Face Recognition Based on Gabor Filter and Improved Linear Local Tangent Space Alignment Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 4091-4094
Author(s):  
Xue Yan Xu ◽  
Jiao Yu Liu ◽  
Yuan Shi ◽  
Tuo Deng
Keyword(s):  
Face Recognition ◽  
Tangent Space ◽  
Gabor Filter ◽  
Recognition Rate ◽  
Gabor Wavelet ◽  
Alignment Algorithm ◽  
Gabor Filtering ◽  
The Face ◽  
Secondary Feature ◽  
Local Tangent

In this paper, Gabor filtering and linear local tangent space alignment algorithm and its improved algorithm are used on face recognition. The Gabor wavelet transform can detect the image information in different directions and scales, according to its selective direction and frequency characteristics. The LLTSA reduces the dimension of the sample while the LLTSA and the other improved algorithms extract secondary feature. Experiment and analyze the average recognition rate of the LLTSA and its improved algorithms with the variation of dimension. The experiment results show the effectiveness of the method, increasing the face recognition accuracy.

Adaptive Discriminant Linear Local Tangent Space Alignment Algorithm on Face Recognition

2014 ◽  
Vol 989-994 ◽  
pp. 2381-2384
Author(s):  
Yuan Xing Lv ◽  
Yan Ni Deng ◽  
Yuan Shi ◽  
Qiang Li ◽  
Wen Peng
Keyword(s):  
Face Recognition ◽  
Tangent Space ◽  
Recognition Rate ◽  
Degree Of Polymerization ◽  
Local Geometry ◽  
Alignment Algorithm ◽  
Scatter Matrix ◽  
The Face ◽  
Local Tangent

This paper proposes an adaptive discriminant linear local tangent space alignment algorithm DALLTSA. On the basis of LLTSA algorithm adding adaptive and discriminant gets DALLTSA.DALLTSA not only combines characteristics in DLLTSA that maintain the local geometry and meets the maximum between-class scatter matrix, but also dynamically selects K-neighbor better to reflect the degree of polymerization between samples. Finally, the face recognition experiments based on Gabor [1] filter and DALLTSA shows that this algorithm improves the recognition rate and robustness.

Face recognition algorithm based on Gabor wavelet and locality preserving projections

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740041 ◽  
Author(s):  
Xiaojie Liu ◽  
Lin Shen ◽  
Honghui Fan
Keyword(s):  
Face Recognition ◽  
Gabor Filter ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Gabor Wavelet ◽  
Light Illumination ◽  
Locality Preserving Projections ◽  
Locality Preserving ◽  
Illumination Changes ◽  
The Face

In order to solve the effects of illumination changes and differences of personal features on the face recognition rate, this paper presents a new face recognition algorithm based on Gabor wavelet and Locality Preserving Projections (LPP). The problem of the Gabor filter banks with high dimensions was solved effectively, and also the shortcoming of the LPP on the light illumination changes was overcome. Firstly, the features of global image information were achieved, which used the good spatial locality and orientation selectivity of Gabor wavelet filters. Then the dimensions were reduced by utilizing the LPP, which well-preserved the local information of the image. The experimental results shown that this algorithm can effectively extract the features relating to facial expressions, attitude and other information. Besides, it can reduce influence of the illumination changes and the differences in personal features effectively, which improves the face recognition rate to 99.2%.

Fusion Algorithm Based on Improved Linear Local Tangent Space Alignment Algorithm

2014 ◽  
Vol 602-605 ◽  
pp. 3447-3450
Author(s):  
Wen Peng ◽  
Yan Ni Deng ◽  
Yuan Shi ◽  
Yuan Xing Lv ◽  
Qiang Li
Keyword(s):  
Tangent Space ◽  
Learning Algorithm ◽  
Recognition Rate ◽  
Alignment Algorithm ◽  
Redundant Information ◽  
Fusion Algorithm ◽  
Scatter Matrix ◽  
Neighborhood Selection ◽  
Adaptive Neighborhood ◽  
Local Tangent

