scholarly journals STUDY OF TWO 3D FACE REPRESENTATION ALGORITHMS USING RANGE IMAGE AND CURVATURE-BASED REPRESENTATIONS

2014 ◽  
pp. 42-49
Author(s):  
Agata Manolova ◽  
Krasimir Tonchev
Keyword(s):  
Image Representation ◽  
Recognition Rate ◽  
Principal Component ◽  
Range Image ◽  
Subspace Projection ◽  
Linear Discriminant ◽  
3D Face ◽  
Face Features ◽  
The Face ◽  
Image Representations

In this paper we present a comparative analysis of two algorithms for image representation with application to recognition of 3D face scans with the presence of facial expressions. We begin with processing of the input point cloud based on curvature analysis and range image representation to achieve a unique representation of the face features. Then, subspace projection using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) is performed. Finally classification with different classifiers will be performed over the 3D face scans dataset with 61 subject with 7 scans per subject (427 scans), namely two "frontal", one "look-up", one "look-down", one "smile", one "laugh", one "random expression". The experimental results show a high recognition rate for the chosen database. They demonstrate the effectiveness of the proposed 3D image representations and subspace projection for 3D face 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.

scholarly journals Research on Face Recognition Based on Embedded System

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Hong Zhao ◽  
Xi-Jun Liang ◽  
Peng Yang
Keyword(s):  
Face Recognition ◽  
Embedded System ◽  
High Performance ◽  
Recognition Rate ◽  
Principal Component ◽  
Image Feature ◽  
Stable Operation ◽  
Embedded Platform ◽  
Face Features ◽  
The Face

Because a number of image feature data to store, complex calculation to execute during the face recognition, therefore the face recognition process was realized only by PCs with high performance. In this paper, the OpenCV facial Haar-like features were used to identify face region; the Principal Component Analysis (PCA) was employed in quick extraction of face features and the Euclidean Distance was also adopted in face recognition; as thus, data amount and computational complexity would be reduced effectively in face recognition, and the face recognition could be carried out on embedded platform. Finally, based on Tiny6410 embedded platform, a set of embedded face recognition systems was constructed. The test results showed that the system has stable operation and high recognition rate can be used in portable and mobile identification and authentication.

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.

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.

Face Recognition by PCA and Improved LBP Fusion Algorithm

2015 ◽  
Vol 734 ◽  
pp. 562-567 ◽  
Author(s):  
En Zeng Dong ◽  
Yan Hong Fu ◽  
Ji Gang Tong
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Texture Feature ◽  
Principal Component ◽  
Texture Features ◽  
Rotation Invariant ◽  
Fusion Algorithm ◽  
Face Images ◽  
The Face ◽  
Pca Method

This paper proposed a theoretically efficient approach for face recognition based on principal component analysis (PCA) and rotation invariant uniform local binary pattern texture features in order to weaken the effects of varying illumination conditions and facial expressions. Firstly, the rotation invariant uniform LBP operator was adopted to extract the local texture feature of the face images. Then PCA method was used to reduce the dimensionality of the extracted feature and get the eigenfaces. Finally, the nearest distance classification was used to distinguish each face. The method has been accessed on Yale and ATR-Jaffe face databases. Results demonstrate that the proposed method is superior to standard PCA and its recognition rate is higher than the traditional PCA. And the proposed algorithm has strong robustness against the illumination changes, pose, rotation and expressions.

scholarly journals Study on discrimination of different grades of rapeseed oil based on flavor fingerprint

2020 ◽  
Author(s):  
Tong Chen ◽  
Chunyou Liu ◽  
Bin Chen ◽  
Yongchun Huang
Keyword(s):  
Rapeseed Oil ◽  
Solid Phase ◽  
Oil Quality ◽  
Recognition Rate ◽  
Principal Component ◽  
Internal Quality ◽  
Extraction Technology ◽  
Linear Discriminant ◽  
Discriminant Model ◽  
The Relationship

AbstractIn this work, Gas chromatograph-Mass Spectrometry (GC-MS) combined with solid phase micro-extraction technology was used to analyze the difference of volatile organic compounds (VOCs) in rapeseed oil of different grades, and the relationship between changes of VOCs and refining process were also investigated in order to construct a non-linear model, which could realize rapid and accurate discrimination of different grade rapeseed oils. 124 rapeseed oil samples with different grades were collected and analyzed by GC-MS technology and 55 VOCs were identified and selected as variables to characterize the internal quality information of rapeseed oils. Then, principal component analysis (PCA) method was used to extract useful features and reduce data dimensionality, and finally a discriminant model was built using linear discriminant analysis (LDA) algorithm. The correct recognition rate of sample set was close to 94.59%. The results showed that the proposed method is promising in discriminating different grades of vegetable oils. Besides, it provides a theoretical basis for studying the relationship between VOCs composition and vegetable oil quality.

scholarly journals 3D‐Facial Expression Synthesis and its Application to Face Recognition Systems

2009 ◽  
Vol 7 (03) ◽  
Author(s):  
Leonel Ramí­rez-Valdez ◽  
Rogelio Hasimoto-Beltran
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Range Image ◽  
Fatty Tissue ◽  
Face Model ◽  
3D Face ◽  
Face Expression ◽  
Recognition Systems ◽  
Synthesis Approach ◽  
Neutral Expression

