An Algorithm of Frequent Neighboring Class Set Mining with Constraint Class

In this paper, the purpose is to find a method that can be more suited to facial expression change and also improve the recognition rate. The proposed system contains three parts, wavelet transform, Fisher linear discriminant method feature extraction and face classification. The basic idea of the proposed method is that first extract the low-frequency components through wavelet transform, then the low-frequency images mapped into a low-dimensional space by PCA transform, and finally the utilization of LDA feature extraction method in low-dimensional space. The algorithms were tested on ORL and Yale face database, respectively. Experimental results shows that the proposed method not only improve the recognition rate, but also improve the recognition speed. This method can effectively overcome the impact of expression changes on face recognition, and play a certain role in inhibition of expression.

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Feature Extraction by Decryption to Target

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1008-1009.1509 ◽

2014 ◽

Vol 1008-1009 ◽

pp. 1509-1512

Author(s):

Qing E Wu ◽

Hong Wang ◽

Li Fen Ding

Keyword(s):

Feature Extraction ◽

Wavelet Transform ◽

Extraction Method ◽

Recognition Rate ◽

Region Of Interest ◽

Correct Recognition ◽

Palmprint Recognition ◽

Feature Extraction Method ◽

Energy Feature ◽

Correct Recognition Rate

To carry out an effective classification and recognition for target, this paper studied the target owned characteristics, discussed a decryption algorithm, gave a feature extraction method based on the decryption process, and extracted the feature of palmprint in region of interest. Moreover, this paper used the wavelet transform to extract the energy feature of target, gave an approach on matching and recognition to improve the correctness and efficiency of existing recognition approaches, and compared it with existing approaches of palmprint recognition by experiments. The experiment results show that the correct recognition rate of the approach in this paper is improved averagely by 2.34% than that of the existing recognition approaches.

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Phase Autocorrelation Bark Wavelet Transform (PACWT) Features for Robust Speech Recognition

Archives of Acoustics ◽

10.1515/aoa-2015-0004 ◽

2015 ◽

Vol 40 (1) ◽

pp. 25-31 ◽

Cited By ~ 2

Author(s):

Sayf A. Majeed ◽

Hafizah Husain ◽

Salina A. Samad

Keyword(s):

Feature Extraction ◽

Wavelet Transform ◽

Speech Recognition ◽

Extraction Method ◽

Recognition Performance ◽

Recognition Rate ◽

Feature Extraction Method ◽

Female Data ◽

Recognition Systems ◽

New Feature

Abstract In this paper, a new feature-extraction method is proposed to achieve robustness of speech recognition systems. This method combines the benefits of phase autocorrelation (PAC) with bark wavelet transform. PAC uses the angle to measure correlation instead of the traditional autocorrelation measure, whereas the bark wavelet transform is a special type of wavelet transform that is particularly designed for speech signals. The extracted features from this combined method are called phase autocorrelation bark wavelet transform (PACWT) features. The speech recognition performance of the PACWT features is evaluated and compared to the conventional feature extraction method mel frequency cepstrum coefficients (MFCC) using TI-Digits database under different types of noise and noise levels. This database has been divided into male and female data. The result shows that the word recognition rate using the PACWT features for noisy male data (white noise at 0 dB SNR) is 60%, whereas it is 41.35% for the MFCC features under identical conditions

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FEATURE EXTRACTION OF MOTOR IMAGERY EEG BASED ON WAVELET TRANSFORM AND HIGHER-ORDER STATISTICS

International Journal of Wavelets Multiresolution and Information Processing ◽

10.1142/s0219691310003535 ◽

2010 ◽

Vol 08 (03) ◽

pp. 373-384 ◽

Cited By ~ 27

Author(s):

RENHUAN YANG ◽

AIGUO SONG ◽

BAOGUO XU

Keyword(s):

Feature Extraction ◽

Wavelet Transform ◽

Order Statistics ◽

Motor Imagery ◽

Higher Order ◽

Higher Order Statistics ◽

Data Set ◽

Linear Discriminant ◽

Feature Extraction Method ◽

Harmonic Wavelet

Feature extraction plays an important role in brain-computer interface (BCI) systems. In order to characterize the motor imagery related rhythm and higher-order statistics information contained within the EEG signals, a novel feature extraction method based on harmonic wavelet transform and bispectrum is developed and applied to the recognition of right and left motor imageries for developing EEG-based BCI systems. The experimental results on the Graz BCI data set have shown that the separability of the two classes features extracted by the proposed method is notable. Its performance was evaluated by a linear discriminant analysis (LDA) classifier. The recognition accuracy of 90% was obtained. The recognition results have demonstrated the effectiveness of the proposed method. This method provides an effective way for EEG feature extraction in BCI system.

