scholarly journals A Novel Two-Level Fusion Feature for Mixed ECG Identity Recognition

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2052
Author(s):  
Xin Liu ◽  
Yujuan Si ◽  
Weiyi Yang
Keyword(s):  
Principal Component ◽  
Individual Identification ◽  
Support Vector ◽  
Identification System ◽  
Identity Recognition ◽  
Deep Feature ◽  
Single Feature ◽  
The Individual ◽  
Fusion Feature ◽  
Level Fusion

In recent years, with the increasing standard of biometric identification, it is difficult to meet the requirements of data size and accuracy in practical application for training a single ECG (electrocardiogram) database. The paper aims to construct a recognition model for processing multi-source data and proposes a novel ECG identification system based on two-level fusion features. Firstly, the features of Hilbert transform and power spectrum are extracted from the segmented heartbeat data, then two features are combined into a set and normalized to obtain the elementary fusion feature. Secondly, PCANet (Principal Component Analysis Network) is used to extract the discriminative deep feature of signal, and MF (MaxFusion) algorithm is proposed to fuse and compress the two layers learning features. Finally, a linear support vector machine (SVM) is used to obtain labels of single feature classification and complete the individual identification. The recognition results of the proposed two-level fusion PCANet deep recognition network achieve more than 95% on ECG-ID, MIT-BIH, and PTB public databases. Most importantly, the recognition accuracy of the mixed database can reach 99.77%, which includes 426 individuals.

scholarly journals Support Vector Machine for Behavior-Based Driver Identification System

2010 ◽  
Vol 2010 ◽  
pp. 1-11 ◽  
Author(s):  
Huihuan Qian ◽  
Yongsheng Ou ◽  
Xinyu Wu ◽  
Xiaoning Meng ◽  
Yangsheng Xu
Keyword(s):  
Support Vector Machine ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Identification System ◽  
Dynamic Features ◽  
Experimental Platform ◽  
Driving Behaviors ◽  
The Individual ◽  
Driver Identification

We present an intelligent driver identification system to handle vehicle theft based on modeling dynamic human behaviors. We propose to recognize illegitimate drivers through their driving behaviors. Since human driving behaviors belong to a dynamic biometrical feature which is complex and difficult to imitate compared with static features such as passwords and fingerprints, we find that this novel idea of utilizing human dynamic features for enhanced security application is more effective. In this paper, we first describe our experimental platform for collecting and modeling human driving behaviors. Then we compare fast Fourier transform (FFT), principal component analysis (PCA), and independent component analysis (ICA) for data preprocessing. Using machine learning method of support vector machine (SVM), we derive the individual driving behavior model and we then demonstrate the procedure for recognizing different drivers by analyzing the corresponding models. The experimental results of learning algorithms and evaluation are described.

scholarly journals Feature and Decision Level Fusion in Children Multimodal Biometrics

2020 ◽  
Vol 8 (5) ◽  
pp. 2522-2527
Keyword(s):  
Nearest Neighbor ◽  
Principal Component ◽  
Identification Accuracy ◽  
Support Vector ◽  
Biometric System ◽  
K Nearest Neighbor ◽  
Decision Level ◽  
Multimodal Biometric System ◽  
Decision Level Fusion ◽  
Level Fusion

In this paper, we design method for recognition of fingerprint and IRIS using feature level fusion and decision level fusion in Children multimodal biometric system. Initially, Histogram of Gradients (HOG), Gabour and Maximum filter response are extracted from both the domains of fingerprint and IRIS and considered for identification accuracy. The combination of feature vector of all the possible features is recommended by biometrics traits of fusion. For fusion vector the Principal Component Analysis (PCA) is used to select features. The reduced features are fed into fusion classifier of K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Navie Bayes(NB). For children multimodal biometric system the suitable combination of features and fusion classifiers is identified. The experimentation conducted on children’s fingerprint and IRIS database and results reveal that fusion combination outperforms individual. In addition the proposed model advances the unimodal biometrics system.

