Face Recognition Using Local Variation

2012 ◽  
Vol 236-237 ◽  
pp. 875-880
Author(s):  
Jing Jing Liu ◽  
Wei Guo Shen ◽  
Quan Xue Gao ◽  
Xiu Juan Hao
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Dimensionality Reduction ◽  
Local Variation ◽  
Adjacency Graph ◽  
Small Weight ◽  
Face Images ◽  
Novel Approach

In this paper, a novel approach, namely local variation projection (LVP), is presented for face recognition. LVP defines an adjacency graph to model the variation among nearby face images, which includes the within-class variation and between-class variation, also called margin. In order to better detect the discriminant structure, we assign a small weight to the variation among nearby face images from the same class. Based on this content, a concise feature extraction criterion is built for dimensionality reduction. Experiments indicate the effectiveness of our proposed approach.

scholarly journals AN ADOPTION OF 2D-PCA/ICA BASED POST-PROCESSING DIMENSIONALITY REDUCTION ALGORITHM FOR FACIAL RECOGNITION SYSTEM

2020 ◽  
Vol 3 (2) ◽  
pp. 222-235
Author(s):  
Vivian Nwaocha ◽  
◽  
Ayodele Oloyede ◽  
Deborah Ogunlana ◽  
Michael Adegoke ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Dimensionality Reduction ◽  
Nearest Neighbor ◽  
Gaussian Function ◽  
Principal Component ◽  
Component Analysis ◽  
Two Dimensional ◽  
Discrimination Ability ◽  
Face Images

Face images undergo considerable amount of variations in pose, facial expression and illumination condition. This large variation in facial appearances of the same individual makes most Existing Face Recognition Systems (E-FRS) lack strong discrimination ability and timely inefficient for face representation due to holistic feature extraction technique used. In this paper, a novel face recognition framework, which is an extension of the standard (PCA) and (ICA) denoted as two-dimensional Principal Component Analysis (2D-PCA) and two-dimensional Independent Component Analysis (2D-ICA) respectively is proposed. The choice of 2D was advantageous as image covariance matrix can be constructed directly using original image matrices. The face images used in this study were acquired from the publicly available ORL and AR Face database. The features belonging to similar class were grouped and correlation calculated in the same order. Each technique was decomposed into different components by employing multi-dimensional grouped empirical mode decomposition using Gaussian function. The nearest neighbor (NN) classifier is used for classification. The results of evaluation showed that the 2D-PCA method using ORL database produced RA of 92.5%, PCA produced RA of 75.00%, ICA produced RA of 77.5%, 2D-ICA produced RA of 96.00%. However, 2D-PCA methods using AR database produced RA of 73.56%, PCA produced RA of 62.41%, ICA produced RA of 66.20%, 2D-ICA method produced RA of 77.45%. This study revealed that the developed face recognition framework algorithm achieves an improvement of 18.5% and 11.25% for the ORL and AR databases respectively as against PCA and ICA feature extraction techniques. Keywords: computer vision, dimensionality reduction techniques, face recognition, pattern recognition

FastNMF

2010 ◽  
pp. 137-163
Author(s):  
Le Li ◽  
Le Li ◽  
Yu-Jin Zhang ◽  
Yu-Jin Zhang
Keyword(s):  
Feature Extraction ◽  
Fixed Point ◽  
Dimensionality Reduction ◽  
Matrix Factorization ◽  
Ease Of Use ◽  
Experimental Results ◽  
Fixed Point Algorithm ◽  
Popular Method ◽  
Face Images ◽  
Multiplicative Update

Non-negative matrix factorization (NMF) is a more and more popular method for non-negative dimensionality reduction and feature extraction of non-negative data, especially face images. Currently no NMF algorithm holds not only satisfactory efficiency for dimensionality reduction and feature extraction of face images but also high ease of use. To improve the applicability of NMF, this chapter proposes a new monotonic, fixed-point algorithm called FastNMF by implementing least squares error-based non-negative factorization essentially according to the basic properties of parabola functions. The minimization problem corresponding to an operation in FastNMF can be analytically solved just by this operation, which is far beyond existing NMF algorithms’ power, and therefore FastNMF holds much higher efficiency, which is validated by a set of experimental results. For the simplicity of design philosophy, FastNMF is still one of NMF algorithms that are the easiest to use and the most comprehensible. Besides, theoretical analysis and experimental results also show that FastNMF tends to extract facial features with better representation ability than popular multiplicative update-based algorithms.

