An Improved Block 2DPCA Face Recognition Algorithm with L1-Norm

2012 ◽  
Vol 457-458 ◽  
pp. 1077-1082 ◽  
Author(s):  
Hao Zheng ◽  
Lei Pan
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Recognition Algorithm ◽  
Experimental Results ◽  
Frobenius Norm ◽  
Face Database ◽  
L1 Norm

Most of the existing Block 2DPCA algorithms are based on Frobenius norm, which makes them sensitive to outliers. In this paper, we propose a new improved Block 2DPCA algorithm with L1-norm, which is robust to outliers. The experimental results of FERET face database indicated that the recognition performance of new algorithm is superior to Block 2DPCA, more robust than Block 2DPCA.

Face Recognition Algorithm Based on Kernel Collaborative Representation

2013 ◽  
Vol 756-759 ◽  
pp. 3590-3595
Author(s):  
Liang Zhang ◽  
Ji Wen Dong
Keyword(s):  
Face Recognition ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Recognition Algorithm ◽  
Experimental Results ◽  
Collaborative Representation ◽  
Nonlinear Structures ◽  
Components Analysis ◽  
Kernel Principal Components Analysis ◽  
The Relationship

Aiming at solving the problems of occlusion and illumination in face recognition, a new method of face recognition based on Kernel Principal Components Analysis (KPCA) and Collaborative Representation Classifier (CRC) is developed. The KPCA can obtain effective discriminative information and reduce the feature dimensions by extracting faces nonlinear structures features, the decisive factor. Considering the collaboration among the samples, the CRC which synthetically consider the relationship among samples is used. Experimental results demonstrate that the algorithm obtains good recognition rates and also improves the efficiency. The KCRC algorithm can effectively solve the problem of illumination and occlusion in face recognition.

scholarly journals On the Robustness of Face Recognition Algorithms Against Attacks and Bias

2020 ◽  
Vol 34 (09) ◽  
pp. 13583-13589
Author(s):  
Richa Singh ◽  
Akshay Agarwal ◽  
Maneet Singh ◽  
Shruti Nagpal ◽  
Mayank Vatsa
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
Recognition Algorithm ◽  
Future Research ◽  
Research Directions ◽  
Recognition Algorithms ◽  
Future Research Directions ◽  
And Gender ◽  
Gender Variations ◽  
Very High

Face recognition algorithms have demonstrated very high recognition performance, suggesting suitability for real world applications. Despite the enhanced accuracies, robustness of these algorithms against attacks and bias has been challenged. This paper summarizes different ways in which the robustness of a face recognition algorithm is challenged, which can severely affect its intended working. Different types of attacks such as physical presentation attacks, disguise/makeup, digital adversarial attacks, and morphing/tampering using GANs have been discussed. We also present a discussion on the effect of bias on face recognition models and showcase that factors such as age and gender variations affect the performance of modern algorithms. The paper also presents the potential reasons for these challenges and some of the future research directions for increasing the robustness of face recognition models.

Bayesian Robust Coding Face Recognition Algorithm Based on New Dictionary

2014 ◽  
Vol 644-650 ◽  
pp. 4080-4083
Author(s):  
Ye Cai Guo ◽  
Ling Hua Zhang
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Binary Image ◽  
Test Image ◽  
Face Database ◽  
Robust Coding ◽  
The Face ◽  
Isolated Points ◽  
Rate Comparison

In order to overcome the defects that the face recognition rate can be greatly reduced in the existing uncontrolled environments, Bayesian robust coding for face recognition based on new dictionary was proposed. In this proposed algorithm, firstly a binary image is gained by gray threshold transformation and a more clear image without some isolated points can be obtained via smoothing, secondly a new dictionary can be reconstructed via fusing the binary image with the original training dictionary, finally the test image can be classified as the existing class via Bayesian robust coding. The experimental results based on AR face database show that the proposed algorithm has higher face recognition rate comparison with RRC and RSC algorithm.

3D Face Recognition Based on Regional Shape Maps

2018 ◽  
Vol 22 (1) ◽  
pp. 141-146 ◽  
Author(s):  
Xiaoni Wang ◽  
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
3D Face Recognition ◽  
Face Database ◽  
Registration Method ◽  
3D Face ◽  
Feature Vectors ◽  
Input Face ◽  
Point To Point ◽  
3D Face Database

This study proposes an iterative closest shape point (ICSP) registration method based on regional shape maps for 3D face recognition. A neutral expression image randomly selected from a face database is considered as the reference face. The point-to-point correspondences between the input face and the reference face are achieved by constructing the points’ regional shape maps. The distance between corresponding point pairs is then minimized by iterating through the correspondence findings and coordinate transformations. The vectors composed of the closest shape points obtained in the last iteration are regarded as the feature vectors of the input face. These 3D face feature vectors are finally used for both training and recognition using the Fisherface method. Experiments are conducted using the 3D face database maintained by the Chinese Academy of Science Institute of Automation (CASIA). The results show that the proposed method can effectively improve 3D face recognition performance.