scholarly journals Analysis of Age Invariant Face Recognition using QSVM-PCA

2020 ◽  
Author(s):  
ASHUTOSH DHAMIJA ◽  
R.B DUBEY
Keyword(s):  
Principal Component Analysis ◽  
Face Recognition ◽  
Principal Component ◽  
Support Vector ◽  
Facial Features ◽  
Identity Recognition ◽  
The Face ◽  
A New Technique ◽  
Age Range ◽  
Face Aging

Abstract Forage, face recognition is one of the most demanding field challenges, since aging affects the shape and structure of the face. Age invariant face recognition (AIFR) is a relatively new area in face recognition studies, which in real-world implementations recently gained considerable interest due to its huge potential and relevance. The AIFR, however, is still evolving and evolving, providing substantial potential for further study and progress inaccuracy. Major issues with the AIFR involve major variations in appearance, texture, and facial features and discrepancies in position and illumination. These problems restrict the AIFR systems developed and intensify identity recognition tasks. To address this problem, a new technique Quadratic Support Vector Machine- Principal Component Analysis (QSVM-PCA) is introduced. Experimental results suggest that our QSVM-PCA achieved better results especially when the age range is larger than other existing techniques of face-aging datasets of FGNET. The maximum accuracy achieved by demonstrated methodology is 98.87%.

Influence of the Intra-Modal Facial Information for an Identification Approach

2014 ◽  
pp. 318-342
Author(s):  
Carlos M. Travieso ◽  
Marcos del Pozo-Baños ◽  
Jaime R. Ticay-Rivas ◽  
Jesús B. Alonso
Keyword(s):  
Principal Component ◽  
Component Analysis ◽  
Multimodal Fusion ◽  
Support Vector ◽  
Identification System ◽  
Facial Features ◽  
Classifier Systems ◽  
Lighting Conditions ◽  
Facial Information ◽  
Identification Approach

This chapter presents a comprehensive study on the influence of the intra-modal facial information for an identification approach. It was developed and implemented a biometric identification system by merging different intra-multimodal facial features: mouth, eyes, and nose. The Principal Component Analysis, Independent Component Analysis, and Discrete Cosine Transform were used as feature extractors. Support Vector Machines were implemented as classifier systems. The recognition rates obtained by multimodal fusion of three facial features has reached values above 97% in each of the databases used, confirming that the system is adaptive to images from different sources, sizes, lighting conditions, etc. Even though a good response has been shown when the three facial traits were merged, an acceptable performance has been shown when merging only two facial features. Therefore, the system is robust against problems in one isolate sensor or occlusion in any biometric trait. In this case, the success rate achieved was over 92%.

Individual Identification Using Images of Eyes

1997 ◽  
Vol 9 (1) ◽  
pp. 41-45
Author(s):  
Satoshi Tanigawa ◽  
◽  
Masafumi Uchida ◽  
Hideto Ide
Keyword(s):  
Individual Identification ◽  
Identification System ◽  
Information Industry ◽  
Business Information ◽  
Crime Investigation ◽  
Security Business ◽  
The Individual ◽  
Business Security

Individual identification is required in various fields such as credit business, security business, information industry and crime investigation. This paper describes the individual identification system using images of eyes. With this system having used images data of 20 registered persons and 20 unregistered persons, we could obtain a high recogniniton rate and showing how efficient this system is.

scholarly journals Research on emitter individual identification technology based on Automatic Dependent Surveillance–Broadcast signal

2021 ◽  
Vol 17 (2) ◽  
pp. 155014772199262
Author(s):  
Shiwen Chen ◽  
Junjian Yuan ◽  
Xiaopeng Xing ◽  
Xin Qin
Keyword(s):  
Individual Recognition ◽  
Evidence Theory ◽  
Field Measurements ◽  
Training Data ◽  
Individual Identification ◽  
Identification System ◽  
Individual Feature ◽  
Data Set ◽  
Theoretical Simulation ◽  
The Individual

