Grid-Based Multi-scale PCA Method for Face Recognition in the Large Face Database

2006 ◽  
pp. 1033-1040 ◽  
Author(s):  
Haiyang Zhang ◽  
Huadong Ma ◽  
Anlong Ming
Keyword(s):  
Face Recognition ◽  
Face Database ◽  
Multi Scale ◽  
Pca Method ◽  
Grid Based ◽  
Large Face

FUZZY MEASURE BASED ADAPTIVE METHODS FOR ILLUMINATION INVARIANT FACE RECOGNITION

2011 ◽  
Vol 10 (03) ◽  
pp. 313-333 ◽  
Author(s):  
RAMJI M. MAKWANA ◽  
VISHVJIT K. THAKAR ◽  
NARENDRA C. CHAUHAN
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Facial Feature ◽  
Adaptive Methods ◽  
Fuzzy Measure ◽  
Image Method ◽  
Face Database ◽  
Multi Scale ◽  
Single Scale ◽  
Adaptive Nature

Varying illumination is one of the well known and challenging problems in Face Recognition applications. Numerous methods have been proposed by researchers, but recognition performance under complex illumination is not yet satisfactory. The paper presents Fuzzy based methods to adaptively normalize illumination in face images for Face Recognition under varying illumination conditions. The paper has main two contributions: (1) Fuzzy measure based Adaptive Single-scale Retinex and (2) Fuzzy measure based Adaptive Single-scale Self Quotient Image method. Also, two more variations of these methods are presented. There are two main advantages of these methods, as compared to multi-scale Retinex and Self Quotient methods. Firstly, due to the adaptive nature of proposed methods, discontinuity in facial feature is smoothed and discontinuity due to shadows is preserved and hence performance is better. Secondly, computational complexity is reduced because of single scale 3∗3 filter instead of multi-scale filters. Rigorous experiments have been performed on CMU PIE face database and Extended Yale B face database. For determining False Acceptance Rate, 529 and 550 imposter faces are used for experiments on PIE and Yale databases respectively. Proposed methods are compared with existing methods under same experimental setup using six performance evaluation parameters. Results have shown that Fuzzy measure based methods performs well.

On a development of sparse PCA method for face recognition problem

2021 ◽  
Author(s):  
Loc Tran ◽  
Bich Ngo ◽  
Tuan Tran ◽  
Lam Pham ◽  
An Mai
Keyword(s):  
Face Recognition ◽  
Recognition Problem ◽  
Sparse Pca ◽  
Pca Method

scholarly journals Face Recognition Using the SR-CNN Model

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4237 ◽  
Author(s):  
Yu-Xin Yang ◽  
Chang Wen ◽  
Kai Xie ◽  
Fang-Qing Wen ◽  
Guan-Qun Sheng ◽  
...  
Keyword(s):  
Face Recognition ◽  
Target Position ◽  
Operation Time ◽  
True Positive Rate ◽  
Processing Unit ◽  
True Positive ◽  
Face Database ◽  
Acceleration Ratio ◽  
Computational Performance ◽  
Positive Rate

In order to solve the problem of face recognition in complex environments being vulnerable to illumination change, object rotation, occlusion, and so on, which leads to the imprecision of target position, a face recognition algorithm with multi-feature fusion is proposed. This study presents a new robust face-matching method named SR-CNN, combining the rotation-invariant texture feature (RITF) vector, the scale-invariant feature transform (SIFT) vector, and the convolution neural network (CNN). Furthermore, a graphics processing unit (GPU) is used to parallelize the model for an optimal computational performance. The Labeled Faces in the Wild (LFW) database and self-collection face database were selected for experiments. It turns out that the true positive rate is improved by 10.97–13.24% and the acceleration ratio (the ratio between central processing unit (CPU) operation time and GPU time) is 5–6 times for the LFW face database. For the self-collection, the true positive rate increased by 12.65–15.31%, and the acceleration ratio improved by a factor of 6–7.