One of the main problems in Face Recognition systems is the recognition of an input face with a different expression than the available in the training database. In this work, we propose a new 3D‐face expression synthesis approach for expression independent face recognition systems (FRS). Different than current schemes in the literature, all the steps involved in our approach (face denoising, registration, and expression synthesis) are performed in the 3D domain. Our final goal is to increase the flexibility of 3D‐FRS by allowing them to artificially generate multiple face expressions from a neutral expression face. A generic 3D‐range image is modeled by the Finite Element Method with three simplified layers representing the skin, fatty tissue and the cranium. The face muscular anatomy is superimposed to the 3D model for the synthesis of expressions. Our approach can be divided into three main steps: Denoising Algorithm, which is applied to remove long peaks present in the original 3Dface samples; Automatic Control Points Detection, to detect particular facial landmarks such as eye and mouth corners, nose tip, etc., helpful in the recognition process; Face Registration of a 3D‐face model with each sample face with neutral expression in the training database in order to augment its training set (with 18 predefined expressions). Additional expressions can be learned from input faces or an unknown expression can be transformed to the closest known expression. Our results show that the 3D‐face model resembles perfectly the neutral expression faces in the training database while providing a natural change of expression. Moreover, the inclusion of our expression synthesis approach in a simple 3D‐FRS based on Fisherfaces increased significantly the recognition rate without requiring complex 3D‐face recognition chemes.

REPRESENTING 3D FACE FROM POINT CLOUD TO FACE-ALIGNED SPHERICAL DEPTH MAP

2012 ◽  
Vol 26 (01) ◽  
pp. 1255003 ◽  
Author(s):  
PEIJIANG LIU ◽  
YUNHONG WANG ◽  
ZHAOXIANG ZHANG
Keyword(s):  
Point Cloud ◽  
Special Kind ◽  
Depth Map ◽  
Face Verification ◽  
Face Alignment ◽  
Range Image ◽  
Human Face ◽  
3D Face ◽  
Face Shape ◽  
The Face

We propose a novel representation of 3D face shape which is a key step for feature extraction and face recognition. The input of the proposed methods is unstructured point cloud, which determines the wide applicability of the proposed representation. Our contributions mainly include two parts: Spherical Depth Map (SDM) and face alignment based on SDM. SDM, which can be adopted to many applications, is a special kind of range image utilizing the prior anatomical knowledge of human face. Useful characteristics of SDM facilitate face alignment with higher efficiency and accuracy. Experiments conducted on three popular 3D face databases verify the high efficacy and superiority of the proposed method. The accuracy of face alignment is up to 100% with our strategy. The face verification rates based on the standard protocols are all higher than the baseline performance of FRGC2.0.

An improved technique for face recognition applications

2018 ◽  
Vol 119 (9/10) ◽  
pp. 529-544 ◽  
Author(s):  
Ihab Zaqout ◽  
Mones Al-Hanjori
Keyword(s):  
Face Recognition ◽  
Computer Games ◽  
Recognition Accuracy ◽  
Learning Algorithm ◽  
Recognition Rate ◽  
Data Set ◽  
Content Type ◽  
Linear Discriminant ◽  
The Face ◽  
Feature Based

Purpose The face recognition problem has a long history and a significant practical perspective and one of the practical applications of the theory of pattern recognition, to automatically localize the face in the image and, if necessary, identify the person in the face. Interests in the procedures underlying the process of localization and individual’s recognition are quite significant in connection with the variety of their practical application in such areas as security systems, verification, forensic expertise, teleconferences, computer games, etc. This paper aims to recognize facial images efficiently. An averaged-feature based technique is proposed to reduce the dimensions of the multi-expression facial features. The classifier model is generated using a supervised learning algorithm called a back-propagation neural network (BPNN), implemented on a MatLab R2017. The recognition rate and accuracy of the proposed methodology is comparable with other methods such as the principle component analysis and linear discriminant analysis with the same data set. In total, 150 faces subjects are selected from the Olivetti Research Laboratory (ORL) data set, resulting 95.6 and 85 per cent recognition rate and accuracy, respectively, and 165 faces subjects from the Yale data set, resulting 95.5 and 84.4 per cent recognition rate and accuracy, respectively. Design/methodology/approach Averaged-feature based approach (dimension reduction) and BPNN (generate supervised classifier). Findings The recognition rate is 95.6 per cent and recognition accuracy is 85 per cent for the ORL data set, whereas the recognition rate is 95.5 per cent and recognition accuracy is 84.4 per cent for the Yale data set. Originality/value Averaged-feature based method.

Center-Symmetric Local Nonsubsampled Contourlet Transform Binary Pattern Histogram Sequence for Face Recognition

2013 ◽  
Vol 433-435 ◽  
pp. 405-411
Author(s):  
Rong Bing Huang ◽  
Xiao Qun Liu
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Contourlet Transform ◽  
Nonsubsampled Contourlet Transform ◽  
Feature Maps ◽  
Face Expression ◽  
Face Features ◽  
The Face ◽  
Representation Method ◽  
Pattern Histogram

In order to alleviate the effect of illumination variations and improve the face recognition rate, this paper proposes a novel non-statistics based face representation method, which is called Center-Symmetric Local Nonsubsampled Contourlet Transform Binary Pattern Histogram Sequence (CS-LNBPHS). This method first applies NSCT to decompose a face image, and obtains NSCT coefficients in different scales and various orientations. Then, CS-LBP operator is used to get CS-LBP feature maps from NSCT coefficients. After that, feature maps are respectively divided into several blocks, the concatenated histogram, which are calculated over each block, are used as the face features. Experimental results on YaleB, ORL face databases show the validity of the proposed approach especially for illumination, face expression and position.

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