Aiming at the shortcomings of the research on individual identification technology of emitters, which is primarily based on theoretical simulation and lack of verification equipment to conduct external field measurements, an emitter individual identification system based on Automatic Dependent Surveillance–Broadcast is designed. On one hand, the system completes the individual feature extraction of the signal preamble. On the other hand, it realizes decoding of the transmitter’s individual identity information and generates an individual recognition training data set, on which we can train the recognition network to achieve individual signal recognition. For the collected signals, six parameters were extracted as individual features. To reduce the feature dimensions, a Bessel curve fitting method is used for four of the features. The spatial distribution of the Bezier curve control points after fitting is taken as an individual feature. The processed features are classified with multiple classifiers, and the classification results are fused using the improved Dempster–Shafer evidence theory. Field measurements show that the average individual recognition accuracy of the system reaches 88.3%, which essentially meets the requirements.

scholarly journals I3S Classic and Insect Species Identification of Diptera and Hymenoptera (Mosquitoes and Bees)

2016 ◽  
Author(s):  
Nayna Vyas-Patel ◽  
John D Mumford
Keyword(s):  
Apis Mellifera ◽  
Image Recognition ◽  
Bombus Terrestris ◽  
Individual Recognition ◽  
Sensu Stricto ◽  
Individual Identification ◽  
Insect Species ◽  
Identification System ◽  
Accurate Identification ◽  
The Individual

AbstractA number of image recognition systems have been specifically formulated for the individual recognition of large animals. These programs are versatile and can easily be adapted for the identification of smaller individuals such as insects. The Interactive Individual Identification System, I3S Classic, initially produced for the identification of individual whale sharks was employed to distinguish between different species of mosquitoes and bees, utilising the distinctive vein pattern present on insect wings. I3S Classic proved to be highly effective and accurate in identifying different species and sexes of mosquitoes and bees, with 80% to100% accuracy for the majority of the species tested. The sibling species Apis mellifera and Apis mellifera carnica were both identified with100% accuracy. Bombus terrestris terrestris and Bombus terrestris audax; were also identified and separated with high degrees of accuracy (90% to 100% respectively for the fore wings and 100% for the hind wings). When both Anopheles gambiae sensu stricto and Anopheles arabiensis were present in the database, they were identified with 94% and 100% accuracy respectively, allowing for a morphological and non-molecular method of sorting between these members of the sibling complex. Flat, not folded and entire, rather than broken, wing specimens were required for accurate identification. Only one wing image of each sex was required in the database to retrieve high levels of accurate results in the majority of species tested. The study describes how I3S was used to identify different insect species and draws comparisons with the use of the CO1 algorithm. As with CO1, I3S Classic proved to be suitable software which could reliably be used to aid the accurate identification of insect species. It is emphasised that image recognition for insect species should always be used in conjunction with other identifying characters in addition to the wings, as is the norm when identifying species using traditional taxonomic keys.

scholarly journals Categorization of Common Pigmented Skin Lesions (CPSL) using Multi-Deep Features and Support Vector Machine

2021 ◽  
Author(s):  
SANTI BEHERA ◽  
PRABIRA SETHY
Keyword(s):  
Support Vector Machine ◽  
Skin Cancer ◽  
Principal Component ◽  
Skin Lesions ◽  
Classification Model ◽  
Healthcare Sector ◽  
Support Vector ◽  
Svm Classifier ◽  
Deep Feature ◽  
Pigmented Skin Lesions

