scholarly journals A Curvelet Domain Face Recognition Scheme Based on Local Dominant Feature Extraction

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Hafiz Imtiaz ◽  
Shaikh Anowarul Fattah
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Selection Criterion ◽  
Curvelet Transform ◽  
Principal Component ◽  
Face Image ◽  
Spatial Variations ◽  
Threshold Criterion ◽  
Dominant Feature ◽  
The Face

A feature extraction algorithm is introduced for face recognition, which efficiently exploits the local spatial variations in a face image utilizing curvelet transform. Although multi-resolution ideas have been profusely employed for addressing face recognition problems, theoretical studies indicate that digital curvelet transform is an even better method due to its directional properties. Instead of considering the entire face image, an entropy-based local band selection criterion is developed for feature extraction, which selects high-informative horizontal bands from the face image. These bands are segmented into several small spatial modules to capture the local spatial variations precisely. The effect of modularization in terms of the entropy content of the face images has been investigated. Dominant curvelet transform coefficients corresponding to each local region residing inside the horizontal bands are selected, based on the proposed threshold criterion, as features, which not only drastically reduces the feature dimension but also provides high within-class compactness and high between-class separability. A principal component analysis is performed to further reduce the dimensionality of the feature space. Extensive experimentation is carried out upon standard face databases and a very high degree of recognition accuracy is achieved even with a simple Euclidean distance based classifier.

scholarly journals A Wavelet-Domain Local Dominant Feature Selection Scheme for Face Recognition

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Hafiz Imtiaz ◽  
Shaikh Anowarul Fattah
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Face Image ◽  
Spatial Variations ◽  
Discrete Wavelet ◽  
Selection Scheme ◽  
Threshold Criterion ◽  
Dominant Feature ◽  
The Face ◽  
Horizontal Band

A multiresolution feature extraction algorithm for face recognition is proposed based on two-dimensional discrete wavelet transform (2D-DWT), which efficiently exploits the local spatial variations in a face image. For feature extraction, instead of considering the entire face image, an entropy-based local band selection criterion is developed, which selects high-informative horizontal segments from the face image. In order to capture the local spatial variations within these bands precisely, the horizontal band is segmented into several small spatial modules. The effect of modularization in terms of the entropy content of the face images has been investigated. Dominant wavelet coefficients corresponding to each module residing inside those bands are selected as features. A histogram-based threshold criterion is proposed to select dominant coefficients, which drastically reduces the feature dimension and provides high within-class compactness and high between-class separability. The effect of using different mother wavelets for the purpose of feature extraction has been also investigated. PCA is performed to further reduce the dimensionality of the feature space. Extensive experimentation is carried out upon standard face databases, and a very high degree of recognition accuracy is achieved by the proposed method in comparison to those obtained by some of the existing methods.

Matrix Based Feature Measurement and Extraction for Face Recognition

2015 ◽  
Vol 738-739 ◽  
pp. 643-647
Author(s):  
Qi Zhu ◽  
Jin Rong Cui ◽  
Zi Zhu Fan
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Face Recognition ◽  
Measurement Method ◽  
Principal Component ◽  
Component Analysis ◽  
High Accuracy ◽  
Face Image ◽  
Two Dimensional ◽  
The Matrix

In this paper, a matrix based feature extraction and measurement method, i.e.: multi-column principle component analysis (MCPCA) is used to directly and effectively extract features from the matrix. We analyze the advantages of MCPCA over the conventional principal component analysis (PCA) and two-dimensional PCA (2DPCA), and we have successfully applied it into face image recognition. Extensive face recognition experiments illustrate that the proposed method obtains high accuracy, and it is more robust than previous conventional face recognition methods.

Research of Face Recognition Methods Based on Binding Feature Extraction

2014 ◽  
Vol 568-570 ◽  
pp. 668-671
Author(s):  
Yi Long ◽  
Fu Rong Liu ◽  
Guo Qing Qiu
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Extraction Method ◽  
Local Binary Pattern ◽  
Feature Vector ◽  
Recognition Rate ◽  
Principal Component ◽  
Face Image ◽  
Feature Extraction Method ◽  
Histogram Feature

To address the problem that the dimension of the feature vector extracted by Local Binary Pattern (LBP) for face recognition is too high and Principal Component Analysis (PCA) extract features are not the best classification features, an efficient feature extraction method using LBP, PCA and Maximum scatter difference (MSD) has been introduced in this paper. The original face image is firstly divided into sub-images, then the LBP operator is applied to extract the histogram feature. and the feature dimensions are further reduced by using PCA. Finally,MSD is performed on the reduced PCA-based feature.The experimental results on ORL and Yale database demonstrate that the proposed method can classify more effectively and can get higher recognition rate than the traditional recognition methods.