Application Research of Face Feature Extraction Based on DM642

2010 ◽  
Vol 139-141 ◽  
pp. 2024-2028
Author(s):  
Jin Qing Liu ◽  
Qun Zhen Fan ◽  
Dong Cao
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Application Research ◽  
Feature Extraction Method ◽  
Modified Method ◽  
Face Images ◽  
Safety Awareness ◽  
Time Requirements ◽  
Hardware Platforms ◽  
Ica Algorithm

With the safety awareness strengthened, identification authentication technology has been increasingly concerned. Face recognition is attractive in pattern recognition and artificial intelligence field, and face feature extraction is a very important part in face recognition. This paper first introduced preprocessing of face images, PCA and ICA algorithm. Considering PCA and ICA their respective strengths and weaknesses, then a novel face feature extraction method based on PCA and ICA is proposed. The NN classifier is select to face classification and recognition on the ORL face database. From the actual requirements, the paper analyses hardware platforms based on DM642, and finally use tool CCS software to optimize program and implementation base on DM642 to meet the real-time requirements. Experiments indicated that the modified method is superior to PCA and ICA algorithm.

An efficient deep learning model for classification of thermal face images

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Basma Abd El-Rahiem ◽  
Ahmed Sedik ◽  
Ghada M. El Banby ◽  
Hani M. Ibrahem ◽  
Mohamed Amin ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Low Contrast ◽  
Content Type ◽  
Automatic Feature Extraction ◽  
Face Images ◽  
Proposed Model ◽  
Thermal Face ◽  
Deep Learning Model

PurposeThe objective of this paper is to perform infrared (IR) face recognition efficiently with convolutional neural networks (CNNs). The proposed model in this paper has several advantages such as the automatic feature extraction using convolutional and pooling layers and the ability to distinguish between faces without visual details.Design/methodology/approachA model which comprises five convolutional layers in addition to five max-pooling layers is introduced for the recognition of IR faces.FindingsThe experimental results and analysis reveal high recognition rates of IR faces with the proposed model.Originality/valueA designed CNN model is presented for IR face recognition. Both the feature extraction and classification tasks are incorporated into this model. The problems of low contrast and absence of details in IR images are overcome with the proposed model. The recognition accuracy reaches 100% in experiments on the Terravic Facial IR Database (TFIRDB).

scholarly journals Fourier Spectrum Features for Face Recognition

2020 ◽  
Vol 9 (4) ◽  
pp. 2632-2636
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Fourier Spectrum ◽  
Partial Occlusion ◽  
Novel Approach ◽  
The Face ◽  
Unsupervised Methods

The objective is to introduce a novel approach which deals with the challenges: uneven illumination and partial occlusion. This method performs face recognition by extracting the magnitude spectra features. At each point on the face, largest matching areas were found. Thus robustness is achieved using Fourier magnitude spectra feature extraction and largest matching area comparison. This method performs competitively with corrupted images and other unsupervised methods. The proposed approach is experimented on Yale B and AR datasets.

WAVELET-FACE BASED SUBSPACE LDA METHOD TO SOLVE SMALL SAMPLE SIZE PROBLEM IN FACE RECOGNITION

2009 ◽  
Vol 07 (02) ◽  
pp. 199-214 ◽  
Author(s):  
WEN-SHENG CHEN ◽  
JIAN HUANG ◽  
JIN ZOU ◽  
BIN FANG
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Dimensionality Reduction ◽  
Sample Size ◽  
Null Space ◽  
Small Sample Size ◽  
Small Sample ◽  
Projection Matrix ◽  
Scatter Matrix ◽  
Linear Discriminant

