Perspective methods of human identification: Ear biometrics

2008 ◽  
Vol 16 (1) ◽  
Author(s):  
M. Choraś
Keyword(s):  
Feature Extraction ◽  
Feature Space ◽  
Human Identification ◽  
Geometrical Approach ◽  
Geometrical Feature ◽  
Feature Vectors ◽  
Ear Biometrics ◽  
Geometrical Features ◽  
Is Development ◽  
Novel Algorithms

AbstractGeometrical methods of feature extraction from ear images in order to perform human identification are presented. Geometrical approach is motivated by the actual procedures used by police and forensic experts (so-called ear otoscopy). In their work, geometrical features of ears such as size, height, width, and shapes of earlobe are useful and valid proofs of identity. The contribution of the article is development of the new and original methods of geometrical feature extraction from 2D ear images. Four novel algorithms of ear feature extraction from contour images are described in detail. Moreover, identification results obtained for each of the methods, based on the distance of feature vectors in the feature space, are presented.

scholarly journals Development of the geometrical feature extraction tool using DBSCAN clustering for toolpath generation in incremental forming

2021 ◽  
Author(s):  
Aniket Nagargoje ◽  
Pavan K. Kankar ◽  
Prashant K. Jain ◽  
Puneet Tandon
Keyword(s):  
Feature Extraction ◽  
Convex Hull ◽  
Incremental Forming ◽  
Numerical Control ◽  
Free Form ◽  
Extraction Technology ◽  
Geometrical Feature ◽  
Dbscan Clustering ◽  
Geometrical Features ◽  
Complicated Geometries

Abstract Incremental forming is an emerging manufacturing technique, which allows the forming of the components without product-specific dies. The process uses Computer Numerical Control (CNC) machine tools to form complicated geometries. A punch, mostly a ball end tool, follows the toolpath obtained from the 3D model of the desired geometry to deform a blank into the desired shape. The objective of the current research is to develop a geometrical feature extraction technology to generate the toolpaths for the incremental forming process. A novel geometrical feature extraction tool, developed using attribute clustering techniques is proposed here. The proposed technology extracts geometrical features from the sliced contoured data of the geometry using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering and convex hull algorithms. Initially, the DBSCAN clustering technique is used for parent feature extraction. Later, child features are extracted from the parent features with the help of a convex hull algorithm. This paper discusses in detail the algorithms developed to extract geometrical features. The performance of the developed algorithms is validated with three different multi-featured geometries representing different types of families like geometries with natural partitions (features are connected with edges), geometries with smoothly connected features, and free form geometries. The results show that the techniques work successfully with different complicated geometries to extract the features. The outcome of this research would help evolve a system for an automatic generation of the feature-based toolpaths for the incremental forming (and similar) processes.

scholarly journals Unknown SAR Target Identification Method Based on Feature Extraction Network and KLD–RPA Joint Discrimination

2021 ◽  
Vol 13 (15) ◽  
pp. 2901
Author(s):  
Zhiqiang Zeng ◽  
Jinping Sun ◽  
Congan Xu ◽  
Haiyang Wang
Keyword(s):  
Feature Extraction ◽  
Position Angle ◽  
Automatic Target Recognition ◽  
Identification Accuracy ◽  
Training Dataset ◽  
Target Feature ◽  
Practical Applications ◽  
Feature Vectors ◽  
Identification Method ◽  
Leibler Divergence

Recently, deep learning (DL) has been successfully applied in automatic target recognition (ATR) tasks of synthetic aperture radar (SAR) images. However, limited by the lack of SAR image target datasets and the high cost of labeling, these existing DL based approaches can only accurately recognize the target in the training dataset. Therefore, high precision identification of unknown SAR targets in practical applications is one of the important capabilities that the SAR–ATR system should equip. To this end, we propose a novel DL based identification method for unknown SAR targets with joint discrimination. First of all, the feature extraction network (FEN) trained on a limited dataset is used to extract the SAR target features, and then the unknown targets are roughly identified from the known targets by computing the Kullback–Leibler divergence (KLD) of the target feature vectors. For the targets that cannot be distinguished by KLD, their feature vectors perform t-distributed stochastic neighbor embedding (t-SNE) dimensionality reduction processing to calculate the relative position angle (RPA). Finally, the known and unknown targets are finely identified based on RPA. Experimental results conducted on the MSTAR dataset demonstrate that the proposed method can achieve higher identification accuracy of unknown SAR targets than existing methods while maintaining high recognition accuracy of known targets.

scholarly journals Handwritten English Character Recognition and translate English to Devnagari Words

2019 ◽  
pp. 142-151
Author(s):  
Shivali Parkhedkar ◽  
Shaveri Vairagade ◽  
Vishakha Sakharkar ◽  
Bharti Khurpe ◽  
Arpita Pikalmunde ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Character Recognition ◽  
Computational Time ◽  
Feature Vectors ◽  
Form Processing ◽  
Knn Classifier ◽  
Gabor Feature ◽  
Handwritten Document ◽  
The Individual

