scholarly journals A novel framework for face recognition using robust local representation–based classification

2019 ◽  
Vol 15 (3) ◽  
pp. 155014771983608
Author(s):  
Aihua Yu ◽  
Gang Li ◽  
Beiping Hou ◽  
Hongan Wang ◽  
Gaoya Zhou
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Recall Rate ◽  
Data Sets ◽  
Computation Complexity ◽  
Face Images ◽  
In The Wild ◽  
Recognition Systems

Face recognition via representation-based classification is a trending technique in the recent years. However, the recognition performance of the systems using such a technique degrades in an unconstrained environment. In this article, a novel framework is proposed for representation-based face recognition. To deal with the unconstrained environment, a pre-process is used to frontalize face images, and aligned downsampling local binary pattern features of the frontalized images are used for classification. A dimension reduction is then adopted in order to reduce the computation complexity via an optimized projection matrix. The recognition is carried out using an improved robust sparse coding algorithm. Such an algorithm is expected to avoid the overfitting problem. The open-universe test on labeled faces in the wild data sets shows that the recognition rate of the proposed system can reach 95% with a recall rate of 80%, which is best among those representation-based classification face recognition systems.

scholarly journals Multialgorithmic Frameworks for Human Face Recognition

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Radhey Shyam ◽  
Yogendra Narain Singh
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Recognition Performance ◽  
Critical Evaluation ◽  
Recognition System ◽  
Human Face Recognition ◽  
Face Recognition System ◽  
The Face ◽  
In The Wild ◽  
Recognition Systems

This paper presents a critical evaluation of multialgorithmic face recognition systems for human authentication in unconstrained environment. We propose different frameworks of multialgorithmic face recognition system combining holistic and texture methods. Our aim is to combine the uncorrelated methods of the face recognition that supplement each other and to produce a comprehensive representation of the biometric cue to achieve optimum recognition performance. The multialgorithmic frameworks are designed to combine different face recognition methods such as (i) Eigenfaces and local binary pattern (LBP), (ii) Fisherfaces and LBP, (iii) Eigenfaces and augmented local binary pattern (A-LBP), and (iv) Fisherfaces and A-LBP. The matching scores of these multialgorithmic frameworks are processed using different normalization techniques whereas their performance is evaluated using different fusion strategies. The robustness of proposed multialgorithmic frameworks of face recognition system is tested on publicly available databases, for example, AT & T (ORL) and Labeled Faces in the Wild (LFW). The experimental results show a significant improvement in recognition accuracies of the proposed frameworks of face recognition system in comparison to their individual methods. In particular, the performance of the multialgorithmic frameworks combining face recognition methods with the devised face recognition method such as A-LBP improves significantly.

scholarly journals Kernel Coupled Cross-Regression for Low-Resolution Face Recognition

2013 ◽  
Vol 2013 ◽  
pp. 1-9
Author(s):  
Zhifei Wang ◽  
Zhenjiang Miao ◽  
Yanli Wan ◽  
Zhen Tang
Keyword(s):  
Face Recognition ◽  
Time Complexity ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Feature Space ◽  
Low Resolution ◽  
Face Images ◽  
Novel Method ◽  
Nonlinear Embedding ◽  
Spectral Regression

Low resolution (LR) in face recognition (FR) surveillance applications will cause the problem of dimensional mismatch between LR image and its high-resolution (HR) template. In this paper, a novel method called kernel coupled cross-regression (KCCR) is proposed to deal with this problem. Instead of processing in the original observing space directly, KCCR projects LR and HR face images into a unified nonlinear embedding feature space using kernel coupled mappings and graph embedding. Spectral regression is further employed to improve the generalization performance and reduce the time complexity. Meanwhile, cross-regression is developed to fully utilize the HR embedding to increase the information of the LR space, thus to improve the recognition performance. Experiments on the FERET and CMU PIE face database show that KCCR outperforms the existing structure-based methods in terms of recognition rate as well as time complexity.

