Research of Feature Extraction Based on Improved LBP

Facial feature extraction and recognition plays a prominent role in human non-verbal interaction and it is one of the crucial factors among pose, speech, facial expression, behaviour and actions which are used in conveying information about the intentions and emotions of a human being. In this article an extended local binary pattern is used for the feature extraction process and a principal component analysis (PCA) is used for dimensionality reduction. The projections of the sample and model images are calculated and compared by Euclidean distance method. The combination of extended local binary pattern and PCA (ELBP+PCA) improves the accuracy of the recognition rate and also diminishes the evaluation complexity. The evaluation of proposed facial expression recognition approach will focus on the performance of the recognition rate. A series of tests are performed for the validation of algorithms and to compare the accuracy of the methods on the JAFFE, Extended Cohn-Kanade images database.

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Discrimination of Chinese Liquors Based on Electronic Nose and Fuzzy Discriminant Principal Component Analysis

Foods ◽

10.3390/foods8010038 ◽

2019 ◽

Vol 8 (1) ◽

pp. 38 ◽

Cited By ~ 2

Author(s):

Xiaohong Wu ◽

Jin Zhu ◽

Bin Wu ◽

Chao Zhao ◽

Jun Sun ◽

...

Keyword(s):

Principal Component Analysis ◽

Feature Extraction ◽

Electronic Nose ◽

Nearest Neighbor ◽

Principal Component ◽

Component Analysis ◽

K Nearest Neighbor ◽

Knn Classifier ◽

Extraction Algorithm ◽

Leave One Out

The detection of liquor quality is an important process in the liquor industry, and the quality of Chinese liquors is partly determined by the aromas of the liquors. The electronic nose (e-nose) refers to an artificial olfactory technology. The e-nose system can quickly detect different types of Chinese liquors according to their aromas. In this study, an e-nose system was designed to identify six types of Chinese liquors, and a novel feature extraction algorithm, called fuzzy discriminant principal component analysis (FDPCA), was developed for feature extraction from e-nose signals by combining discriminant principal component analysis (DPCA) and fuzzy set theory. In addition, principal component analysis (PCA), DPCA, K-nearest neighbor (KNN) classifier, leave-one-out (LOO) strategy and k-fold cross-validation (k = 5, 10, 20, 25) were employed in the e-nose system. The maximum classification accuracy of feature extraction for Chinese liquors was 98.378% using FDPCA, showing this algorithm to be extremely effective. The experimental results indicate that an e-nose system coupled with FDPCA is a feasible method for classifying Chinese liquors.

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Research of Face Recognition Methods Based on Binding Feature Extraction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.568-570.668 ◽

2014 ◽

Vol 568-570 ◽

pp. 668-671

Author(s):

Yi Long ◽

Fu Rong Liu ◽

Guo Qing Qiu

Keyword(s):

Feature Extraction ◽

Face Recognition ◽

Extraction Method ◽

Local Binary Pattern ◽

Feature Vector ◽

Recognition Rate ◽

Principal Component ◽

Face Image ◽

Feature Extraction Method ◽

Histogram Feature

To address the problem that the dimension of the feature vector extracted by Local Binary Pattern (LBP) for face recognition is too high and Principal Component Analysis (PCA) extract features are not the best classification features, an efficient feature extraction method using LBP, PCA and Maximum scatter difference (MSD) has been introduced in this paper. The original face image is firstly divided into sub-images, then the LBP operator is applied to extract the histogram feature. and the feature dimensions are further reduced by using PCA. Finally,MSD is performed on the reduced PCA-based feature.The experimental results on ORL and Yale database demonstrate that the proposed method can classify more effectively and can get higher recognition rate than the traditional recognition methods.

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A Hybrid Approach for Facial Expression Recognition Using Extended Local Binary Patterns and Principal Component Analysis

Research Anthology on Rehabilitation Practices and Therapy ◽

10.4018/978-1-7998-3432-8.ch026 ◽

2021 ◽

pp. 517-544

Author(s):

Gopal Krishan Prajapat ◽

Rakesh Kumar

Keyword(s):

Principal Component Analysis ◽

Feature Extraction ◽

Facial Expression ◽

Facial Expression Recognition ◽

Local Binary Pattern ◽

Recognition Rate ◽

Principal Component ◽

Local Binary Patterns ◽

Component Analysis ◽

Expression Recognition

Facial feature extraction and recognition plays a prominent role in human non-verbal interaction and it is one of the crucial factors among pose, speech, facial expression, behaviour and actions which are used in conveying information about the intentions and emotions of a human being. In this article an extended local binary pattern is used for the feature extraction process and a principal component analysis (PCA) is used for dimensionality reduction. The projections of the sample and model images are calculated and compared by Euclidean distance method. The combination of extended local binary pattern and PCA (ELBP+PCA) improves the accuracy of the recognition rate and also diminishes the evaluation complexity. The evaluation of proposed facial expression recognition approach will focus on the performance of the recognition rate. A series of tests are performed for the validation of algorithms and to compare the accuracy of the methods on the JAFFE, Extended Cohn-Kanade images database.

