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.

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.

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.

Robust Face Recognition Technique for a Real-Time Embedded Face Recognition System

2008 ◽  
pp. 188-211
Author(s):  
Ting Shan ◽  
Abbas Bigdeli ◽  
Brian C. Lovell ◽  
Shaokang Chen
Keyword(s):  
Face Recognition ◽  
Embedded System ◽  
Principal Component ◽  
Recognition System ◽  
Active Appearance Models ◽  
Face Recognition System ◽  
The Face ◽  
The Embedded System ◽  
Computationally Intensive ◽  
Robust Face Recognition

In this chapter, we propose a pose variability compensation technique, which synthesizes realistic frontal face images from nonfrontal views. It is based on modeling the face via active appearance models and estimating the pose through a correlation model. The proposed technique is coupled with adaptive principal component analysis (APCA), which was previously shown to perform well in the presence of both lighting and expression variations. The proposed recognition techniques, though advanced, are not computationally intensive. So they are quite well suited to the embedded system environment. Indeed, the authors have implemented an early prototype of a face recognition module on a mobile camera phone so the camera can be used to identify the person holding the phone.

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.

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.

A Face Recognition Algorithm Based on Block PCA and Wavelet Transform

2010 ◽  
Vol 40-41 ◽  
pp. 523-530 ◽  
Author(s):  
Dong Cheng Shi ◽  
Qing Qing Wang
Keyword(s):  
Face Recognition ◽  
Wavelet Transform ◽  
Recognition Rate ◽  
Low Frequency ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Global Features ◽  
Face Area ◽  
The Face ◽  
Pca Method

As the most successful method of linear distinguish, principal component analysis(PCA) method is widely used in identify areas, such as face recognition. But traditional PCA is influenced by light conditions, facial expression and it extracts the global features of the image, so the recognition rate is not very high. In order to improve more accurately identify facial features and extract local features which account for a larger contribution to the identification. This paper brings up a method of a block face recognition based on wavelet transform (WT-BPCA). In the algorithm, face images are done two-dimensional wavelet decomposition, then from which extract low frequency sub-images. According to different face area makes different contribution to recognition, we use sub-block PCA method. According to the contribution of the block recognition results generate weighting factors, the face recognition rate based on PCA is effectively improved. Finally we construct classification to recognite. Do experiments in the ORL face database. Results show that this method is superior to the method of the traditional PCA.

scholarly journals Identification and Recognition of facial images

2020 ◽  
pp. 69-76
Author(s):  
Mallika Kohli ◽  
Vasundra Wazir ◽  
Parul Sharma ◽  
Pawanesh Abrol
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition System ◽  
Main Task ◽  
Face Recognition System ◽  
The Face ◽  
Almost All ◽  
Facial Images

Face detection is the power to identify a face and recognition is the ability to recognize whose face it is by means of facial characteristics. Face is multivariate and requires a lot of mathematical summation. Almost all imperative applications use a face recognition system. There are many methods that have been already proposed which provides low recognition rate. Hence, the main task of research is to develop a face recognition system with higher recognition capability and better accuracy. This paper proposes Face recognition system by combining two techniques Viola Jones and Principal Component Analysis. An approach of Eigen faces is employed in Principle Component Analysis(PCA). The face recognition system is implemented in MATLAB.

scholarly journals 3D Face Factorisation for Face Recognition Using Pattern Recognition Algorithms

2019 ◽  
Vol 19 (2) ◽  
pp. 28-37
Author(s):  
Hawraa H. Abbas ◽  
Bilal Z. Ahmed ◽  
Ahmed Kamil Abbas
Keyword(s):  
Face Recognition ◽  
High Performance ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Recognition Task ◽  
Recognition System ◽  
3D Face Recognition ◽  
Shape Variation ◽  
3D Face ◽  
The Face

Abstract The face is the preferable biometrics for person recognition or identification applications because person identifying by face is a human connate habit. In contrast to 2D face recognition, 3D face recognition is practically robust to illumination variance, facial cosmetics, and face pose changes. Traditional 3D face recognition methods describe shape variation across the whole face using holistic features. In spite of that, taking into account facial regions, which are unchanged within expressions, can acquire high performance 3D face recognition system. In this research, the recognition analysis is based on defining a set of coherent parts. Those parts can be considered as latent factors in the face shape space. Non-negative matrix Factorisation technique is used to segment the 3D faces to coherent regions. The best recognition performance is achieved when the vertices of 20 face regions are utilised as a feature vector for recognition task. The region-based 3D face recognition approach provides a 96.4% recognition rate in FRGCv2 dataset.

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