Research on Importance of Texture Information in Face Recognition

2013 ◽  
Vol 278-280 ◽  
pp. 1193-1196 ◽  
Author(s):  
Yong Gao Jin ◽  
Cheng Zhe Xu
Keyword(s):  
Face Recognition ◽  
Face Image ◽  
Experimental Results ◽  
Face Database ◽  
Illumination Variation ◽  
Illumination Normalization ◽  
Skin Texture ◽  
Shape Information ◽  
Texture Information

This paper presents importance of skin texture information in face recognition. To this end, we perform the illumination normalization on face image in order to extract texture information unaffected by illumination variation. And then apply mask image on each illumination normalized face image to obtain the corresponding texture data, which hardly contain the shape information. Face recognition experiments are carried out by using texture data. Experimental results on Yale face database B and CMU PIE database show that the texture information has considerable ability to distinguish subjects in face recognition.

Two-Dimensional Locality Discriminant Preserving Projections for Face Recognition

2010 ◽  
Vol 121-122 ◽  
pp. 391-398 ◽  
Author(s):  
Qi Rong Zhang ◽  
Zhong Shi He
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Image Feature ◽  
Two Dimensional ◽  
Face Database ◽  
Image Feature Extraction ◽  
Locality Preserving Projection ◽  
Locality Preserving

In this paper, we propose a new face recognition approach for image feature extraction named two-dimensional locality discriminant preserving projections (2DLDPP). Two-dimensional locality preserving projections (2DLPP) can direct on 2D image matrixes. So, it can make better recognition rate than locality preserving projection. We investigate its more. The 2DLDPP is to use modified maximizing margin criterion (MMMC) in 2DLPP and set the parameter optimized to maximize the between-class distance while minimize the within-class distance. Extensive experiments are performed on ORL face database and FERET face database. The 2DLDPP method achieves better face recognition performance than PCA, 2DPCA, LPP and 2DLPP.

scholarly journals Modified SIFT Descriptors for Face Recognition under Different Emotions

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Nirvair Neeru ◽  
Lakhwinder Kaur
Keyword(s):  
Face Recognition ◽  
Recognition Performance ◽  
User Interaction ◽  
Recognition Algorithm ◽  
Scale Invariant ◽  
Distance Ratio ◽  
Japanese Female ◽  
Database Size ◽  
Fully Automatic ◽  
Scale Invariant Feature

The main goal of this work is to develop a fully automatic face recognition algorithm. Scale Invariant Feature Transform (SIFT) has sparingly been used in face recognition. In this paper, a Modified SIFT (MSIFT) approach has been proposed to enhance the recognition performance of SIFT. In this paper, the work is done in three steps. First, the smoothing of the image has been done using DWT. Second, the computational complexity of SIFT in descriptor calculation is reduced by subtracting average from each descriptor instead of normalization. Third, the algorithm is made automatic by using Coefficient of Correlation (CoC) instead of using the distance ratio (which requires user interaction). The main achievement of this method is reduced database size, as it requires only neutral images to store instead of all the expressions of the same face image. The experiments are performed on the Japanese Female Facial Expression (JAFFE) database, which indicates that the proposed approach achieves better performance than SIFT based methods. In addition, it shows robustness against various facial expressions.

scholarly journals Efficient Gabor Phase Based Illumination Invariant for Face Recognition

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Chunnian Fan ◽  
Shuiping Wang ◽  
Hao Zhang
Keyword(s):  
Face Recognition ◽  
Extraction Method ◽  
Experimental Results ◽  
Gabor Wavelet ◽  
Image Transformation ◽  
Face Database ◽  
Illumination Variation ◽  
Illumination Invariant ◽  
Face Images

This paper presents a novel Gabor phase based illumination invariant extraction method aiming at eliminating the effect of varying illumination on face recognition. Firstly, It normalizes varying illumination on face images, which can reduce the effect of varying illumination to some extent. Secondly, a set of 2D real Gabor wavelet with different directions is used for image transformation, and multiple Gabor coefficients are combined into one whole in considering spectrum and phase. Lastly, the illumination invariant is obtained by extracting the phase feature from the combined coefficients. Experimental results on the Yale B and the CMU PIE face database show that our method obtained a significant improvement over other related methods for face recognition under large illumination variation condition.

A FACE RECOGNITION SCHEME BASED ON EMBEDDED HIDDEN MARKOV MODEL AND SELECTIVE ENSEMBLE STRATEGY

2009 ◽  
Vol 09 (03) ◽  
pp. 355-367
Author(s):  
XINBO GAO ◽  
JINXIU LI ◽  
BING XIAO
Keyword(s):  
Face Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Recognition Performance ◽  
Recognition Algorithm ◽  
Model Parameters ◽  
Ensemble Strategy ◽  
Selective Ensemble ◽  
Selection Of

As an effective method, the embedded hidden Markov model (E-HMM) has been widely used in pattern recognition. On applying the E-HMM to face recognition, the performance heavily depends on the selection of model parameters. Aiming at the problem of model selection, a selective ensemble of multi E-HMMs based face recognition algorithm is proposed. Experimental results illustrate that compared with the traditional E-HMM based face recognition algorithm the proposed method cannot only obtain better and more stable recognition performance, but also achieve higher generalization ability.

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