scholarly journals Recognition Algorithm of Popular Elements of Ethnic Minority Traditional Clothing Based on PCA

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hu Juan
Keyword(s):  
Ethnic Minority ◽  
National Culture ◽  
Euclidean Distance ◽  
Recognition Rate ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Pca Method ◽  
Neural Network Algorithm ◽  
Matching Degree ◽  
Better Than

Image recognition of ethnic minority costumes is helpful for people to understand, carry forward, and inherit national culture. Taking the minority clothing image as the research object, the image enhancement and threshold segmentation are completed; the principal component features of the minority clothing image are extracted by PCA method; and the image matching degree is obtained according to the principle of minimizing the Euclidean distance. Finally, the calculation process of the PCA method is optimized by a wavelet transform algorithm to realize the recognition of popular elements of minority traditional clothing. The comparative experimental results show that the PCA + BP neural network algorithm is better than the other two recognition algorithms in recognition rate and recognition time.

A Novel Face Recognition Algorithm

2013 ◽  
Vol 718-720 ◽  
pp. 2055-2061
Author(s):  
Cai Rang Zhaxi ◽  
Yue Guang Li
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Feature Space ◽  
Recognition Algorithm ◽  
Analysis Method ◽  
Fisher Linear Discriminant ◽  
Linear Discriminant ◽  
Discriminant Analysis Method ◽  
Pca Method ◽  
Better Than

This paper firstly analyzes the principle of face recognition algorithm, studies feature selection and distance criterion problem, puts forward the defects of PCA face recognition algorithm and LDA face recognition algorithm. According to the deficiencies and shortcomings of PCA face recognition algorithm and LDA face recognition algorithm, this paper proposes a solution -- PCA+LDA. The method uses the PCA method to reduce the dimensionality of feature space, it uses Fisher linear discriminant analysis method to classification, the realization of face recognition. Experiments show that, this method can not only improve the feature extraction speed, but also the recognition rate is better than single PCA method and LDA method.

A Face Recognition Algorithm Based on Block PCA and Wavelet Transform

2010 ◽  
Vol 40-41 ◽  
pp. 523-530 ◽  
Author(s):  
Dong Cheng Shi ◽  
Qing Qing Wang
Keyword(s):  
Face Recognition ◽  
Wavelet Transform ◽  
Recognition Rate ◽  
Low Frequency ◽  
Principal Component ◽  
Recognition Algorithm ◽  
Global Features ◽  
Face Area ◽  
The Face ◽  
Pca Method

As the most successful method of linear distinguish, principal component analysis(PCA) method is widely used in identify areas, such as face recognition. But traditional PCA is influenced by light conditions, facial expression and it extracts the global features of the image, so the recognition rate is not very high. In order to improve more accurately identify facial features and extract local features which account for a larger contribution to the identification. This paper brings up a method of a block face recognition based on wavelet transform (WT-BPCA). In the algorithm, face images are done two-dimensional wavelet decomposition, then from which extract low frequency sub-images. According to different face area makes different contribution to recognition, we use sub-block PCA method. According to the contribution of the block recognition results generate weighting factors, the face recognition rate based on PCA is effectively improved. Finally we construct classification to recognite. Do experiments in the ORL face database. Results show that this method is superior to the method of the traditional PCA.

The Application of the Fuzzy ART Neural Network Algorithm

2014 ◽  
Vol 513-517 ◽  
pp. 1783-1786 ◽  
Author(s):  
Ming Gu
Keyword(s):  
Neural Network ◽  
Simulation Experiment ◽  
Recognition Rate ◽  
Learning Rule ◽  
Recognition Algorithm ◽  
Fuzzy Art ◽  
Fast Learning ◽  
Average Recognition Rate ◽  
Neural Network Algorithm ◽  
Better Than

An algorithm based on fuzzy ART neural network which can deal with online-learning and recognition of the known and unknown faces at the same time was designed and realized. Based on structure and learning rule of the fuzzy ART system, face recognition algorithm was designed. The simulation experiment results show that average recognition rate of not fast learning is better than fast learning. Not fast learning is accepted to get 89.83% online and 99.42% offline recognition rate.

