Research of Face Recognition Based on Wavelet Transform and EHMM

2013 ◽  
Vol 694-697 ◽  
pp. 1998-2002
Author(s):  
Xian Wei Li ◽  
Guo Long Chen
Keyword(s):  
Face Recognition ◽  
Wavelet Transform ◽  
Principal Components ◽  
Hidden Markov ◽  
Recognition Rate ◽  
Affect Recognition ◽  
Method Of Principal Components ◽  
Critical Problems ◽  
Components Analysis ◽  
Hidden Markov Mode

A method was presented which was based on Wavelet Transform and Embedded Hidden Markov Mode (EHMM). The proposed algorithm can reduce the affections such as illuminations which affects the recognition rate using the method of Principal Components Analysis (PCA).Analyzed the critical problems that affect recognition rates in Wavelet Transform. Experimental results show that the presented method can get better results.

Face Recognition Algorithm Based on Kernel Collaborative Representation

2013 ◽  
Vol 756-759 ◽  
pp. 3590-3595
Author(s):  
Liang Zhang ◽  
Ji Wen Dong
Keyword(s):  
Face Recognition ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Recognition Algorithm ◽  
Experimental Results ◽  
Collaborative Representation ◽  
Nonlinear Structures ◽  
Components Analysis ◽  
Kernel Principal Components Analysis ◽  
The Relationship

Aiming at solving the problems of occlusion and illumination in face recognition, a new method of face recognition based on Kernel Principal Components Analysis (KPCA) and Collaborative Representation Classifier (CRC) is developed. The KPCA can obtain effective discriminative information and reduce the feature dimensions by extracting faces nonlinear structures features, the decisive factor. Considering the collaboration among the samples, the CRC which synthetically consider the relationship among samples is used. Experimental results demonstrate that the algorithm obtains good recognition rates and also improves the efficiency. The KCRC algorithm can effectively solve the problem of illumination and occlusion in face recognition.

scholarly journals Learning Kernel-Based Robust Disturbance Dictionary for Face Recognition

2019 ◽  
Vol 9 (6) ◽  
pp. 1189 ◽  
Author(s):  
Biwei Ding ◽  
Hua Ji
Keyword(s):  
Face Recognition ◽  
Discriminant Analysis ◽  
Principal Components ◽  
Dictionary Learning ◽  
Recognition Rate ◽  
Computation Time ◽  
Kernel Discriminant Analysis ◽  
Training Samples ◽  
Sparse Representation Classifier ◽  
Facial Changes

In this paper, a kernel-based robust disturbance dictionary (KRDD) is proposed for face recognition that solves the problem in modern dictionary learning in which significant components of signal representation cannot be entirely covered. KRDD can effectively extract the principal components of the kernel by dimensionality reduction. KRDD not only performs well with occluded face data, but is also good at suppressing intraclass variation. KRDD learns the robust disturbance dictionaries by extracting and generating the diversity of comprehensive training samples generated by facial changes. In particular, a basic dictionary, a real disturbance dictionary, and a simulated disturbance dictionary are acquired to represent data from distinct subjects to fully represent commonality and disturbance. Two of the disturbance dictionaries are modeled by learning few kernel principal components of the disturbance changes, and then the corresponding dictionaries are obtained by kernel discriminant analysis (KDA) projection modeling. Finally, extended sparse representation classifier (SRC) is used for classification. In the experimental results, KRDD performance displays great advantages in recognition rate and computation time compared with many of the most advanced dictionary learning methods for face recognition.

POSE-EXPRESSION NORMALIZATION FOR FACE RECOGNITION USING CONNECTED COMPONENTS ANALYSIS

2006 ◽  
Vol 20 (06) ◽  
pp. 869-881
Author(s):  
JAE-YOUNG CHOI ◽  
TAEG-KEUN WHANGBO ◽  
YOUNG-GYU YANG ◽  
MURLIKRISHNA VISWANATHAN ◽  
NAK-BIN KIM
Keyword(s):  
Face Recognition ◽  
Recognition Rate ◽  
Connected Components ◽  
Recognition Method ◽  
Facial Region ◽  
Detection Model ◽  
Recognition Systems ◽  
Components Analysis ◽  
Empirical Tests ◽  
Connected Components Analysis