Linear Discriminant Analysis (LDA) is a popular statistical method for both feature extraction and dimensionality reduction in face recognition. The major drawback of LDA is the so-called small sample size (3S) problem. This problem always occurs when the total number of training samples is smaller than the dimension of feature space. Under this situation, the within-class scatter matrix Sw becomes singular and LDA approach cannot be implemented directly. To overcome the 3S problem, this paper proposes a novel wavelet-face based subspace LDA algorithm. Wavelet-face feature extraction and dimensionality reduction are based on two-level D4-filter wavelet transform and discarding the null space of total class scatter matrix St. It is shown that our obtained projection matrix satisfies the uncorrelated constraint conditions. Hence in the sense of statistical uncorrelation, this projection matrix is optimal. The proposed method for face recognition has been evaluated with two public available databases, namely ORL and FERET databases. Comparing with existing LDA-based methods to solve the 3S problem, our method gives the best performance.

BIONIC FACE RECOGNITION USING GABOR TRANSFORMATION

2011 ◽  
Vol 25 (03) ◽  
pp. 391-402 ◽  
Author(s):  
GUANGHUI HE ◽  
YUANYAN TANG ◽  
BIN FANG ◽  
Patrick S. P. WANG
Keyword(s):  
Face Recognition ◽  
Dimensionality Reduction ◽  
Face Database ◽  
Recognition Method ◽  
Feature Vectors ◽  
Face Images ◽  
Gabor Features ◽  
Gabor Feature ◽  
Pca Algorithm

In this paper, we propose a bionic face recognition method based on Gabor feature. First, Gabor features are extracted from face images, followed by dimensionality reduction using 2DPCA algorithm, which serves as the feature vectors of the proposed method. Finally, the bionic classifier is trained for classification. The experiment on AR and PIE face database is reported to show the effectiveness of the proposed method and compare it with Gabor-2DPCA algorithm and Gabor-PCA algorithm.

A Novel Approach for Face Recognition under Varying Illumination Conditions

2018 ◽  
Vol 14 (2) ◽  
pp. 22-42
Author(s):  
V Mohanraj ◽  
V. Vaidehi ◽  
S Vasuhi ◽  
Ranajit Kumar
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Recognition Rate ◽  
Feature Space ◽  
Image Feature ◽  
Feature Descriptor ◽  
Chi Square ◽  
Invariant Region ◽  
Facial Landmarks ◽  
Novel Approach

Face recognition systems are in great demand for domestic and commercial applications. A novel feature extraction approach is proposed based on TanTrigg Lower Edge Directional Patterns for robust face recognition. Histogram of Orientated Gradients is used to detect faces and the facial landmarks are localized using Ensemble of Regression Trees. The detected face is rotated based on facial landmarks using affine transformation followed by cropping and resizing. TanTrigg preprocessor is used to convert the aligned face region into an illumination invariant region for better feature extraction. Eight directional Kirsch compass masks are convolved with the preprocessed face image. Feature descriptor is extracted by dividing the TTLEDP image into several sub-regions and concatenating the histograms of all the sub-regions. Chi-square distance metric is used to match faces from the trained feature space. The experimental results prove that the proposed TTLEDP feature descriptor has better recognition rate than existing methods, overcoming the challenges like varying illumination and noise

An Intelligent Fused Approach for Face Recognition

2013 ◽  
Vol 22 (2) ◽  
pp. 197-212 ◽  
Author(s):  
Khitikun Meethongjan ◽  
Mohamad Dzulkifli ◽  
Amjad Rehman ◽  
Ayman Altameem ◽  
Tanzila Saba
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Wavelet Transform ◽  
Human Computer Interaction ◽  
Face Detection ◽  
Delaunay Triangulation ◽  
State Of The Art ◽  
Moment Invariants ◽  
Discrete Wavelet ◽  
Face Images

AbstractFace detection plays important roles in many applications such as human–computer interaction, security and surveillance, face recognition, etc. This article presents an intelligent enhanced fused approach for face recognition based on the Voronoi diagram (VD) and wavelet moment invariants. Discrete wavelet transform and moment invariants are used for feature extraction of the facial face. Finally, VD and the dual tessellation (Delaunay triangulation, DT) are used to locate and detect original face images. Face recognition results based on this new fusion are promising in the state of the art.

Sign in / Sign up

Export Citation Format

Share Document