Neural Network-Based Approach for Face Recognition Across Varying Pose

2015 ◽  
Vol 29 (08) ◽  
pp. 1556015 ◽  
Author(s):  
Seyed Omid Shahdi ◽  
S. A. R. Abu-Bakar
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Local Binary Pattern ◽  
Feature Vector ◽  
Recognition Rate ◽  
Local Information ◽  
Discrete Wavelet ◽  
Face Images ◽  
The Face ◽  
The Relationship

At present, frontal or even near frontal face recognition problem is no longer considered as a challenge. Recently, the shift has been to improve the recognition rate for the nonfrontal face. In this work, a neural network paradigm based on the radial basis function approach is proposed to tackle the challenge of recognizing faces in different poses. Exploiting the symmetrical properties of human face, our work takes the advantage of the existence of even half of the face. The strategy is to maximize the linearity relationship based on the local information of the face rather than on the global information. To establish the relationship, our proposed method employs discrete wavelet transform and multi-color uniform local binary pattern (ULBP) in order to obtain features for the local information. The local information will then be represented by a single vector known as the face feature vector. This face feature vector will be used to estimate the frontal face feature vector which will be used for matching with the actual vector. With such an approach, our proposed method relies on a database that contains only single frontal face images. The results shown in this paper demonstrate the robustness of our proposed method even at low-resolution conditions.

scholarly journals The Face Recognition Method Based on CS-LBP and DBN

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Kun Sun ◽  
Xin Yin ◽  
Mingxin Yang ◽  
Yang Wang ◽  
Jianying Fan
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Recognition Rate ◽  
Texture Feature ◽  
Texture Features ◽  
Face Image ◽  
Recognition Method ◽  
Traditional Methods ◽  
Face Images ◽  
The Face

At present, the face recognition method based on deep belief network (DBN) has advantages of automatically learning the abstract information of face images and being affected slightly by active factors, so it becomes the main method in the face recognition area. Because DBN ignores the local information of face images, the face recognition rate based on DBN is badly affected. To solve this problem, a face recognition method based on center-symmetric local binary pattern (CS-LBP) and DBN (FRMCD) is proposed in this paper. Firstly, the face image is divided into several subblocks. Secondly, CS-LBP is used to extract texture features of each image subblock. Thirdly, texture feature histograms are formed and input into the DBN visual layer. Finally, face classification and face recognition are completed through deep learning in DBN. Through the experiments on face databases ORL, Extend Yale B, and CMU-PIE by the proposed method (FRMCD), the best partitioning way of the face image and the hidden unit number of the DBN hidden layer are obtained. Then, comparative experiments between the FRMCD and traditional methods are performed. The results show that the recognition rate of FRMCD is superior to those of traditional methods; the highest recognition rate is up to 98.82%. When the number of training samples is less, the FRMCD has more significant advantages. Compared with the method based on local binary pattern (LBP) and DBN, the time-consuming of FRMCD is shorter.

scholarly journals A novel approach for Face Recognition using Local Binary Pattern

2012 ◽  
pp. 25-31
Author(s):  
Sonal R. Ahirrao ◽  
D. S. Bormane
Keyword(s):  
Face Recognition ◽  
Local Binary Pattern ◽  
Recognition Rate ◽  
Global Features ◽  
Face Images ◽  
Grey Scale ◽  
Novel Approach ◽  
The Face ◽  
Scale Transformations ◽  
Micro Patterns

This paper presents Local Binary pattern (LBP) as an approach for face recognition with the use of some global features also. Face recognition has received quite a lot of attention from researchers in biometrics, pattern recognition, and computer vision communities. The idea behind using the LBP features is that the face images can be seen as composition of micro-patterns which are invariant with respect to monotonic grey scale transformations and robust to factors like ageing. Combining these micro-patterns, a global description of the face image is obtained. Efficiency and the simplicity of the proposed method allows for very fast feature extraction giving better accuracy than the other algorithms. The proposed method is tested and evaluated on ORL datasets combined with other university dataset to give a good recognition rate and 89% classification accuracy using LBP only and 98% when global features are combined with LBP. The method is also tested for real images to give good accuracy and recognition rate. The experimental results show that the method is valid and feasible.

scholarly journals Data Augmentation-Assisted Makeup-Invariant Face Recognition

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Muhammad Sajid ◽  
Nouman Ali ◽  
Saadat Hanif Dar ◽  
Naeem Iqbal Ratyal ◽  
Asif Raza Butt ◽  
...  
Keyword(s):  
Face Recognition ◽  
Data Augmentation ◽  
Recognition Performance ◽  
Recognition Task ◽  
Face Images ◽  
Face Features ◽  
Face Datasets ◽  
Exponential Rates ◽  
Interpersonal Situations