The Recognition and Research of Improved Probabilistic Neural Network

2013 ◽  
Vol 756-759 ◽  
pp. 2819-2824
Author(s):  
Xiao Jing Shang
Keyword(s):  
Neural Network ◽  
Gesture Recognition ◽  
Probabilistic Neural Network ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Bp Network ◽  
Training Time ◽  
Neural Network Structure ◽  
Neural Network Algorithm ◽  
Better Than

Probabilistic neural network compared with the traditional BP neural network structure is simpler and it is faster to be identificated, so it is widely used in the field of pattern recognition. This paper is mainly focused on similar gesture recognition research, propose an probabilistic neural network gesture recognition algorithm. The simulation results show that the improved probabilistic neural network algorithm on the recognition rate and training time is better than the traditional BP network.

Application of Gabor Dictionary and SL0 Norm in Disguise Face Recognition

2013 ◽  
Vol 278-280 ◽  
pp. 1211-1214
Author(s):  
Jun Ying Zeng ◽  
Jun Ying Gan ◽  
Yi Kui Zhai
Keyword(s):  
Face Recognition ◽  
Function Approximation ◽  
Multiple Scales ◽  
Recognition Rate ◽  
Principal Component ◽  
Facial Feature ◽  
Training Image ◽  
Recognition Algorithm ◽  
The Face ◽  
Gabor Dictionary

A fast sparse representation face recognition algorithm based on Gabor dictionary and SL0 norm is proposed in this paper. The Gabor filters, which could effectively extract local directional features of the image at multiple scales, are less sensitive to variations of illumination, expression and camouflage. SL0 algorithm, with the advantages of calculation speed，require fewer measurement values by continuously differentiable function approximation L0 norm and reconstructed sparse signal by minimizing the approximate L0 norm. The algorithm obtain the local feature face by extracting the Gabor face feature, reduce the dimensions by principal component analysis, fast sparse classify by the SL0 norm. Under camouflage condition, The algorithm block the Gabor facial feature and improve the speed of formation of the Gabor dictionary. The experimental results on AR face database show that the proposed algorithm can improve recognition speed and recognition rate to some extent and can generalize well to the face recognition, even with a few training image per class.

Face Recognition by PCA and Improved LBP Fusion Algorithm

2015 ◽  
Vol 734 ◽  
pp. 562-567 ◽  
Author(s):  
En Zeng Dong ◽  
Yan Hong Fu ◽  
Ji Gang Tong
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Texture Feature ◽  
Principal Component ◽  
Texture Features ◽  
Rotation Invariant ◽  
Fusion Algorithm ◽  
Face Images ◽  
The Face ◽  
Pca Method

This paper proposed a theoretically efficient approach for face recognition based on principal component analysis (PCA) and rotation invariant uniform local binary pattern texture features in order to weaken the effects of varying illumination conditions and facial expressions. Firstly, the rotation invariant uniform LBP operator was adopted to extract the local texture feature of the face images. Then PCA method was used to reduce the dimensionality of the extracted feature and get the eigenfaces. Finally, the nearest distance classification was used to distinguish each face. The method has been accessed on Yale and ATR-Jaffe face databases. Results demonstrate that the proposed method is superior to standard PCA and its recognition rate is higher than the traditional PCA. And the proposed algorithm has strong robustness against the illumination changes, pose, rotation and expressions.

scholarly journals THE IDENTIFICATION OF EAR PRINTS USING COMPLEX GABOR FILTERS

2012 ◽  
Vol 6 (1) ◽  
pp. 32
Author(s):  
Alexander A S Gunawan ◽  
Heni Kurniaty ◽  
Wikaria Gazali
Keyword(s):  
Euclidean Distance ◽  
Recognition Rate ◽  
Principal Component ◽  
Gabor Filters ◽  
Forensic Evidence ◽  
Behavioral Traits ◽  
Feature Based ◽  
Left And Right ◽  
Dimension Space ◽  
Principal Componentanalysis

