scholarly journals A Fast and Efficient Palmprint Identification Method for a Large Database

1970 ◽  
Vol 5 (2) ◽  
pp. 118-126 ◽  
Author(s):  
Mongkon Sakdanupab ◽  
Nongluk Covavisaruch
Keyword(s):  
Template Matching ◽  
Hamming Distance ◽  
Gabor Filter ◽  
Experimental Results ◽  
Large Database ◽  
Identification Methods ◽  
Palmprint Identification ◽  
Identification Method ◽  
Head Line ◽  
Log Gabor

This paper proposes a fast and efficient palmprint identification method for a large database. The process is accelerated as a result of our efficient palmprint classification and matching scheme. Palmprint classification method is based on principle lines which are life line, head line and heart line. Palmprints’ features are extracted with Log-Gabor filter and matched with Hamming distance in the most potential palmprint group/category, and if necessary, continues orderly to the less potential ones. Experiments are done with 2 hand databases, Visgraph database and CU-CGCI hand database. Experimental results show that the proposed method can greatly reduce the number of template matching from 100% (as in general identification methods) to 33.2-38.2% while maintaining the equivalent EER as the general identification method.

A Person Identification Method Using a Top-View Head Image from an Overhead Camera

2012 ◽  
Vol 16 (6) ◽  
pp. 696-703 ◽  
Author(s):  
Ryota Nakatani ◽  
◽  
Daichi Kouno ◽  
Kazutaka Shimada ◽  
Tsutomu Endo
Keyword(s):  
Body Size ◽  
Background Subtraction ◽  
Experimental Results ◽  
Hair Color ◽  
Identification Task ◽  
Person Identification ◽  
Identification Methods ◽  
Adaboost Algorithm ◽  
Identification Method ◽  
Top View

In this paper, we describe a novel image-based person identification task. Conventional face-based person identification methods have a low tolerance for occluded situations, such as overlapping of people in an image. We focus on an image from an overhead camera. Using the overhead camera reduces restrictions on the installation location of the camera and solves the problem of occluded images. First, our method identifies a person area in a captured image by using background subtraction. Then, it extracts four features from the area: (1) body size, (2) hair color, (3) hairstyle, and (4) hair whorl. We apply the four features to the AdaBoost algorithm. Experimental results show the effectiveness of our method.

scholarly journals Authentication Based on Texture Analysis And SVM Classification

2011 ◽  
pp. 61-66
Author(s):  
Sanjay Kumar Mohanty ◽  
Prasant Kumar Pattnaik
Keyword(s):  
Feature Vector ◽  
Hamming Distance ◽  
Gabor Filter ◽  
Gabor Wavelet ◽  
Support Vector ◽  
Wavelet Filter ◽  
Canny Edge Detection ◽  
Circular Hough Transform ◽  
Canny Edge ◽  
Log Gabor

This paper presents for identification and here used a fusion mechanism that amalgamates both, a Canny Edge detection and a Circular Hough Transform to detect the iris boundaries in the eye’s digital image. We then applied the Gabor Wavelet filter instead of using 1D Log-Gabor filter in order to exact the deterministic patterns in a person’s iris in the form of a feature vector. By comparing the quantized vectors using the Hamming Distance operator, we determine finally and for classification used Support vector Machine.

scholarly journals Identification Based on Texturer Analysis And Gabor Wavelet Filter

2012 ◽  
pp. 29-32
Author(s):  
Sanjay Kumar Mohanty ◽  
Niladree Nandini Das
Keyword(s):  
Digital Image ◽  
Hough Transform ◽  
Hamming Distance ◽  
Gabor Filter ◽  
Gabor Wavelet ◽  
Wavelet Filter ◽  
Canny Edge Detection ◽  
Canny Edge ◽  
Distance Operator ◽  
Log Gabor

This paper presents for human identification and here used a fusion mechanism that amalgamates both, Canny Edge Detection and a circulat hough transform to detect the iris boundaries in the eye's digital image. Applying the Gabor Wavelet filter isted of using 1D Log-Gabor filter in order to exact the deterministic pattern in a person's iris in the form of a featute vector. By comparing the quantized vectors using the hamming distance operator, we determine finally.

scholarly journals A Novel Approach for Diagnosis of Diabetes Using Iris Image Processing Technique and Evaluation Parameters

2021 ◽  
Author(s):  
Shilpa Jagtap ◽  
J L Mudegaonkar ◽  
Sanjay Patil ◽  
Dinesh Bhoyar
Keyword(s):  
Wavelet Transforms ◽  
Template Matching ◽  
Hamming Distance ◽  
Gabor Filter ◽  
Rejection Rate ◽  
Image Processing Technique ◽  
Computational Time ◽  
Support Vector ◽  
Adaptive Boosting ◽  
Human Iris

This paper presented here deals with study of identification and verification approach of Diabetes based on human iris pattern. In the pre-processing of this work, region of interest according to color (ROI) concept is used for iris localization, Dougman's rubber sheet model is used for normalization and Circular Hough Transform can be used for pupil and boundary detection. To extract features, Gabor Filter, Histogram of Oriented Gradients, five level decomposition of wavelet transforms likeHaar, db2, db4, bior 2.2, bior6.8 waveletscan be used. Binary coding scheme binaries’ the feature vector coefficients and classifier like hamming distance, Support Vector Machine (SVM), Adaptive Boosting (AdaBoost), Neural Networks (NN), Random Forest (RF) and Linear Discriminative Analysis (LDA) with shrinkage parametercan be used for template matching. Performance parameters such as Computational time, Hamming distance variation, False Acceptance Rate (FAR), False Rejection Rate (FRR), Accuracy, and Match ratio can be calculated for the comparison purpose.