Abstract The skin is the main organ. It is approximately 8 pounds for the average adult. Our skin is a truly wonderful organ. It isolates us and shields our bodies from hazards. However, the skin is also vulnerable to damage and distracted from its original appearance; brown, black, or blue, or combinations of those colors, known as pigmented skin lesions. These common pigmented skin lesions (CPSL) are the leading factor of skin cancer, or can say these are the primary causes of skin cancer. In the healthcare sector, the categorization of CPSL is the main problem because of inaccurate outputs, overfitting, and higher computational costs. Hence, we proposed a classification model based on multi-deep feature and support vector machine (SVM) for the classification of CPSL. The proposed system comprises two phases: first, evaluate the 11 CNN model's performance in the deep feature extraction approach with SVM. Then, concatenate the top performed three CNN model's deep features and with the help of SVM to categorize the CPSL. In the second step, 8192 and 12288 features are obtained by combining binary and triple networks of 4096 features from the top performed CNN model. These features are also given to the SVM classifiers. The SVM results are also evaluated with principal component analysis (PCA) algorithm to the combined feature of 8192 and 12288. The highest results are obtained with 12288 features. The experimentation results, the combination of the deep feature of Alexnet, VGG16 & VGG19, achieved the highest accuracy of 91.7% using SVM classifier. As a result, the results show that the proposed methods are a useful tool for CPSL classification.

scholarly journals The use of multidimensional methods of analysis of the intra-breed variability of fat content in milk of dairy cattle

2019 ◽  
Vol 21 (91) ◽  
pp. 3-9
Author(s):  
S. S. Kramarenko ◽  
N. I. Kuzmichova ◽  
A. S. Kramarenko
Keyword(s):  
Latent Variables ◽  
Principal Component ◽  
Original Data ◽  
Fat Content ◽  
Individual Identification ◽  
Linear Discriminant ◽  
Rate Of Increase ◽  
The Individual ◽  
Main Component ◽  
New Variables

The analysis included data on the origin and milk productivity of 109 first-born red steppe breed, which were descendants of five bulls-offspring (Narcissus, Topol, Tangens, Neptune, and Orpheus) and were kept in SE “Plemproductor Stepove” (Mykolaiv region, Ukraine ) during the years 2001–2014. The purpose of this study was to analyze the fat content of milk during different months of lactation (MFP1, MFP2,…, MFP10) to determine latent variables that best describe the variability of dairy cows' productivity in this herd. High correlation estimates of fat milk scores in different lactation months have been established. According to the results of the Principal Component Analysis, based on the (co)variation matrix of fat content in milk, three new variables (PC1, PC2, and PC3) were identified, which accounted for about 82% of the variability of the original data. The First Main Component (PC1) explained 53.5%, Second (PC2) – 17.7%, and Third (PC3) – 10.6% of the variability of the original data, respectively. PC1 was highly correlated with MFP4-MFP10 and, thus, it distributed the animals according to their fat content level. PC2 was highly positively correlated with MFP8-MFP10 but highly negatively correlated with M FP1-MFP3 and thus it shows the rate of increase in fat content in milk during lactation. PC3 characterizes the variability of fat content in milk during the first and second half of lactation. The Linear Discriminant Analysis found that the MFP1-MFP2 and MFP9-MFP10 scores contributed most to the discrimination among the five subpopulations. The individual identification of the offspring groups of different bulls according to the cross-check classification ranged from 44.4% (Topol) to 87.5% (Orpheus) of cows, which were correctly assigned to their own group.

Analysis of age invariant face recognition using quadratic support vector machine-principal component analysis

2021 ◽  
pp. 1-15
Author(s):  
Ashutosh Dhamija ◽  
R. B. Dubey
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Face Recognition ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Identity Recognition ◽  
The Face ◽  
A New Technique ◽  
Age Range

Face recognition is one of the most challenging and demanding field, since aging affects the shape and structure of the face. Age invariant face recognition is a relatively new area in face recognition studies, which in real-world implementations recently gained considerable interest due to its huge potential and relevance. The Age invariant face recognition, however, is still evolving and evolving, providing substantial potential for further study and progress inaccuracy. Major issues with the age invariant face recognition involve major variations in appearance, texture, and facial features and discrepancies in position and illumination. These problems restrict the age invariant face recognition systems developed and intensify identity recognition tasks. To address this problem, a new technique Quadratic Support Vector Machine- Principal Component Analysis (QSVM-PCA) is introduced. Experimental results suggest that our QSVM-PCA achieved better results especially when the age range is larger than other existing techniques of face-aging dataset of FGNET. The maximum accuracy achieved by demonstrated methodology is 98.87%.

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