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Double Feature Extraction Method of Ship-Radiated Noise Signal Based on Slope Entropy and Permutation Entropy

Entropy ◽

10.3390/e24010022 ◽

2021 ◽

Vol 24 (1) ◽

pp. 22

Author(s):

Yuxing Li ◽

Peiyuan Gao ◽

Bingzhao Tang ◽

Yingmin Yi ◽

Jianjun Zhang

Keyword(s):

Feature Extraction ◽

Extraction Method ◽

Nearest Neighbor ◽

Recognition Rate ◽

Noise Signal ◽

Permutation Entropy ◽

K Nearest Neighbor ◽

Feature Extraction Method ◽

Radiated Noise ◽

Single Feature

In order to accurately identify various types of ships and develop coastal defenses, a single feature extraction method based on slope entropy (SlEn) and a double feature extraction method based on SlEn combined with permutation entropy (SlEn&PE) are proposed. Firstly, SlEn is used for the feature extraction of ship-radiated noise signal (SNS) compared with permutation entropy (PE), dispersion entropy (DE), fluctuation dispersion entropy (FDE), and reverse dispersion entropy (RDE), so that the effectiveness of SlEn is verified, and SlEn has the highest recognition rate calculated by the k-Nearest Neighbor (KNN) algorithm. Secondly, SlEn is combined with PE, DE, FDE, and RDE, respectively, to extract the feature of SNS for a higher recognition rate, and SlEn&PE has the highest recognition rate after the calculation of the KNN algorithm. Lastly, the recognition rates of SlEn and SlEn&PE are compared, and the recognition rates of SlEn&PE are higher than SlEn by 4.22%. Therefore, the double feature extraction method proposed in this paper is more effective in the application of ship type recognition.

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Patch-Based Principal Component Analysis for Face Recognition

Computational Intelligence and Neuroscience ◽

10.1155/2017/5317850 ◽

2017 ◽

Vol 2017 ◽

pp. 1-9 ◽

Cited By ~ 10

Author(s):

Tai-Xiang Jiang ◽

Ting-Zhu Huang ◽

Xi-Le Zhao ◽

Tian-Hui Ma

Keyword(s):

Principal Component Analysis ◽

Face Recognition ◽

Nearest Neighbor ◽

Spatial Information ◽

Principal Component ◽

Component Analysis ◽

Two Dimensional ◽

Face Images ◽

Pca Method ◽

Local Spatial Information

We have proposed a patch-based principal component analysis (PCA) method to deal with face recognition. Many PCA-based methods for face recognition utilize the correlation between pixels, columns, or rows. But the local spatial information is not utilized or not fully utilized in these methods. We believe that patches are more meaningful basic units for face recognition than pixels, columns, or rows, since faces are discerned by patches containing eyes and noses. To calculate the correlation between patches, face images are divided into patches and then these patches are converted to column vectors which would be combined into a new “image matrix.” By replacing the images with the new “image matrix” in the two-dimensional PCA framework, we directly calculate the correlation of the divided patches by computing the total scatter. By optimizing the total scatter of the projected samples, we obtain the projection matrix for feature extraction. Finally, we use the nearest neighbor classifier. Extensive experiments on the ORL and FERET face database are reported to illustrate the performance of the patch-based PCA. Our method promotes the accuracy compared to one-dimensional PCA, two-dimensional PCA, and two-directional two-dimensional PCA.

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A High Performace of Local Binary Pattern on Classify Javanese Character Classification

Scientific Journal of Informatics ◽

10.15294/sji.v5i1.14017 ◽

2018 ◽

Vol 5 (1) ◽

pp. 8 ◽

Cited By ~ 1

Author(s):

Ajib Susanto ◽

Daurat Sinaga ◽

Christy Atika Sari ◽

Eko Hari Rachmawanto ◽

De Rosal Ignatius Moses Setiadi

Keyword(s):

Feature Extraction ◽

Image Classification ◽

Local Binary Pattern ◽

Nearest Neighbor ◽

Classification Algorithm ◽

K Nearest Neighbor ◽

Characteristic Extraction ◽

Research Objects ◽

Character Classification

The classification of Javanese character images is done with the aim of recognizing each character. The selected classification algorithm is K-Nearest Neighbor (KNN) at K = 1, 3, 5, 7, and 9. To improve KNN performance in Javanese character written by the author, and to prove that feature extraction is needed in the process image classification of Javanese character. In this study selected Local Binary Patter (LBP) as a feature extraction because there are research objects with a certain level of slope. The LBP parameters are used between [16 16], [32 32], [64 64], [128 128], and [256 256]. Experiments were performed on 80 training drawings and 40 test images. KNN values after combination with LBP characteristic extraction were 82.5% at K = 3 and LBP parameters [64 64].