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Classification of Sleep Stages Using Multi-wavelet Time Frequency Entropy and LDA

Methods of Information in Medicine ◽

10.3414/me09-01-0054 ◽

2010 ◽

Vol 49 (03) ◽

pp. 230-237 ◽

Cited By ~ 44

Author(s):

K. Lweesy ◽

N. Khasawneh ◽

M. Fraiwan ◽

H. Wenz ◽

H. Dickhaus ◽

...

Keyword(s):

Feature Extraction ◽

Wavelet Transform ◽

Discriminant Analysis ◽

Sleep Stage ◽

Continuous Wavelet ◽

Eeg Signal ◽

Linear Discriminant ◽

Time Frequency ◽

Mother Wavelets ◽

Sleep Stage Scoring

Summary Background: The process of automatic sleep stage scoring consists of two major parts: feature extraction and classification. Features are normally extracted from the polysomno-graphic recordings, mainly electroencephalograph (EEG) signals. The EEG is considered a non-stationary signal which increases the complexity of the detection of different waves in it. Objectives: This work presents a new technique for automatic sleep stage scoring based on employing continuous wavelet transform (CWT) and linear discriminant analysis (LDA) using different mother wavelets to detect different waves embedded in the EEG signal. Methods: The use of different mother wave-lets increases the ability to detect waves in the EEG signal. The extracted features were formed based on CWT time frequency entropy using three mother wavelets, and the classification was performed using the linear discriminant analysis. Thirty-two data sets from the MIT-BIH database were used to evaluate the performance of the proposed method. Results: Features of a single EEG signal were extracted successfully based on the time frequency entropy using the continuous wavelet transform with three mother wavelets. The proposed method has shown to outperform the classification based on a CWT using a single mother wavelet. The accuracy was found to be 0.84, while the kappa coefficient was 0.78. Conclusions: This work has shown that wavelet time frequency entropy provides a powerful tool for feature extraction for the non-stationary EEG signal; the accuracy of the classification procedure improved when using multiple wavelets compared to the use of single wavelet time frequency entropy.

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Real-Time Vehicle License Plate Recognition Based on Scanning and 2D Haar Discrete Wavelet Transform

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.284-287.2402 ◽

2013 ◽

Vol 284-287 ◽

pp. 2402-2406 ◽

Cited By ~ 4

Author(s):

Rong Choi Lee ◽

King Chu Hung ◽

Huan Sheng Wang

Keyword(s):

Wavelet Transform ◽

Discrete Wavelet Transform ◽

Real Time ◽

Recognition Rate ◽

Low Frequency ◽

Experimental Result ◽

Discrete Wavelet ◽

License Plate ◽

License Plate Recognition ◽

Image Pixels

This thesis is to approach license-plate recognition using 2D Haar Discrete Wavelet Transform (HDWT) and artificial neural network. This thesis consists of three main parts. The first part is to locate and extract the license-plate. The second part is to train the license-plate. The third part is to real time scan recognition. We select only after the second 2D Haar Discrete Wavelet Transform the image of low-frequency part, image pixels into one-sixteen, thus, reducing the image pixels and can increase rapid implementation of recognition and the computer memory. This method is to scan for car license plate recognition, without make recognition of the individual characters. The experimental result can be high recognition rate.

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Research on Adaptive Face Gender Recognition Based on Compressive Sensing

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.989-994.4187 ◽

2014 ◽

Vol 989-994 ◽

pp. 4187-4190 ◽

Cited By ~ 1

Author(s):

Lin Zhang

Keyword(s):

Feature Extraction ◽

Compressive Sensing ◽

Recognition Accuracy ◽

Recognition Rate ◽

Low Frequency ◽

Face Image ◽

Gender Recognition ◽

Recognition Method ◽

Frequency Information ◽

Learning Machine

An adaptive gender recognition method is proposed in this paper. At first, do multiwavlet transform to face image and get its low frequency information, then do feature extraction to the low frequency information using compressive sensing (CS), use extreme learning machine (ELM) to achieve gender recognition finally. In the process of feature extraction, we use genetic algorithm (GA) to get the number of measurements of CS in order to gain the highest recognition rate, so the method can adaptive access optimal performance. Experimental results show that compared with PDA and LDA, the new method improved the recognition accuracy substantially.