A uncorrelated adaptive discriminant linear local tangent space alignment (UDALLTSA) is proposed based on improved linear local tangent space alignment algorithm. The algorithm uses an adaptive neighborhood selection to select the appropriate neighborhood, and introduces curvature to amend the model, modifies the constraints of the objective function by use inter-class scatter matrix, and constraints on basis vectors to compute the best projection matrix. By comparing the results of the experiments, it shows that after integrating the discriminant information into the algorithm , uncorrelated constraints and adaptive neighborhood selection can well improve the recognition rate and robustness, thus, possessing good noise immunity, and eliminating redundant information of base vectors, make this fusion algorithm a supervised learning algorithm.

scholarly journals Deeply Learned Pose Invariant Image Analysis with Applications in 3D Face Recognition

2019 ◽  
Vol 2019 ◽  
pp. 1-21 ◽  
Author(s):  
Naeem Ratyal ◽  
Imtiaz Ahmad Taj ◽  
Muhammad Sajid ◽  
Anzar Mahmood ◽  
Sohail Razzaq ◽  
...  
Keyword(s):  
Face Recognition ◽  
Superior Performance ◽  
Support Vector ◽  
Complex Nature ◽  
Alignment Algorithm ◽  
3D Face Recognition ◽  
3D Face ◽  
Norm Minimization ◽  
Wide Range ◽  
The Face

Face recognition aims to establish the identity of a person based on facial characteristics and is a challenging problem due to complex nature of the facial manifold. A wide range of face recognition applications are based on classification techniques and a class label is assigned to the test image that belongs to the unknown class. In this paper, a pose invariant deeply learned multiview 3D face recognition approach is proposed and aims to address two problems: face alignment and face recognition through identification and verification setups. The proposed alignment algorithm is capable of handling frontal as well as profile face images. It employs a nose tip heuristic based pose learning approach to estimate acquisition pose of the face followed by coarse to fine nose tip alignment using L2 norm minimization. The whole face is then aligned through transformation using knowledge learned from nose tip alignment. Inspired by the intrinsic facial symmetry of the Left Half Face (LHF) and Right Half Face (RHF), Deeply learned (d) Multi-View Average Half Face (d-MVAHF) features are employed for face identification using deep convolutional neural network (dCNN). For face verification d-MVAHF-Support Vector Machine (d-MVAHF-SVM) approach is employed. The performance of the proposed methodology is demonstrated through extensive experiments performed on four databases: GavabDB, Bosphorus, UMB-DB, and FRGC v2.0. The results show that the proposed approach yields superior performance as compared to existing state-of-the-art methods.

scholarly journals Incremental Graph Regulated Nonnegative Matrix Factorization for Face Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Zhe-Zhou Yu ◽  
Yu-Hao Liu ◽  
Bin Li ◽  
Shu-Chao Pang ◽  
Cheng-Cheng Jia
Keyword(s):  
Face Recognition ◽  
Matrix Factorization ◽  
Information Needs ◽  
Nonnegative Matrix Factorization ◽  
Recognition Rate ◽  
Nonnegative Matrix ◽  
Recognition Algorithm ◽  
Incremental Method ◽  
Real World Application ◽  
The Face

In a real world application, we seldom get all images at one time. Considering this case, if a company hired an employee, all his images information needs to be recorded into the system; if we rerun the face recognition algorithm, it will be time consuming. To address this problem, In this paper, firstly, we proposed a novel subspace incremental method called incremental graph regularized nonnegative matrix factorization (IGNMF) algorithm which imposes manifold into incremental nonnegative matrix factorization algorithm (INMF); thus, our new algorithm is able to preserve the geometric structure in the data under incremental study framework; secondly, considering we always get many face images belonging to one person or many different people as a batch, we improved our IGNMF algorithms to Batch-IGNMF algorithms (B-IGNMF), which implements incremental study in batches. Experiments show that (1) the recognition rate of our IGNMF and B-IGNMF algorithms is close to GNMF algorithm while it runs faster than GNMF. (2) The running times of our IGNMF and B-IGNMF algorithms are close to INMF while the recognition rate outperforms INMF. (3) Comparing with other popular NMF-based face recognition incremental algorithms, our IGNMF and B-IGNMF also outperform then both the recognition rate and the running time.

