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

scholarly journals NEURO-FUZZY SYSTEM FOR DETECTING PD PATIENTS BASED ON EUCLID DISTANCE, FFT, AND PCA

2020 ◽  
Vol 20 (09) ◽  
pp. 2040017
Author(s):  
SEOK-WOO JANG ◽  
SANG-HONG LEE
Keyword(s):  
Pressure Sensor ◽  
Euclidean Distance ◽  
Performance Enhancement ◽  
Principal Component ◽  
Sensor Data ◽  
Second Step ◽  
Neuro Fuzzy ◽  
Euclidean Distances ◽  
Left And Right ◽  
The Individual

This study proposes a method to distinguish between healthy people and Parkinson’s disease patients using sole pressure sensor data, neural network with weighted fuzzy membership (NEWFM), and preprocessing techniques. The preprocessing techniques include fast Fourier transform (FFT), Euclidean distance, and principal component analysis (PCA), to remove noise in the data for performance enhancement. To make the features usable as inputs for NEWFM, the Euclidean distances between the left and right sole pressure sensor data were used at the first step. In the second step, the frequency scales of the Euclidean distances extracted in the first step were divided into individual scales by the FFT using the Hamming method. In the final step, 1–15 dimensions were extracted as the features of NEWFM from the individual scales by the FFT extracted in the second step by the PCA. An accuracy of 75.90% was acquired from the eight dimensions as the inputs of NEWFM.

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.

R-HOG Feature-Based Off-Line Odia Handwritten Character Recognition

2020 ◽  
pp. 196-210 ◽  
Author(s):  
Abhisek Sethy ◽  
Prashanta Kumar Patra
Keyword(s):  
Character Recognition ◽  
Recognition Rate ◽  
Principal Component ◽  
Recognition System ◽  
Feature Extraction Method ◽  
Proposed Model ◽  
Gradient Based ◽  
Feature Based ◽  
Handwritten Recognition ◽  
Fold Cross Validation

Offline handwritten recognition system for Odia characters has received attention in the last few years. Although the recent research showed that there has been lots of work reported in different language, there is limited research carried out in Odia character recognition. Most of Odia characters are round in nature, similar in orientation and size also, which increases the ambiguity among characters. This chapter has harnessed the rectangle histogram-oriented gradient (R-HOG) for feature extraction method along with the principal component analysis. This gradient-based approach has been able to produce relevant features of individual ones in to the proposed model and helps to achieve high recognition rate. After certain simulations, the respective analysis of classifier shows that SVM performed better than quadratic. Among them, SVM produces with 98.8% and QC produces 96.8%, respectively, as recognition rate. In addition to it, the authors have also performed the 10-fold cross-validation to make the system more robust.

Face Recognition using Segmental Euclidean Distance

2011 ◽  
Vol 61 (5) ◽  
pp. 431 ◽  
Author(s):  
Farrukh Sayeed ◽  
Madasu Hanmandlu ◽  
Abdul Quaiyum Ansari
Keyword(s):  
Euclidean Distance ◽  
Learning Algorithm ◽  
Recognition Rate ◽  
Principal Component ◽  
Support Vector ◽  
Svm Classifier ◽  
Test Image ◽  
Integral Image ◽  
Fuzzy Features ◽  
The Face

<p>In this paper an attempt has been made to detect the face using the combination of integral image along with the cascade structured classifier which is built using Adaboost learning algorithm. The detected faces are then passed through a filtering process for discarding the non face regions. They are individually split up into five segments consisting of forehead, eyes, nose, mouth and chin. Each segment is considered as a separate image and Eigenface also called principal component analysis (PCA) features of each segment is computed. The faces having a slight pose are also aligned for proper segmentation. The test image is also segmented similarly and its PCA features are found. The segmental Euclidean distance classifier is used for matching the test image with the stored one. The success rate comes out to be 88 per cent on the CG(full) database created from the databases of California Institute and Georgia Institute. However the performance of this approach on ORL(full) database with the same features is only 70 per cent. For the sake of comparison, DCT(full) and fuzzy features are tried on CG and ORL databases but using a well known classifier, support vector machine (SVM). Results of recognition rate with DCT features on SVM classifier are increased by 3 per cent over those due to PCA features and Euclidean distance classifier on the CG database. The results of recognition are improved to 96 per cent with fuzzy features on ORL database with SVM.</p><p><strong>Defence Science Journal, 2011, 61(5), pp.431-442</strong><strong><strong>, DOI:http://dx.doi.org/10.14429/dsj.61.1178</strong></strong></p>

scholarly journals Study of Second and Fourth Digit Lengths and Their Ratios With Physical Attributes in Nepali Population

2018 ◽  
Vol 16 (2) ◽  
pp. 78-81
Author(s):  
Biswas Satyal ◽  
Abhishek Satyal
Keyword(s):  
Ring Finger ◽  
The Body ◽  
Behavioral Traits ◽  
Digit Ratios ◽  
Hand Deformities ◽  
Significant Difference ◽  
Physical Attributes ◽  
Physical Traits ◽  
Left And Right ◽  
Males And Females

