scholarly journals Robust Face Recognition Based on a New Supervised Kernel Subspace Learning Method

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1643 ◽  
Author(s):  
Ali Khalili Mobarakeh ◽  
Juan Antonio Cabrera Carrillo ◽  
Juan Jesús Castillo Aguilar
Keyword(s):  
Face Recognition ◽  
Dimensional Space ◽  
Subspace Learning ◽  
Classification Performance ◽  
Learning Method ◽  
Structure Information ◽  
Pattern Recognition Methods ◽  
Face Images ◽  
Robust Face Recognition ◽  
Complex Variation

Face recognition is one of the most popular techniques to achieve the goal of figuring out the identity of a person. This study has been conducted to develop a new non-linear subspace learning method named “supervised kernel locality-based discriminant neighborhood embedding,” which performs data classification by learning an optimum embedded subspace from a principal high dimensional space. In this approach, not only nonlinear and complex variation of face images is effectively represented using nonlinear kernel mapping, but local structure information of data from the same class and discriminant information from distinct classes are also simultaneously preserved to further improve final classification performance. Moreover, in order to evaluate the robustness of the proposed method, it was compared with several well-known pattern recognition methods through comprehensive experiments with six publicly accessible datasets. Experiment results reveal that our method consistently outperforms its competitors, which demonstrates strong potential to be implemented in many real-world systems.

DISCRIMINATIVE LOCAL LEARNING PROJECTION FOR FACE RECOGNITION

2011 ◽  
Vol 25 (01) ◽  
pp. 83-97 ◽  
Author(s):  
YU CHEN ◽  
JIAN HUANG ◽  
XIAOHONG XU ◽  
JIANHUANG LAI
Keyword(s):  
Face Recognition ◽  
Local Structure ◽  
Estimation Error ◽  
Subspace Learning ◽  
High Dimensional ◽  
Local Geometry ◽  
Local Learning ◽  
Learning Method ◽  
Local Structures ◽  
Face Images

Subspace learning method has commonly been used as a popular way to understand high dimensional data such as face images. In this paper, a novel subspace learning method called Discriminative Local Learning Projection (DLLP) is proposed for face recognition. By characterizing the local structures and dissimilarities between the supervised data manifolds, a linear transformation that can maximize the dissimilarities between all manifolds and simultaneously minimize the local estimation error can be computed. Thus the proposed algorithm embeds the discriminative information as well as the local geometry of samples into the objective function. And the abilities of preserving the local structure in each manifold and classification are both combined into the algorithm. Extensive experiments on face databases demonstrate the effectiveness of DLLP.

SUBSPACE LEARNING BASED ON LAPLACIAN EIGENMAPS AND LDA FOR FACE RECOGNITION

2006 ◽  
Vol 03 (01) ◽  
pp. 45-51
Author(s):  
YANWEI PANG ◽  
ZHENGKAI LIU ◽  
YUEFANG SUN
Keyword(s):  
Face Recognition ◽  
Subspace Learning ◽  
Dimensional Subspace ◽  
Training Data ◽  
Objective Functions ◽  
Recognition Method ◽  
Laplacian Eigenmaps ◽  
Linear Discriminant ◽  
Structure Information ◽  
Low Dimensional

Subspace-based face recognition method aims to find a low-dimensional subspace of face appearance embedded in a high-dimensional image space. The differences between different methods lie in their different motivations and objective functions. The objective function of the proposed method is formed by combining the ideas of linear Laplacian eigenmaps and linear discriminant analysis. The actual computation of the subspace reduces to a maximum eigenvalue problem. Major advantage of the proposed method over traditional methods is that it utilizes both local manifold structure information and discriminant information of the training data. Experimental results on the AR face databases demonstrate the effectiveness of the proposed method.

scholarly journals Eigenvector Weighting Function in Face Recognition

2011 ◽  
Vol 2011 ◽  
pp. 1-15 ◽  
Author(s):  
Pang Ying Han ◽  
Andrew Teoh Beng Jin ◽  
Lim Heng Siong
Keyword(s):  
Face Recognition ◽  
Weighting Function ◽  
Subspace Learning ◽  
Linear Projection ◽  
Face Images ◽  
Locality Preserving ◽  
Learning Techniques ◽  
Dimensionality Reduction Technique ◽  
The Face ◽  
Face Subspace

