting facial texture features of 40 and 401 subjects taken from ORL and UFI databases respectively. As well, the comparison has taken in the account using three distance measurements such as; Manhattan Distance (MD), Euclidean Distance (ED), and Cosine Distance (CD). Where the maximum accuracy of the LBP method (99.23%) is obtained with a Manhattan and ORL database, while the LTP method attained (98.76%) using the same distance and database. While, the facial database of UFI shows low quality, which is satisfied 75.98% and 73.82% recognition rates using LBP and LTP respectively with Manhattan distance.

scholarly journals AN EFFECTIVE COLOR FACE RECOGNITION BASED ON BEST COLOR FEATURE SELECTION ALGORITHM USING WEIGHTED FEATURES FUSION SYSTEM

2013 ◽  
Vol 8 (2) ◽  
pp. 787-795
Author(s):  
Sasi Kumar Balasundaram ◽  
J. Umadevi ◽  
B. Sankara Gomathi
Keyword(s):  
Feature Selection ◽  
Face Recognition ◽  
Feature Fusion ◽  
Recognition Performance ◽  
Feature Selection Method ◽  
Recognition Method ◽  
Color Feature ◽  
Color Component ◽  
Pose Variation ◽  
Color Face Recognition

This paper aims to achieve the best color face recognition performance. The newly introduced feature selection method takes advantage of novel learning which is used to find the optimal set of color-component features for the purpose of achieving the best face recognition result. The proposed color face recognition method consists of two parts namely color-component feature selection with boosting and color face recognition solution using selected color component features. This method is better than existing color face recognition methods with illumination, pose variation and low resolution face images. This system is based on the selection of the best color component features from various color models using the novel boosting learning framework. These selected color component features are then combined into a single concatenated color feature using weighted feature fusion. The effectiveness of color face recognition method has been successfully evaluated by the public face databases.

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