Face Recognition Based on HOG and Fast PCA Algorithm

2017 ◽  
pp. 10-21 ◽  
Author(s):  
Xiang-Yu Li ◽  
Zhen-Xian Lin
Keyword(s):  
Face Recognition ◽  
Pca Algorithm

An Improved PCA Algorithm for Fast Face Recognition

2013 ◽  
Vol 5 (2) ◽  
pp. 429-435 ◽  
Author(s):  
Pengli Lu ◽  
Xingbin Jiang
Keyword(s):  
Face Recognition ◽  
Pca Algorithm

Low-Resolution Face Recognition of Multi-Scale Blocking CS-LBP and Weighted PCA

2016 ◽  
Vol 30 (08) ◽  
pp. 1656005 ◽  
Author(s):  
Jiadi Li ◽  
Zhenxue Chen ◽  
Chengyun Liu
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Principal Component ◽  
Low Resolution ◽  
Multi Scale ◽  
Face Features ◽  
Weighted Principal Component ◽  
Pca Algorithm

A novel method is proposed in this paper to improve the recognition accuracy of Local Binary Pattern (LBP) on low-resolution face recognition. More precise descriptors and effectively face features can be extracted by combining multi-scale blocking center symmetric local binary pattern (CS-LBP) based on Gaussian pyramids and weighted principal component analysis (PCA) on low-resolution condition. Firstly, the features statistical histograms of face images are calculated by multi-scale blocking CS-LBP operator. Secondly, the stronger classification and lower dimension features can be got by applying weighted PCA algorithm. Finally, the different classifiers are used to select the optimal classification categories of low-resolution face set and calculate the recognition rate. The results in the ORL human face databases show that recognition rate can get 89.38% when the resolution of face image drops to 12[Formula: see text]10 pixel and basically satisfy the practical requirements of recognition. The further comparison of other descriptors and experiments from videos proved that the novel algorithm can improve recognition accuracy.

The Application of Face Recognition Based on OpenCV

2011 ◽  
Vol 403-408 ◽  
pp. 2350-2353
Author(s):  
Su Li
Keyword(s):  
Face Recognition ◽  
Face Detection ◽  
Recognition System ◽  
Biometric Recognition ◽  
The Core ◽  
Face Recognition System ◽  
Learning Functions ◽  
Key Technologies ◽  
Pca Algorithm ◽  
Mathematical Operations

Face recognition is a significant method, which is one of the biometric recognition. A face recognition system consists of two key technologies, namely, face detection and face recognition. In order to achieve two key technologies, Haar-Like feature and AdsBoost algorithm can be used to achieve face detection module. And PCA algorithm can be used to achieve face recognition module. For achieve application more quickly and efficiently, the core of the system develops with OpenCV. And the main use is its image processing, mathematical operations, and machine learning functions.

2D-PCA Representation and Sparse Representation for Image Recognition

2017 ◽  
Vol 14 (1) ◽  
pp. 829-834 ◽  
Author(s):  
Chunwei Tian ◽  
Qi Zhang ◽  
Jian Zhang ◽  
Guanglu Sun ◽  
Yuan Sun
Keyword(s):  
Face Recognition ◽  
Sparse Representation ◽  
Image Classification ◽  
Principal Component ◽  
Face Image ◽  
Theoretical Research ◽  
Optimal Weights ◽  
Face Images ◽  
Pca Method ◽  
Pca Algorithm

The two-dimensional principal component analysis (2D-PCA) method has been widely applied in fields of image classification, computer vision, signal processing and pattern recognition. The 2D-PCA algorithm also has a satisfactory performance in both theoretical research and real-world applications. It not only retains main information of the original face images, but also decreases the dimension of original face images. In this paper, we integrate the 2D-PCA and spare representation classification (SRC) method to distinguish face images, which has great performance in face recognition. The novel representation of original face image obtained using 2D-PCA is complementary with original face image, so that the fusion of them can obviously improve the accuracy of face recognition. This is also attributed to the fact the features obtained using 2D-PCA are usually more robust than original face image matrices. The experiments of face recognition demonstrate that the combination of original face images and new representations of the original face images is more effective than the only original images. Especially, the simultaneous use of the 2D-PCA method and sparse representation can extremely improve accuracy in image classification. In this paper, the adaptive weighted fusion scheme automatically obtains optimal weights and it has no any parameter. The proposed method is not only simple and easy to achieve, but also obtains high accuracy in face recognition.

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