Graph-based subspace learning is a class of dimensionality reduction technique in face recognition. The technique reveals the local manifold structure of face data that hidden in the image space via a linear projection. However, the real world face data may be too complex to measure due to both external imaging noises and the intra-class variations of the face images. Hence, features which are extracted by the graph-based technique could be noisy. An appropriate weight should be imposed to the data features for better data discrimination. In this paper, a piecewise weighting function, known as Eigenvector Weighting Function (EWF), is proposed and implemented in two graph based subspace learning techniques, namely Locality Preserving Projection and Neighbourhood Preserving Embedding. Specifically, the computed projection subspace of the learning approach is decomposed into three partitions: a subspace due to intra-class variations, an intrinsic face subspace, and a subspace which is attributed to imaging noises. Projected data features are weighted differently in these subspaces to emphasize the intrinsic face subspace while penalizing the other two subspaces. Experiments on FERET and FRGC databases are conducted to show the promising performance of the proposed technique.

scholarly journals Design of an Active Multispectral SWIR Camera System for Skin Detection and Face Verification

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Holger Steiner ◽  
Sebastian Sporrer ◽  
Andreas Kolb ◽  
Norbert Jung
Keyword(s):  
Face Recognition ◽  
Imaging System ◽  
Short Wave ◽  
Classification Performance ◽  
High Spectral Resolution ◽  
Face Verification ◽  
Camera System ◽  
Spectral Signatures ◽  
Face Images ◽  
Small Band

Biometric face recognition is becoming more frequently used in different application scenarios. However, spoofing attacks with facial disguises are still a serious problem for state of the art face recognition algorithms. This work proposes an approach to face verification based on spectral signatures of material surfaces in the short wave infrared (SWIR) range. They allow distinguishing authentic human skin reliably from other materials, independent of the skin type. We present the design of an active SWIR imaging system that acquires four-band multispectral image stacks in real-time. The system uses pulsed small band illumination, which allows for fast image acquisition and high spectral resolution and renders it widely independent of ambient light. After extracting the spectral signatures from the acquired images, detected faces can be verified or rejected by classifying the material as “skin” or “no-skin.” The approach is extensively evaluated with respect to both acquisition and classification performance. In addition, we present a database containing RGB and multispectral SWIR face images, as well as spectrometer measurements of a variety of subjects, which is used to evaluate our approach and will be made available to the research community by the time this work is published.

scholarly journals Face recognition based on stable uniform patterns

2018 ◽  
Vol 7 (2) ◽  
pp. 626
Author(s):  
A. Mallikarjuna Reddy ◽  
V. Venkata Krishna ◽  
L. Sumalatha
Keyword(s):  
Face Recognition ◽  
Local Pattern ◽  
Feature Extraction Method ◽  
Image Histogram ◽  
Research Fields ◽  
Face Images ◽  
Wide Range ◽  
Robust Face Recognition ◽  
Active Research ◽  
Lbp Descriptor

Face recognition (FR) is one of the challenging and active research fields of image processing, computer vision and biometrics with numerous proposed systems. We present a feature extraction method named “stable uniform local pattern (SULP)”, a refined variant of ULBP operator, for robust face recognition. The SULP directly applied on gradient face images (in x and y directions) of a single image for capturing significant fundamental local texture patterns to build up a feature vector of a face image. Histogram sequences of SULP images of the two gradient images are finally concatenated to form the “stable uniform local pattern gradient (SULPG)” vector for the given image. The SULPG approach is experimented on Yale, ATT-ORL, FERET, CAS-PEAL and LFW face databases and the results are compared with the LBP model and various variants of LBP descriptor. The results indicate that the present descriptor is more powerful against a wide range of challenges, such as illumination, expression and pose variations and outperforms the state-of-the-art methods based on LBP.

scholarly journals Robust face recognition based on multi-task convolutional neural network

2021 ◽  
Vol 18 (5) ◽  
pp. 6638-6651
Author(s):  
Huilin Ge ◽  
◽  
Yuewei Dai ◽  
Zhiyu Zhu ◽  
Biao Wang
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Face Detection ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Structural Similarity ◽  
Recognition Algorithm ◽  
Face Images ◽  
Robust Face Recognition

<abstract> <sec><title>Purpose</title><p>Due to the lack of prior knowledge of face images, large illumination changes, and complex backgrounds, the accuracy of face recognition is low. To address this issue, we propose a face detection and recognition algorithm based on multi-task convolutional neural network (MTCNN).</p> </sec> <sec><title>Methods</title><p>In our paper, MTCNN mainly uses three cascaded networks, and adopts the idea of candidate box plus classifier to perform fast and efficient face recognition. The model is trained on a database of 50 faces we have collected, and Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM), and receiver operating characteristic (ROC) curve are used to analyse MTCNN, Region-CNN (R-CNN) and Faster R-CNN.</p> </sec> <sec><title>Results</title><p>The average PSNR of this technique is 1.24 dB higher than that of R-CNN and 0.94 dB higher than that of Faster R-CNN. The average SSIM value of MTCNN is 10.3% higher than R-CNN and 8.7% higher than Faster R-CNN. The Area Under Curve (AUC) of MTCNN is 97.56%, the AUC of R-CNN is 91.24%, and the AUC of Faster R-CNN is 92.01%. MTCNN has the best comprehensive performance in face recognition. For the face images with defective features, MTCNN still has the best effect.</p> </sec> <sec><title>Conclusions</title><p>This algorithm can effectively improve face recognition to a certain extent. The accuracy rate and the reduction of the false detection rate of face detection can not only be better used in key places, ensure the safety of property and security of the people, improve safety, but also better reduce the waste of human resources and improve efficiency.</p> </sec> </abstract>