Biometrics is a method used to recognize humans based on one or a few characteristicsphysical or behavioral traits that are unique such as DNA, face, fingerprints, gait, iris, palm, retina,signature and sound. Although the facts that ear prints are found in 15% of crime scenes, ear printsresearch has been very limited since the success of fingerprints modality. The advantage of the useof ear prints, as forensic evidence, are it relatively unchanged due to increased age and have fewervariations than faces with expression variation and orientation. In this research, complex Gaborfilters is used to extract the ear prints feature based on texture segmentation. Principal componentanalysis (PCA) is then used for dimensionality-reduction where variation in the dataset ispreserved. The classification is done in a lower dimension space defined by principal componentsbased on Euclidean distance. In experiments, it is used left and right ear prints of ten respondentsand in average, the successful recognition rate is 78%. Based on the experiment results, it isconcluded that ear prints is suitable as forensic evidence mainly when combined with otherbiometric modalities.Keywords: Biometrics; Ear prints; Complex Gabor filters; Principal component analysis;Euclidean distance

A License Plate Recognition Method Based on Wavelet Transform and Symmetrical Principal Component Analysis Algorithm

2013 ◽  
Vol 427-429 ◽  
pp. 1743-1746
Author(s):  
Xue Feng Deng
Keyword(s):  
Principal Component Analysis ◽  
Wavelet Transform ◽  
Recognition Rate ◽  
Principal Component ◽  
Component Analysis ◽  
Recognition Algorithm ◽  
License Plate ◽  
License Plate Recognition ◽  
Analysis Algorithm ◽  
Principal Component Analysis Algorithm

In the past, the license plate recognition algorithm has some shortcomings, such as low recognition rate, slow speed of recognition, inaccurate license plate positioning. This paper proposes a new license plate location algorithm based on wavelet transform and the principal component analysis algorithm is used to feature extraction.The experimental results show that this method can reduce the amount of computation and improve the system recognition rate.

scholarly journals Eye Movement Prediction Based on Adaptive BP Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yushou Tang ◽  
Jianhuan Su
Keyword(s):  
Neural Network ◽  
Eye Movements ◽  
Eye Movement ◽  
Bp Neural Network ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Eye Movement Tracking ◽  
Neural Network Algorithm ◽  
Real World Datasets ◽  
Eye Movements During Reading

This paper uses adaptive BP neural networks to conduct an in-depth examination of eye movements during reading and to predict reading effects. An important component for the implementation of visual tracking systems is the correct detection of eye movement using the actual data or real-world datasets. We propose the identification of three typical types of eye movements, namely, gaze, leap, and smooth navigation, using an adaptive BP neural network-based recognition algorithm for eye movement. This study assesses the BP neural network algorithm using the eye movement tracking sensors. For the experimental environment, four types of eye movement signals were acquired from 10 subjects to perform preliminary processing of the acquired signals. The experimental results demonstrate that the recognition rate of the algorithm provided in this paper can reach up to 97%, which is superior to the commonly used CNN algorithm.

scholarly journals An Improved Face Recognition Using Neighborhood Defined Modular Phase Congruency Based Kernel PCA

2012 ◽  
pp. 288-296
Author(s):  
M.Lokeswara Reddy ◽  
P.Ramana Reddy
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Principal Component ◽  
Visual Image ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Kernel Pca ◽  
Phase Congruency ◽  
Face Images ◽  
Facial Recognition System

A face recognition algorithm based on NMPKPCA algorithm presented in this paper. The proposed algorithm when compared with conventional Principal component analysis (PCA) algorithms has an improved recognition Rate for face images with large variations in illumination, facial expressions. In this technique, first phase congruency features are extracted from the face image so that effects due to illumination variations are avoided by considering phase component of image. Then, face images are divided into small sub images and the kernel PCA approach is applied to each of these sub images. but, dividing into small or large modules creates some problems in recognition. So a special modulation called neighborhood defined modularization approach presented in this paper, so that effects due to facial variations are avoided. Then, kernel PCA has been applied to each module to extract features. So a feature extraction technique for improving recognition accuracy of a visual image based facial recognition system presented in this paper.

Sign in / Sign up

Export Citation Format

Share Document