MULTI-SCALE GRADIENT INVARIANT FOR FACE RECOGNITION UNDER VARYING ILLUMINATION

2012 ◽  
Vol 26 (08) ◽  
pp. 1256016 ◽  
Author(s):  
BIN XU ◽  
YUAN YAN TANG ◽  
BIN FANG ◽  
ZHAO WEI SHANG
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Image Noise ◽  
Scale Analysis ◽  
Image Gradient ◽  
Multi Scale ◽  
Novel Approach ◽  
Gradient Domain ◽  
Inherent Structure ◽  
Multi Scale Analysis

In this paper, a novel approach derived from image gradient domain called multi-scale gradient faces (MGF) is proposed to abstract multi-scale illumination-insensitive measure for face recognition. MGF applies multi-scale analysis on image gradient information, which can discover underlying inherent structure in images and keep the details at most while removing varying lighting. The proposed approach provides state-of-the-art performance on Extended YaleB and PIE: Recognition rates of 99.11% achieved on PIE database and 99.38% achieved on YaleB which outperforms most existing approaches. Furthermore, the experimental results on noised Yale-B validate that MGF is more robust to image noise.

scholarly journals Patch-Based Principal Component Analysis for Face Recognition

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Tai-Xiang Jiang ◽  
Ting-Zhu Huang ◽  
Xi-Le Zhao ◽  
Tian-Hui Ma
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Nearest Neighbor ◽  
Spatial Information ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Face Images ◽  
Pca Method ◽  
Local Spatial Information

We have proposed a patch-based principal component analysis (PCA) method to deal with face recognition. Many PCA-based methods for face recognition utilize the correlation between pixels, columns, or rows. But the local spatial information is not utilized or not fully utilized in these methods. We believe that patches are more meaningful basic units for face recognition than pixels, columns, or rows, since faces are discerned by patches containing eyes and noses. To calculate the correlation between patches, face images are divided into patches and then these patches are converted to column vectors which would be combined into a new “image matrix.” By replacing the images with the new “image matrix” in the two-dimensional PCA framework, we directly calculate the correlation of the divided patches by computing the total scatter. By optimizing the total scatter of the projected samples, we obtain the projection matrix for feature extraction. Finally, we use the nearest neighbor classifier. Extensive experiments on the ORL and FERET face database are reported to illustrate the performance of the patch-based PCA. Our method promotes the accuracy compared to one-dimensional PCA, two-dimensional PCA, and two-directional two-dimensional PCA.

scholarly journals A Novel Approach of Low-Light Image Denoising for Face Recognition

2014 ◽  
Vol 6 ◽  
pp. 256790
Author(s):  
Yimei Kang ◽  
Wang Pan
Keyword(s):  
Face Recognition ◽  
Image Denoising ◽  
Recognition Rate ◽  
Low Frequency ◽  
Frequency Noise ◽  
Low Frequency Noise ◽  
Face Database ◽  
Low Light ◽  
Very Low Frequency ◽  
Light Image

Illumination variation makes automatic face recognition a challenging task, especially in low light environments. A very simple and efficient novel low-light image denoising of low frequency noise (DeLFN) is proposed. The noise frequency distribution of low-light images is presented based on massive experimental results. The low and very low frequency noise are dominant in low light conditions. DeLFN is a three-level image denoising method. The first level denoises mixed noises by histogram equalization (HE) to improve overall contrast. The second level denoises low frequency noise by logarithmic transformation (LOG) to enhance the image detail. The third level denoises residual very low frequency noise by high-pass filtering to recover more features of the true images. The PCA (Principal Component Analysis) recognition method is applied to test recognition rate of the preprocessed face images with DeLFN. DeLFN are compared with several representative illumination preprocessing methods on the Yale Face Database B, the Extended Yale face database B, and the CMU PIE face database, respectively. DeLFN not only outperformed other algorithms in improving visual quality and face recognition rate, but also is simpler and computationally efficient for real time applications.

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