SISTEM PENGENALAN MANUSIA MELALUI KEUNIKAN FISIOLOGIS SELAPUT PELANGI MATA DENGAN MENGGUNAKAN FILTER LOG GABOR

KONVERGENSI ◽  
2019 ◽  
Vol 13 (1) ◽  
Author(s):  
Bima Agung Pratama ◽  
Fajar Astuti Hermawati
Keyword(s):  
Iris Recognition ◽  
Hamming Distance ◽  
Rubber Sheet ◽  
Log Gabor

Penelitian ini mengajukan sebuah sistem pengenalan manusia melalui karakteristik pola fisiologis selaput pelangi (iris) matanya. Pengenalan selaput pelangi mata (iris recognition) merupakan suatu teknologi pengolahan citra yang digunakan untuk mendeteksi dan menampilkan selaput pelangi (iris) pada alat indera mata manusia saat kelopak mata terbuka. Terdapat beberapa tahap dalam proses pengenalan menggunakan pola iris mata manusia. Langkah pertama adalah melakukan proses segmentasi untuk mendapatkan daerah selaput pelangi (iris) mata yang berbentuk melingkat dengan menggunakan metode operator integro-diferensial. Selanjutnya dilakukan proses normalisasi hasil segmentasi menjadi bentuk polar dengan menerapkan metode metode Daughman’s rubber sheet model. Setelah itu diterapkan proses ekstraksi fitur atau pola dari citra ternormalisasi menggunakan filter Log-Gabor. Pencocokan untuk mengukur kesamaan antara pola iris mata manusia dengan pola-pola dalam basisdata sistem dilakukan menggunakan Hamming distance. Dalam percobaan pengenalan individu menggunakan basisdata iris mata MMU diperoleh akurasi sebesar 98%. Kata Kunci: Pengenalan selaput pelangi, Pengenalan iris mata, Filter log-Gabor, Segmentasi citra, Sistem biometrik

scholarly journals Random Forest with Adaptive Local Template for Pedestrian Detection

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Tao Xiang ◽  
Tao Li ◽  
Mao Ye ◽  
Zijian Liu
Keyword(s):  
Computer Vision ◽  
Random Forest ◽  
Classification Accuracy ◽  
Template Matching ◽  
Detection Method ◽  
State Of The Art ◽  
Pedestrian Detection ◽  
Sliding Window ◽  
Experimental Results ◽  
Training Samples

Pedestrian detection with large intraclass variations is still a challenging task in computer vision. In this paper, we propose a novel pedestrian detection method based on Random Forest. Firstly, we generate a few local templates with different sizes and different locations in positive exemplars. Then, the Random Forest is built whose splitting functions are optimized by maximizing class purity of matching the local templates to the training samples, respectively. To improve the classification accuracy, we adopt a boosting-like algorithm to update the weights of the training samples in a layer-wise fashion. During detection, the trained Random Forest will vote the category when a sliding window is input. Our contributions are the splitting functions based on local template matching with adaptive size and location and iteratively weight updating method. We evaluate the proposed method on 2 well-known challenging datasets: TUD pedestrians and INRIA pedestrians. The experimental results demonstrate that our method achieves state-of-the-art or competitive performance.

Real-Time Identification and Tracking of Infrared Markers Based on Kalman Filter

2012 ◽  
Vol 249-250 ◽  
pp. 1147-1153
Author(s):  
Qiao Na Xing ◽  
Da Yuan Yan ◽  
Xiao Ming Hu ◽  
Jun Qin Lin ◽  
Bo Yang
Keyword(s):  
Kalman Filter ◽  
Visible Light ◽  
Real Time ◽  
Complex Terrain ◽  
Experimental Results ◽  
Identification Method ◽  
In The Wild ◽  
Real Time Identification

Automatic equipmenttransportation in the wild complex terrain circumstances is very important in rescue or military. In this paper, an accompanying system based on the identification and tracking of infrared LEDmarkers is proposed. This system avoidsthe defect that visible-light identification method has. In addition, this paper presents a Kalman filter to predict where infraredmarkers may appear in the nextframe imageto reduce the searchingarea of infrared markers, which remarkablyimproves the identificationspeed of infrared markers. The experimental results show that the algorithm proposed in this paper is effective and feasible.

Analysis of Identification Methods of Key Nodes in Transportation Network

2022 ◽  
Author(s):  
Qiang Lai ◽  
Hong-hao Zhang
Keyword(s):  
Transportation Networks ◽  
Transportation Network ◽  
Practical Significance ◽  
Node Degree ◽  
Connected Subgraph ◽  
Network Efficiency ◽  
Identification Methods ◽  
Identification Method ◽  
Bus Networks ◽  
Key Nodes

Abstract The identification of key nodes plays an important role in improving the robustness of the transportation network. For different types of transportation networks, the effect of the same identification method may be different. It is of practical significance to study the key nodes identification methods corresponding to various types of transportation networks. Based on the knowledge of complex networks, the metro networks and the bus networks are selected as the objects, and the key nodes are identified by the node degree identification method, the neighbor node degree identification method, the weighted k-shell degree neighborhood identification method (KSD), the degree k-shell identification method (DKS), and the degree k-shell neighborhood identification method (DKSN). Take the network efficiency and the largest connected subgraph as the effective indicators. The results show that the KSD identification method that comprehensively considers the elements has the best recognition effect and has certain practical significance.

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