A Robust and Efficient Face Recognition System Based on Luminance Distribution Using Maximum Likelihood

2012 ◽  
Vol 241-244 ◽  
pp. 1705-1709
Author(s):  
Ching Tang Hsieh ◽  
Chia Shing Hu
Keyword(s):  
Face Recognition ◽  
Maximum Likelihood ◽  
Recognition Rate ◽  
Computation Time ◽  
Likelihood Estimation ◽  
Recognition System ◽  
Luminance Distribution ◽  
Face Region ◽  
Face Recognition System ◽  
The Face

In this paper, a robust and efficient face recognition system based on luminance distribution by using maximum likelihood estimation is proposed. The distribution of luminance components of the face region is acquired and applied to maximum likelihood test for face matching. The experimental results showed that the proposed method has a high recognition rate and requires less computation time.

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.

scholarly journals A New Local Descriptor Based on Strings for Face Recognition

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Hicham Zaaraoui ◽  
Abderrahim Saaidi ◽  
Rachid El Alami ◽  
Mustapha Abarkan
Keyword(s):  
Face Recognition ◽  
Nearest Neighbor ◽  
Recognition Rate ◽  
Levenshtein Distance ◽  
Local Descriptor ◽  
Chain Code ◽  
Visual Words ◽  
The Face ◽  
Face Descriptor ◽  
Neighbor Classifier

This paper proposes the use of strings as a new local descriptor for face recognition. The face image is first divided into nonoverlapping subregions from which the strings (words) are extracted using the principle of chain code algorithm and assigned into the nearest words in a dictionary of visual words (DoVW) with the Levenshtein distance (LD) by applying the bag of visual words (BoVW) paradigm. As a result, each region is represented by a histogram of dictionary words. The histograms are then assembled as a face descriptor. Our methodology depends on the path pursued from a starting pixel and do not require a model as the other approaches from the literature. Therefore, the information of the local and global properties of an object is obtained. The recognition is performed by using the nearest neighbor classifier with the Hellinger distance (HD) as a comparison between feature vectors. The experimental results on the ORL and Yale databases demonstrate the efficiency of the proposed approach in terms of preserving information and recognition rate compared to the existing face recognition methods.

Application of Gabor Dictionary and SL0 Norm in Disguise Face Recognition

2013 ◽  
Vol 278-280 ◽  
pp. 1211-1214
Author(s):  
Jun Ying Zeng ◽  
Jun Ying Gan ◽  
Yi Kui Zhai
Keyword(s):  
Face Recognition ◽  
Function Approximation ◽  
Multiple Scales ◽  
Recognition Rate ◽  
Principal Component ◽  
Facial Feature ◽  
Training Image ◽  
Recognition Algorithm ◽  
The Face ◽  
Gabor Dictionary

A fast sparse representation face recognition algorithm based on Gabor dictionary and SL0 norm is proposed in this paper. The Gabor filters, which could effectively extract local directional features of the image at multiple scales, are less sensitive to variations of illumination, expression and camouflage. SL0 algorithm, with the advantages of calculation speed，require fewer measurement values by continuously differentiable function approximation L0 norm and reconstructed sparse signal by minimizing the approximate L0 norm. The algorithm obtain the local feature face by extracting the Gabor face feature, reduce the dimensions by principal component analysis, fast sparse classify by the SL0 norm. Under camouflage condition, The algorithm block the Gabor facial feature and improve the speed of formation of the Gabor dictionary. The experimental results on AR face database show that the proposed algorithm can improve recognition speed and recognition rate to some extent and can generalize well to the face recognition, even with a few training image per class.

The Safety Research for Face Recognition System Based on Image Feature and Digital Watermarking

2012 ◽  
Vol 224 ◽  
pp. 485-488
Author(s):  
Fei Li ◽  
Yuan Yuan Wang
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Recognition System ◽  
Face Image ◽  
Image Feature ◽  
Safety Research ◽  
The Face ◽  
Information Image ◽  
Image Integrity ◽  
Recognition Software

Abstract: In order to solve the easily copied problem of images in face recognition software, an algorithm combining the image feature with digital watermark is presented in this paper. As watermark information, image feature of the adjacent blocks are embedded to the face image. And primitive face images are not needed when recovering the watermark. So face image integrity can be well confirmed, and the algorithm can detect whether the face image is the original one and identify whether the face image is attacked by malicious aim-such as tampering, replacing or illegally adding. Experimental results show that the algorithm with good invisibility and excellent robustness has no interference on face recognition rate, and it can position the specific tampered location of human face image.

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