In our proposed work we will accept the challenges of recognizing the words and we will work to win the challenge. The handwritten document is scanned using a scanner. The image of the scanned document is processed victimization the program. Each character in the word is isolated. Then the individual isolated character is subjected to “Feature Extraction” by the Gabor Feature. Extracted features are passed through KNN classifier. Finally we get the Recognized word. Character recognition is a process by which computer recognizes handwritten characters and turns them into a format which a user can understand. Computer primarily based pattern recognition may be a method that involves many sub process. In today’s surroundings character recognition has gained ton of concentration with in the field of pattern recognition. Handwritten character recognition is beneficial in cheque process in banks, form processing systems and many more. Character recognition is one in all the favored and difficult space in analysis. In future, character recognition creates paperless environment. The novelty of this approach is to achieve better accuracy, reduced computational time for recognition of handwritten characters. The proposed method extracts the geometric features of the character contour. These features are based on the basic line types that forms the character skeleton. The system offers a feature vector as its output. The feature vectors so generated from a training set, were then used to train a pattern recognition engine based on Neural Networks so that the system can be benchmarked. The algorithm proposed concentrates on the same. It extracts totally different line varieties that forms a specific character. It conjointly also concentrates on the point options of constant. The feature extraction technique explained was tested using a Neural Network which was trained with the feature vectors obtained from the proposed method.

scholarly journals A Fiber Vibration Signal Identification Method Based on the Combination of EWT-FE and LSTM

2021 ◽  
Vol 2112 (1) ◽  
pp. 012020
Author(s):  
Xin Zhang ◽  
Qingmo Ja ◽  
SaiSai Ruan ◽  
Qin Hu
Keyword(s):  
Feature Extraction ◽  
Recognition Rate ◽  
Real Life ◽  
Vibration Signal ◽  
Machine Learning Algorithms ◽  
Signal Identification ◽  
Feature Vectors ◽  
Identification Method ◽  
Effective Manner ◽  
Empirical Wavelet Transform

Abstract As the optical fiber perimeter security system is widely used in real life, how to identify the types of intrusion events in a timely and effective manner is becoming a major research hotspot. At present, in this field, various signal feature extraction algorithms are usually used to extract intrusion signal features to form feature vectors, and then machine learning algorithms are used to classify the feature vectors to achieve the role of identifying the types of intrusion events. As a common signal feature extraction algorithm, the EMD algorithm has been widely used in the feature extraction of various vibration signals, but it will have the problem of modal aliasing and affect the feature extraction effect of the signal. Therefore, EWT, VMD and other algorithms have been successively used proposed to improve modal aliasing. On the basis of fully comparing the existing algorithms, this paper proposes a fiber vibration signal identification method that decomposes the signal through the empirical wavelet transform (EWT) algorithm and then extracts the fuzzy entropy (FE) of each component, and uses LSTM for classification. The final experiment shows that the method can identify four kinds of fiber intrusion signals in time and effectively, with an average recognition accuracy rate of 97.87%, especially for flap and knock recognition rate of 100%.

Feature Extraction of Video Using Artificial Neural Network

2017 ◽  
Vol 11 (2) ◽  
pp. 25-40 ◽  
Author(s):  
Yoshihiro Hayakawa ◽  
Takanori Oonuma ◽  
Hideyuki Kobayashi ◽  
Akiko Takahashi ◽  
Shinji Chiba ◽  
...  
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Image Recognition ◽  
High Performance ◽  
Feature Space ◽  
Middle Layer ◽  
Image Feature ◽  
Consecutive Series ◽  
Form Analysis ◽  
Low Dimensional

In deep neural networks, which have been gaining attention in recent years, the features of input images are expressed in a middle layer. Using the information on this feature layer, high performance can be demonstrated in the image recognition field. In the present study, we achieve image recognition, without using convolutional neural networks or sparse coding, through an image feature extraction function obtained when identity mapping learning is applied to sandglass-style feed-forward neural networks. In sports form analysis, for example, a state trajectory is mapped in a low-dimensional feature space based on a consecutive series of actions. Here, we discuss ideas related to image analysis by applying the above method.

scholarly journals Research on rolling bearing fault diagnosis based on multi-dimensional feature extraction and evidence fusion theory

2019 ◽  
Vol 6 (2) ◽  
pp. 181488 ◽  
Author(s):  
Jingchao Li ◽  
Yulong Ying ◽  
Yuan Ren ◽  
Siyu Xu ◽  
Dongyuan Bi ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Real Time ◽  
Rolling Bearing ◽  
Diagnostic Methods ◽  
Bearing Fault ◽  
Feature Vectors ◽  
Bearing Fault Diagnosis ◽  
Fusion Theory

Rolling bearing failure is the main cause of failure of rotating machinery, and leads to huge economic losses. The demand of the technique on rolling bearing fault diagnosis in industrial applications is increasing. With the development of artificial intelligence, the procedure of rolling bearing fault diagnosis is more and more treated as a procedure of pattern recognition, and its effectiveness and reliability mainly depend on the selection of dominant characteristic vector of the fault features. In this paper, a novel diagnostic framework for rolling bearing faults based on multi-dimensional feature extraction and evidence fusion theory is proposed to fulfil the requirements for effective assessment of different fault types and severities with real-time computational performance. Firstly, a multi-dimensional feature extraction strategy on the basis of entropy characteristics, Holder coefficient characteristics and improved generalized box-counting dimension characteristics is executed for extracting health status feature vectors from vibration signals. And, secondly, a grey relation algorithm is used to calculate the basic belief assignments (BBAs) using the extracted feature vectors, and lastly, the BBAs are fused through the Yager algorithm for achieving bearing fault pattern recognition. The related experimental study has illustrated the proposed method can effectively and efficiently recognize various fault types and severities in comparison with the existing intelligent diagnostic methods based on a small number of training samples with good real-time performance, and may be used for online assessment.

Sign in / Sign up

Export Citation Format

Share Document