Introduction: The ratio of the lengths of the index and the ring finger (2D:4D ratio) is generally different between men and women. A number of studies have shown a correlation between the 2D:4D digit ratio and various physical and behavioral traits. The aim of the present study is to investigate the association of the index (2nd) and ring (4th) digit ratios with some physical traits in Nepal population. Material & Method: 200 students (100 males and 100 females) between ages of 18 years and above were randomly selected with exclusion of those with hand deformities. The digit lengths were measured from the basal crease to the tips usingvernier calipers. The 2D:4D ratios were determined for each subject while height and weight were used to calculate the body mass index and data analyzed. The study was conducted between January 2018 to November 2018. Result: The result of the anthropometric study of the differences in index (2D) and ring (4D) and their ratios shows that there was a significant difference between the length of index finger (2D), ring finger (4D) and the ratios of right hand's 2D:4D in both males and females. There was appositive correlation between the second digit length and Height and weight in males and females both on right and left sides. The 2D:4D ratio for both left and right hand did not show any positive correlation with height, weight or BMI of an individual.

scholarly journals Parameters Optimization and Application of SVM Based on PCA-Particle Swarm Algorithm

2019 ◽  
pp. 325-330 ◽  
Author(s):  
Qingmi Yang
Keyword(s):  
Parameter Optimization ◽  
Recognition Accuracy ◽  
Recognition Rate ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Principal Component ◽  
Pso Algorithm ◽  
Parameters Optimization ◽  
Support Vector ◽  
Particle Swarm Algorithm

The parameter optimization of Support Vector Machine (SVM) has been a hot research direction. To improve the optimization rate and classification performance of SVM, the Principal Component Analysis (PCA) - Particle Swarm Optimization (PSO) algorithm was used to optimize the penalty parameters and kernel parameters of SVM. PSO which is to find the optimal solution through continuous iteration combined with PCA that eliminates linear redundancy between data, effectively enhance the generalization ability of the model, reduce the optimization time of parameters, and improve the recognition accuracy. The simulation comparison experiments on 6 UCI datasets illustrate that the excellent performance of the PCA-PSO-SVM model. The results show that the proposed algorithm has higher recognition accuracy and better recognition rate than simple PSO algorithm in the parameter optimization of SVM. It is an effective parameter optimization method.

Clustering Algorithm for Vehicle’s Driving Data Feature based on Integrated Navigation

2021 ◽  
Vol 13 (4) ◽  
Author(s):  
Na Guo ◽  
Yiyi Zhu
Keyword(s):  
Clustering Algorithm ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
Integrated Navigation ◽  
Clustering Problem ◽  
Incremental Method ◽  
Feature Parameters ◽  
Vehicle Acceleration ◽  
Feature Based ◽  
Clustering Problems

The clustering result of K-means clustering algorithm is affected by the initial clustering center and the clustering result is not always global optimal. Therefore, the clustering analysis of vehicle’s driving data feature based on integrated navigation is carried out based on global K-means clustering algorithm. The vehicle mathematical model based on GPS/DR integrated navigation is constructed and the vehicle’s driving data based on GPS/DR integrated navigation, such as vehicle acceleration, are collected. After extracting the vehicle’s driving data features, the feature parameters of vehicle’s driving data are dimensionally reduced based on kernel principal component analysis to reduce the redundancy of feature parameters. The global K-means clustering algorithm converts clustering problem into a series of sub-cluster clustering problems. At the end of each iteration, an incremental method is used to select the next cluster of optimal initial centers. After determining the optimal clustering number, the feature clustering of vehicle’s driving data is completed. The experimental results show that the global K-means clustering algorithm has a clustering error of only 1.37% for vehicle’s driving data features and achieves high precision clustering for vehicle’s driving data features.

A Lyapunov Theory-Based Neural Network Approach for Face Recognition

2010 ◽  
pp. 23-48 ◽  
Author(s):  
Li-Minn Ang ◽  
King Hann Lim ◽  
Kah Phooi Seng ◽  
Siew Wen Chin
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Principal Component ◽  
Lyapunov Stability Theory ◽  
Recognition System ◽  
Lyapunov Theory ◽  
Neural Network Approach ◽  
Linear Discriminant ◽  
Feature Based ◽  
Definition Of

This chapter presents a new face recognition system comprising of feature extraction and the Lyapunov theory-based neural network. It first gives the definition of face recognition which can be broadly divided into (i) feature-based approaches, and (ii) holistic approaches. A general review of both approaches will be given in the chapter. Face features extraction techniques including Principal Component Analysis (PCA) and Fisher’s Linear Discriminant (FLD) are discussed. Multilayered neural network (MLNN) and Radial Basis Function neural network (RBF NN) will be reviewed. Two Lyapunov theory-based neural classifiers: (i) Lyapunov theory-based RBF NN, and (ii) Lyapunov theory-based MLNN classifiers are designed based on the Lyapunov stability theory. The design details will be discussed in the chapter. Experiments are performed on two benchmark databases, ORL and Yale. Comparisons with some of the existing conventional techniques are given. Simulation results have shown good performance for face recognition using the Lyapunov theory-based neural network systems.

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

2019 ◽  
Vol 8 (2) ◽  
pp. 1-25
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 ◽  
Component Analysis ◽  
Expression Recognition ◽  
Distance Method

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.

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

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1264 ◽  
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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