Discriminative Low-Rank Subspace Learning with Nonconvex Penalty

2019 ◽  
Vol 33 (10) ◽  
pp. 1951006
Author(s):  
Kan Xie ◽  
Wei Liu ◽  
Yue Lai ◽  
Weijun Li
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Subspace Learning ◽  
Experimental Results ◽  
Low Rank ◽  
Learning Method ◽  
Original Solution ◽  
Discriminative Subspace ◽  
Low Rank Representation ◽  
Facial Images

Subspace learning has been widely utilized to extract discriminative features for classification task, such as face recognition, even when facial images are occluded or corrupted. However, the performance of most existing methods would be degraded significantly in the scenario of that data being contaminated with severe noise, especially when the magnitude of the gross corruption can be arbitrarily large. To this end, in this paper, a novel discriminative subspace learning method is proposed based on the well-known low-rank representation (LRR). Specifically, a discriminant low-rank representation and the projecting subspace are learned simultaneously, in a supervised way. To avoid the deviation from the original solution by using some relaxation, we adopt the Schatten [Formula: see text]-norm and [Formula: see text]-norm, instead of the nuclear norm and [Formula: see text]-norm, respectively. Experimental results on two famous databases, i.e. PIE and ORL, demonstrate that the proposed method achieves better classification scores than the state-of-the-art approaches.

scholarly journals Face Recognition and Drunk Classification Using Infrared Face Images

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Gabriel Hermosilla ◽  
José Luis Verdugo ◽  
Gonzalo Farias ◽  
Esteban Vera ◽  
Francisco Pizarro ◽  
...  
Keyword(s):  
Face Recognition ◽  
Promising Result ◽  
Gaussian Mixture ◽  
Local Descriptor ◽  
Linear Discriminant ◽  
Face Images ◽  
The Face ◽  
Robust Face Recognition ◽  
Thermal Face ◽  
Two Stages

The aim of this study is to propose a system that is capable of recognising the identity of a person, indicating whether the person is drunk using only information extracted from thermal face images. The proposed system is divided into two stages, face recognition and classification. In the face recognition stage, test images are recognised using robust face recognition algorithms: Weber local descriptor (WLD) and local binary pattern (LBP). The classification stage uses Fisher linear discriminant to reduce the dimensionality of the features, and those features are classified using a classifier based on a Gaussian mixture model, creating a classification space for each person, extending the state-of-the-art concept of a “DrunkSpace Classifier.” The system was validated using a new drunk person database, which was specially designed for this work. The main results show that the performance of the face recognition stage was 100% with both algorithms, while the drunk identification saw a performance of 86.96%, which is a very promising result considering 46 individuals for our database in comparison with others that can be found in the literature.

Fuzzy Aggregation Method Using Fisherface and Wavelet Decomposition for Face Recognition

2004 ◽  
Vol 8 (4) ◽  
pp. 379-384 ◽  
Author(s):  
Keun-Chang Kwak ◽  
◽  
Witold Pedrycz ◽  
Hyoun-Joo Go ◽  
Myung-Geun Chun ◽  
...  
Keyword(s):  
Face Recognition ◽  
Wavelet Decomposition ◽  
Classification Performance ◽  
Aggregation Method ◽  
Second Stage ◽  
Face Images ◽  
Fuzzy Aggregation ◽  
Two Phases ◽  
The Individual ◽  
High Consistency

In this paper, we propose the fuzzy aggregation method for face recognition based on subimage sets decomposed by wavelets. The proposed approach consists of four main stages. The first stage uses the wavelet decomposition that helps extract intrinsic features of face images. The second stage of the approach applies a fisherface method to these four subimages obtained by wavelet decomposition. The choice of the fisherface method in this setting is motivated by its insensitivity to large variation in light direction, face pose, and facial expression. The last two phases are concerned with the aggregation of the individual classifiers by means of the fuzzy integral. The experiments use an n-fold cross-validation to assure high consistency of the classification results. The experimental results obtained for the Yale face databases reveal that the approach presented in this paper yields better classification performance in comparison with the results obtained by other recognition methods.

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