COMPARATIVE ASSESSMENT OF CONTENT-BASED FACE IMAGE RETRIEVAL IN DIFFERENT COLOR SPACES

2005 ◽  
Vol 19 (07) ◽  
pp. 873-893 ◽  
Author(s):  
PEICHUNG SHIH ◽  
CHENGJUN LIU
Keyword(s):  
Face Recognition ◽  
Image Retrieval ◽  
Color Space ◽  
Principal Component ◽  
Face Image ◽  
Color Images ◽  
Grand Challenge ◽  
Color Spaces ◽  
The Face ◽  
Different Color

Content-based face image retrieval is concerned with computer retrieval of face images (of a given subject) based on the geometric or statistical features automatically derived from these images. It is well known that color spaces provide powerful information for image indexing and retrieval by means of color invariants, color histogram, color texture, etc. This paper assesses comparatively the performance of content-based face image retrieval in different color spaces using a standard algorithm, the Principal Component Analysis (PCA), which has become a popular algorithm in the face recognition community. In particular, we comparatively assess 12 color spaces (RGB, HSV, YUV, YCbCr, XYZ, YIQ, L*a*b*, U*V*W*, L*u*v*, I1I2I3, HSI, and rgb) by evaluating seven color configurations for every single color space. A color configuration is defined by an individual or a combination of color component images. Take the RGB color space as an example, possible color configurations are R, G, B, RG, RB, GB and RGB. Experimental results using 600 FERET color images corresponding to 200 subjects and 456 FRGC (Face Recognition Grand Challenge) color images of 152 subjects show that some color configurations, such as YV in the YUV color space and YI in the YIQ color space, help improve face retrieval performance.

The Research of Face Recognition Method Based on Wavelet Transform

2012 ◽  
Vol 442 ◽  
pp. 463-467
Author(s):  
Li Hong Bi ◽  
Yan Fang Ma ◽  
Li Hua Piao
Keyword(s):  
Face Recognition ◽  
Wavelet Transform ◽  
Principal Component ◽  
Face Image ◽  
Biometric Identification ◽  
Recognition Method ◽  
Image Block ◽  
The Face ◽  
Speed And Accuracy ◽  
Great Development

Face recognition is a kind of biometric identification technology possessing great development potential, researching on this technology has great theoretical values. The paper presents a method of image block principal component analysis (PCA) based on wavelet transform. The image was firstly disposed by wavelet transform and segmented, then we set the different weight values for each of parts according to the different role in the overall image and extract eigenvector using the PCA, finally, the face image is recognized according to the eigenvector and feature. This method can improve the speed and accuracy, reduce the complexity of feature extraction and improve the speed of recognition.

3D Face Recognition using an Adaptive Non-Uniform Face Mesh

2011 ◽  
pp. 562-573
Author(s):  
Wei Jen Chew ◽  
Kah Phooi Seng ◽  
Li-Minn Ang
Keyword(s):  
Face Recognition ◽  
Principal Component ◽  
Recognition System ◽  
Face Image ◽  
Uniform Mesh ◽  
Range Image ◽  
3D Face Recognition ◽  
Linear Discriminant ◽  
3D Face ◽  
The Face

Face recognition using 3D faces has become widely popular in the last few years due to its ability to overcome recognition problems encountered by 2D images. An important aspect to a 3D face recognition system is how to represent the 3D face image. In this chapter, it is proposed that the 3D face image be represented using adaptive non-uniform meshes which conform to the original range image. Basically, the range image is converted to meshes using the plane fitting method. Instead of using a mesh with uniform sized triangles, an adaptive non-uniform mesh was used instead to reduce the amount of points needed to represent the face. This is because some parts of the face have more contours than others, hence requires a finer mesh. The mesh created is then used for face recognition purposes, using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Simulation results show that an adaptive non-uniform mesh is able to produce almost similar recognition rates compared to uniform meshes but with significant reduction in number of vertices.