Accurate measurement of poses and expressions can increase the efficiency of recognition systems by avoiding the recognition of spurious faces. This paper presents a novel and robust pose-expression invariant face recognition method in order to improve the existing face recognition techniques. First, we apply the TSL color model for detecting facial region and estimate the vector X-Y-Z of face using connected components analysis. Second, the input face is mapped by a deformable 3D facial model. Third, the mapped face is transformed to the frontal face which appropriates for face recognition by the estimated pose vector and action unit of expression. Finally, the damaged regions which occur during the process of normalization are reconstructed using PCA. Several empirical tests are used to validate the application of face detection model and the method for estimating facial poses and expression. In addition, the tests suggest that recognition rate is greatly boosted through the normalization of the poses and expression.

Face Recognition Based on Wavelet Neural Network

2011 ◽  
Vol 204-210 ◽  
pp. 216-219
Author(s):  
Hong Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Face Recognition ◽  
Wavelet Transform ◽  
Recognition Rate ◽  
Human Face ◽  
Recognition Method ◽  
Human Face Recognition ◽  
The Face ◽  
Simulation Testing

It's well known that the technology of human face recognition has become a hot topicin pattern recognition field. Though a lot of progress has been made by many researchersthese years, many key problems still have to be solved in order to popularize the application of face recognition because of the complexity of face recognition. The background, development and main methods of face recognition are introducedfirstly in this paper, then a face recognition method which is based on wavelet transform,KL transform and BP neural networks is used in the paper.Here the face feature extraction includes wavelet transform and KL transform.Moreover,the recognition classifier is BP neural networks.The simulation testing in the paper holds good recognition rate.

scholarly journals Face recognition system based on principal components analysis and distance measures

2018 ◽  
Vol 7 (2.21) ◽  
pp. 15 ◽  
Author(s):  
T Meenpal ◽  
Aarti Goyal ◽  
Ankita Meenpal
Keyword(s):  
Face Recognition ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Recognition System ◽  
Data Representation ◽  
Vital Role ◽  
Distance Measures ◽  
Face Recognition System ◽  
Components Analysis ◽  
Matching Techniques

Face recognition plays a vital role and has a huge scope in the field of biometrics, image processing, artificial intelligence, pattern recognition and computer vision. This paper presents an approach to perform face recognition using Principal Components Analysis (PCA) as feature extraction technique and different distance measures as matching techniques. The proposed method is developed after the deep study of a number of face recognition methods and their outcomes. In the proposed method, Principal Components Analysis is used for facial features extraction and data representation. It generates eigenvalues of the facial images, hence, reduces the dimensionality. The recognition is produced using three different matching techniques (Euclidean, Manhattan and Mahalanobis) and the results are` presented. Yale and Aberdeen Face Databases are used to test and analyze the results of the proposed method.  

scholarly journals THE AUTHORSHIP OF THE HISTORIA AUGUSTA: TWO NEW COMPUTATIONAL STUDIES

2016 ◽  
Vol 59 (2) ◽  
pp. 140-157 ◽  
Author(s):  
JUSTIN A. STOVER ◽  
MIKE KESTEMONT
Keyword(s):  
Principal Components ◽  
Computational Analysis ◽  
Computational Studies ◽  
Multiple Authorship ◽  
Established Method ◽  
Method Of Principal Components ◽  
Authorship Verification ◽  
Simple Alternative ◽  
Historia Augusta ◽  
Components Analysis

Abstract The case of the Historia Augusta, a collection of imperial biographies from Hadrian to Carus supposedly written by six different authors, provided the impetus for the introduction of computational methods into the Echtheitskritik of ancient authors in 1979. After a flurry of studies in the 1990s, interest waned, particularly because most of those studies seemed to support conclusions incompatible with the scholarly consensus on the question. In the paper, we approach this question with the new tool of authorship verification – one of the most promising approaches in forensic stylometry today – as well as the established method of principal components analysis to demonstrate that there is no simple alternative between single and multiple authorship, and that the results of a computational analysis are in fact compatible with the results obtained from historical, literary, and philological analysis.

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