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Pengenalan Wajah Menggunakan Metode Linear Discriminant Analysis dan k Nearest Neighbor

Jurnal ULTIMATICS ◽

10.31937/ti.v9i1.557 ◽

2017 ◽

Vol 9 (1) ◽

pp. 1-9

Author(s):

Fandiansyah Fandiansyah ◽

Jayanti Yusmah Sari ◽

Ika Putri Ningrum

Keyword(s):

Feature Extraction ◽

Face Recognition ◽

Discriminant Analysis ◽

Linear Discriminant Analysis ◽

Nearest Neighbor ◽

Experimental Result ◽

K Nearest Neighbor ◽

Linear Discriminant ◽

Nearest Neighbor Classifier ◽

Neighbor Classifier

Face recognition is one of the biometric system that mostly used for individual recognition in the absent machine or access control. This is because the face is the most visible part of human anatomy and serves as the first distinguishing factor of a human being. Feature extraction and classification are the key to face recognition, as they are to any pattern classification task. In this paper, we describe a face recognition method based on Linear Discriminant Analysis (LDA) and k-Nearest Neighbor classifier. LDA used for feature extraction, which directly extracts the proper features from image matrices with the objective of maximizing between-class variations and minimizing within-class variations. The features of a testing image will be compared to the features of database image using K-Nearest Neighbor classifier. The experiments in this paper are performed by using using 66 face images of 22 different people. The experimental result shows that the recognition accuracy is up to 98.33%. Index Terms—face recognition, k nearest neighbor, linear discriminant analysis.

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Research of SWNMF with New Iteration Rules for Facial Feature Extraction and Recognition

Symmetry ◽

10.3390/sym11030354 ◽

2019 ◽

Vol 11 (3) ◽

pp. 354

Author(s):

Jing Zhou

Keyword(s):

Feature Extraction ◽

Matrix Factorization ◽

Nonnegative Matrix Factorization ◽

Recognition Rate ◽

Nonnegative Matrix ◽

Facial Feature ◽

Face Database ◽

Base Matrix ◽

Sparse Nonnegative Matrix Factorization ◽

Pca Method

Weighted nonnegative matrix factorization (WNMF) is a technology for feature extraction, which can extract the feature of face dataset, and then the feature can be recognized by the classifier. To improve the performance of WNMF for feature extraction, a new iteration rule is proposed in this paper. Meanwhile, the base matrix U is sparse based on the threshold, and the new method is named sparse weighted nonnegative matrix factorization (SWNMF). The new iteration rules are based on the smaller iteration steps, thus, the search is more precise, therefore, the recognition rate can be improved. In addition, the sparse method based on the threshold is adopted to update the base matrix U, which can make the extracted feature more sparse and concentrate, and then easier to recognize. The SWNMF method is applied on the ORL and JAFEE datasets, and from the experiment results we can find that the recognition rates are improved extensively based on the new iteration rules proposed in this paper. The recognition rate of new SWNMF method reached 98% for ORL face database and 100% for JAFEE face database, respectively, which are higher than the PCA method, the sparse nonnegative matrix factorization (SNMF) method, the convex non-negative matrix factorization (CNMF) method and multi-layer NMF method.

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Improving Human Motion Classification by Applying Bagging and Symmetry to PCA-Based Features

Symmetry ◽

10.3390/sym11101264 ◽

2019 ◽

Vol 11 (10) ◽

pp. 1264 ◽

Cited By ~ 3

Author(s):

Tomasz Hachaj

Keyword(s):

Nearest Neighbor ◽

Recognition Rate ◽

Principal Component ◽

Human Motion ◽

Support Vector ◽

Human Observer ◽

Weak Classifier ◽

Motion Classification ◽

Observer Motion ◽

Human Motion Classification

This paper proposes a method for improving human motion classification by applying bagging and symmetry to Principal Component Analysis (PCA)-based features. In contrast to well-known bagging algorithms such as random forest, the proposed method recalculates the motion features for each “weak classifier” (it does not randomly sample a feature set). The proposed classification method was evaluated on a challenging (even to a human observer) motion capture recording dataset of martial arts techniques performed by professional karate sportspeople. The dataset consisted of 360 recordings in 12 motion classes. Because some classes of these motions might be symmetrical (which means that they are performed with a dominant left or right hand/leg), an analysis was conducted to determine whether accounting for symmetry could improve the recognition rate of a classifier. The experimental results show that applying the proposed classifiers’ bagging procedure increased the recognition rate (RR) of the Nearest-Neighbor (NNg) and Support Vector Machine (SVM) classifiers by more than 5% and 3%, respectively. The RR of one trained classifier (SVM) was higher when we did not use symmetry. On the other hand, the application of symmetry information for bagged NNg improved its recognition rate compared with the results without symmetry information. We can conclude that symmetry information might be helpful in situations in which it is not possible to optimize the decision borders of the classifier (for example, when we do not have direct information about class labels). The experiment presented in this paper shows that, in this case, bagging and mirroring might help find a similar object in the training set that shares the same class label. Both the dataset that was used for the evaluation and the implementation of the proposed method can be downloaded, so the experiment is easily reproducible.

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