Recently, face datasets containing celebrities photos with facial makeup are growing at exponential rates, making their recognition very challenging. Existing face recognition methods rely on feature extraction and reference reranking to improve the performance. However face images with facial makeup carry inherent ambiguity due to artificial colors, shading, contouring, and varying skin tones, making recognition task more difficult. The problem becomes more confound as the makeup alters the bilateral size and symmetry of the certain face components such as eyes and lips affecting the distinctiveness of faces. The ambiguity becomes even worse when different days bring different facial makeup for celebrities owing to the context of interpersonal situations and current societal makeup trends. To cope with these artificial effects, we propose to use a deep convolutional neural network (dCNN) using augmented face dataset to extract discriminative features from face images containing synthetic makeup variations. The augmented dataset containing original face images and those with synthetic make up variations allows dCNN to learn face features in a variety of facial makeup. We also evaluate the role of partial and full makeup in face images to improve the recognition performance. The experimental results on two challenging face datasets show that the proposed approach can compete with the state of the art.

scholarly journals Optimization of deep learning features for age-invariant face recognition

2020 ◽  
Vol 10 (2) ◽  
pp. 1833
Author(s):  
Amal A. Moustafa ◽  
Ahmed Elnakib ◽  
Nihal F. F. Areed
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Nearest Neighbor ◽  
Recognition Rate ◽  
Manhattan Distance ◽  
Distance Metrics ◽  
K Nearest Neighbor ◽  
Face Images ◽  
Knn Classifier ◽  
Deep Learning Features

This paper presents a methodology for Age-Invariant Face Recognition (AIFR), based on the optimization of deep learning features. The proposed method extracts deep learning features using transfer deep learning, extracted from the unprocessed face images. To optimize the extracted features, a Genetic Algorithm (GA) procedure is designed in order to select the most relevant features to the problem of identifying a person based on his/her facial images over different ages. For classification, K-Nearest Neighbor (KNN) classifiers with different distance metrics are investigated, i.e., Correlation, Euclidian, Cosine, and Manhattan distance metrics. Experimental results using a Manhattan distance KNN classifier achieves the best Rank-1 recognition rate of 86.2% and 96% on the standard FGNET and MORPH datasets, respectively. Compared to the state-of-the-art methods, our proposed method needs no preprocessing stages. In addition, the experiments show its privilege over other related methods.

Hybridization of Biogeography-Based Optimization and Gravitational Search Algorithm for Efficient Face Recognition

2019 ◽  
pp. 258-279
Author(s):  
Lavika Goel ◽  
Lavanya B. ◽  
Pallavi Panchal
Keyword(s):  
Face Recognition ◽  
Evolutionary Algorithm ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Recognition Rate ◽  
Experimental Results ◽  
Nature Inspired Algorithm ◽  
In The Wild ◽  
Gravitational Search

This chapter aims to apply a novel hybridized evolutionary algorithm to the application of face recognition. Biogeography-based optimization (BBO) has some element of randomness to it that apart from improving the feasibility of a solution could reduce it as well. In order to overcome this drawback, this chapter proposes a hybridization of BBO with gravitational search algorithm (GSA), another nature-inspired algorithm, by incorporating certain knowledge into BBO instead of the randomness. The migration procedure of BBO that migrates SIVs between solutions is done between solutions only if the migration would lead to the betterment of a solution. BBO-GSA algorithm is applied to face recognition with the LFW (labelled faces in the wild) and ORL datasets in order to test its efficiency. Experimental results show that the proposed BBO-GSA algorithm outperforms or is on par with some of the nature-inspired techniques that have been applied to face recognition so far by achieving a recognition rate of 80% with the LFW dataset and 99.75% with the ORL dataset.

Investigation of ANFIS and FFBNN Recognition Methods Performance in Tamil Speech Word Recognition

2014 ◽  
Vol 2 (2) ◽  
pp. 43-53 ◽  
Author(s):  
S. Rojathai ◽  
M. Venkatesulu
Keyword(s):  
Feature Extraction ◽  
Word Recognition ◽  
Recognition Performance ◽  
Recognition Rate ◽  
Back Propagation ◽  
Recognition System ◽  
Inference System ◽  
Feed Forward Back Propagation ◽  
Statistical Measures ◽  
Recognition Systems

In speech word recognition systems, feature extraction and recognition plays a most significant role. More number of feature extraction and recognition methods are available in the existing speech word recognition systems. In most recent Tamil speech word recognition system has given high speech word recognition performance with PAC-ANFIS compared to the earlier Tamil speech word recognition systems. So the investigation of speech word recognition by various recognition methods is needed to prove their performance in the speech word recognition. This paper presents the investigation process with well known Artificial Intelligence method as Feed Forward Back Propagation Neural Network (FFBNN) and Adaptive Neuro Fuzzy Inference System (ANFIS). The Tamil speech word recognition system with PAC-FFBNN performance is analyzed in terms of statistical measures and Word Recognition Rate (WRR) and compared with PAC-ANFIS and other existing Tamil speech word recognition systems.

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.

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