A Face Recognition Algorithm Based on Intermediate Layers Connected by the CNN

2021 ◽  
Author(s):  
Yuxiang Long
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Network Model ◽  
Recognition Algorithm ◽  
Image Features ◽  
Face Image ◽  
Face Features ◽  
Intermediate Layers ◽  
Training Samples ◽  
The Face

Face recognition is difficult due to the higher dimension of face image features and fewer training samples. Firstly, in order to improve the performance of feature extraction, we inventively construct a double hierarchical network structure convolution neural network (CNN) model. The front-end network adopts a relatively simple network model to achieve rough feature extraction from input images and obtain multiple suspect face candidate windows. The back-end network uses a relatively complex network model to filter the best detection window and return the face size and position by nonmaximum suppression. Then, in order to fully extract the face features in the optimal window, a face recognition algorithm based on intermediate layers connected by the deep CNN is proposed in this paper. Based on AlexNet, the front, intermediate and end convolution layers are combined by deep connection. Then, the feature vector describing the face image is obtained by the operation of the pooling layer and the full connection layer. Finally, the auxiliary classifier training method is used to train the model to ensure the effectiveness of the features of the intermediate layer. Experimental results based on open face database show that the recognition accuracy of the proposed algorithm is higher than that of other face recognition algorithms compared in this paper.

scholarly journals FACE RECOGNITION THROUGH CASCADE CLASSIFIER USING EIGENFACES ALGORITHMS.

2020 ◽  
Vol 9 (6) ◽  
pp. 39-47
Keyword(s):  
Face Recognition ◽  
Dimensional Space ◽  
Principal Component ◽  
Face Image ◽  
Human Face ◽  
Cascade Classifier ◽  
Facial Image ◽  
Human Face Recognition ◽  
Cascade Classifiers ◽  
The Face

The easiest way to distinguish each person's identity is through the face. Face recognition is included as an inevitable pre-processing step for face recognition. Face recognition itself has to face difficulties and challenges because sometimes some form of issue is quite different from human face recognition. There are two stages used for the human face recognition process, i.e. face detection, where this process is very fast in humans. In the first phase, the person stored the face image in the database from a different angle. The person's face image storage with the help of Eigenvector value depended on components - face coordinates, face index, face angles, eyes, nose, lips, and mouth within certain distances and positions with each other. There are two types of methods that are popular in currently developed face recognition patterns, the Cascade Classifier method and the Eigenface Algorithm. Facial image recognition The Eigenface method is based on the lack of dimensional space of the face, using principal component analysis for facial features. The main purpose of the use of cascade classifiers on facial recognition using the Eigenface Algorithm was made by finding the eigenvectors corresponding to the largest eigenvalues of the facial image

scholarly journals Face Detection and Recognition using Open CV Based on Fisher Faces Algorithm

2020 ◽  
Vol 8 (5) ◽  
pp. 1204-1208
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Face Detection ◽  
Face Image ◽  
Image Feature ◽  
Learning Stage ◽  
Identity Recognition ◽  
Video Frames ◽  
Testing Stage ◽  
The Face

Facial Recognition represents the event of a system which may determine the person with the assistance of a face using Computer Vision (Open CV). Face recognition is employed within the fields of Identity Recognition, police investigation and enforcement. It's a method of characteristic someone supported facial expression. This method is enforced in 2 stages. They're the training stage and therefore the testing stage. This study primarily consists of 3 elements, specifically face detection from the image, feature extraction and storing many reminder images, and recognition. Face finding rule is employed to detect the face from the given image. The foremost helpful and distinctive options of the face image are extracted within the feature extraction part. Face Detection may be challenging because of pictures and video frames will contain advanced background, completely different head poses and occlusion like carrying glasses or scarf. It presents a rule for finding face recognition downside and concatenated into one feature vector that is employed to coach the system to recognise among the prevailing photos with it. Within the testing stage the system takes the face of the image of someone for recognition. Image acquisition, preprocessing, image filtering, feature extraction is just like the learning stage. For classification the options are fed to the trained system. The algorithms can determine the face image from the content and acknowledges it.

scholarly journals Research on Face Image Digital Processing and Recognition Based on Data Dimensionality Reduction Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ming He
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Environmental Factors ◽  
Dimensionality Reduction ◽  
Digital Processing ◽  
Face Image ◽  
Reduction Algorithm ◽  
Data Dimensionality Reduction ◽  
The Face ◽  
Image Digital Processing

Because face recognition is greatly affected by external environmental factors and the partial lack of face information challenges the robustness of face recognition algorithm, while the existing methods have poor robustness and low accuracy in face image recognition, this paper proposes a face image digital processing and recognition based on data dimensionality reduction algorithm. Based on the analysis of the existing data dimensionality reduction and face recognition methods, according to the face image input, feature composition, and external environmental factors, the face recognition and processing technology flow is given, and the face feature extraction method is proposed based on nonparametric subspace analysis (NSA). Finally, different methods are used to carry out comparative experiments in different face databases. The results show that the method proposed in this paper has a higher correct recognition rate than the existing methods and has an obvious effect on the XM2VTS face database. This method not only improves the shortcomings of existing methods in dealing with complex face images but also provides a certain reference for face image feature extraction and recognition in complex environment.

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