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A Region-Based Iris Feature Extraction Method Based on 2D-Wavelet Transform

Biometric ID Management and Multimodal Communication - Lecture Notes in Computer Science ◽

10.1007/978-3-642-04391-8_39 ◽

2009 ◽

pp. 301-307 ◽

Cited By ~ 4

Author(s):

Nima Tajbakhsh ◽

Khashayar Misaghian ◽

Naghmeh Mohammadi Bandari

Keyword(s):

Feature Extraction ◽

Wavelet Transform ◽

Extraction Method ◽

Feature Extraction Method ◽

Iris Feature Extraction

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An Incremental Version of L-MVU for the Feature Extraction of MI-EEG

Computational Intelligence and Neuroscience ◽

10.1155/2019/4317078 ◽

2019 ◽

Vol 2019 ◽

pp. 1-19

Author(s):

Mingai Li ◽

Hongwei Xi ◽

Xiaoqing Zhu

Keyword(s):

Feature Extraction ◽

Extraction Method ◽

Average Energy ◽

High Dimensional ◽

Dimensional Representation ◽

Feature Extraction Method ◽

Sample Data ◽

Out Of Sample ◽

Nonlinear Features ◽

Low Dimensional

Due to the nonlinear and high-dimensional characteristics of motor imagery electroencephalography (MI-EEG), it can be challenging to get high online accuracy. As a nonlinear dimension reduction method, landmark maximum variance unfolding (L-MVU) can completely retain the nonlinear features of MI-EEG. However, L-MVU still requires considerable computation costs for out-of-sample data. An incremental version of L-MVU (denoted as IL-MVU) is proposed in this paper. The low-dimensional representation of the training data is generated by L-MVU. For each out-of-sample data, its nearest neighbors will be found in the high-dimensional training samples and the corresponding reconstruction weight matrix be calculated to generate its low-dimensional representation as well. IL-MVU is further combined with the dual-tree complex wavelet transform (DTCWT), which develops a hybrid feature extraction method (named as IL-MD). IL-MVU is applied to extract the nonlinear features of the specific subband signals, which are reconstructed by DTCWT and have the obvious event-related synchronization/event-related desynchronization phenomenon. The average energy features of α and β waves are calculated simultaneously. The two types of features are fused and are evaluated by a linear discriminant analysis classifier. Based on the two public datasets with 12 subjects, extensive experiments were conducted. The average recognition accuracies of 10-fold cross-validation are 92.50% on Dataset 3b and 88.13% on Dataset 2b, and they gain at least 1.43% and 3.45% improvement, respectively, compared to existing methods. The experimental results show that IL-MD can extract more accurate features with relatively lower consumption cost, and it also has better feature visualization and self-adaptive characteristics to subjects. The t-test results and Kappa values suggest the proposed feature extraction method reaches statistical significance and has high consistency in classification.

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A Feature Extraction Method of Wheelset-Bearing Fault Based on Wavelet Sparse Representation with Adaptive Local Iterative Filtering

Shock and Vibration ◽

10.1155/2020/2019821 ◽

2020 ◽

Vol 2020 ◽

pp. 1-20

Author(s):

Zhan Xing ◽

Jianhui Lin ◽

Yan Huang ◽

Cai Yi

Keyword(s):

Feature Extraction ◽

Sparse Representation ◽

Extraction Method ◽

High Speed ◽

Optimal Solution ◽

Feature Extraction Method ◽

Bearing Fault ◽

Fault Features ◽

Iterative Filtering ◽

The Impact

The feature extraction of wheelset-bearing fault is important for the safety service of high-speed train. In recent years, sparse representation is gradually applied to the fault diagnosis of wheelset-bearing. However, it is difficult for traditional sparse representation to extract fault features ideally when some strong interference components are imposed on the signal. Therefore, this paper proposes a novel feature extraction method of wheelset-bearing fault based on the wavelet sparse representation with adaptive local iterative filtering. In this method, the adaptive local iterative filtering reduces the impact of interference components effectively and contributes to the extraction of sparse impulses. The wavelet sparse representation, which adopts L1-regularized optimization for a globally optimal solution in sparse coding, extracts intrinsic features of fault in the wavelet domain. To validate the effectiveness of this proposed method, both simulated signals and experimental signals are analyzed. The results show that the fault features of wheelset-bearing are sufficiently extracted by the proposed method.

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