Face And Ear Composite Recognition Tool
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This document discusses a project to develop a tool for face and ear composite recognition using biometrics. It aims to use both face and ear recognition techniques together for enhanced security and flexibility. The document introduces biometrics and discusses face and ear biometrics in particular. It outlines the goals, implementation, work completed so far including feature extraction and database building, and future work such as automatic extraction, algorithm comparisons, and integration into a single tool.
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Novel Image Fusion Techniques using DCT .........................................................................................1
V. P. S. Naidu
High Performance Data mining by Genetic Neural Network ................................................................1
Dadmehr Rahbari
Parallel Ensemble Techniques for Data Mining Application .................................................................1
M. Govindarajan
A Hybrid Cryptosystem for Image using Chaotic Mapping ...................................................................1
Nidhi Sethi and Sandip Vijay
Investigating Factors Affecting Adoption and Implementation of m-Government in the South African
Department of Home Affairs: An on-going Research ..........................................................................1
Maleshoane Sepeame and Emmanuel Babatunde Ajala
Prospects of Thermal Management Techniques in Microprocessor Architecture .................................1
Ajaegbu Chigozirim, Shodiya A.S and Kuyoro Shade O.
Capacity Based Clustering Model for Dense Wireless Sensor Networks ...............................................1
S. R. Boselin Prabhu and S. Sophia
Biometric Ear Recognition System
This document discusses biometric ear recognition systems for authentication. It begins with an abstract describing a system that uses ear images captured during sign up to authenticate users during login by matching the live ear image to the stored one. It then reviews the literature on various ear recognition techniques, including 3D ear recognition using iterative closest point and stochastic clustering matching algorithms, and normalization methods to make ear features invariant to transformations. The document evaluates different feature extraction methods like PCA, SURF and geometric features to recognize individuals based on ear patterns. Overall, the document examines the viability of using ear biometrics for authentication and reviews different approaches in the literature.
Pattern recognition multi biometrics using face and ear
Pattern recognition multi biometrics using face and ear seminar
Mustansiriya University
Department of Education
Computer Science
Biometric Technology
First of all, Thank you for giving this opportunity of presenting my knowledge with you all, hope this is helpful to learn something regarding biometric technology
Performance of Gabor Mean Feature Extraction Techniques for Ear Biometrics Re...
Abstract
Ear biometric recognition is used in a lot of applications as person identification in
criminal cases, investigation, and security purpose. Feature optimization stage
has an important role for accuracy of correct recognition. Gabor filter have a
problem of high dimension and high redundancy. Sampling filter is a problem of
not reducing features optimum way. In the proposed Gabor feature extraction
technique the Gabor features are filtered using proposed mean filter and obtained
optimum features for ear biometric dataset.
Independent Research
This document provides a summary of face detection and recognition techniques. It discusses common methods like feature-based, holistic, and hybrid approaches. For face detection, it examines the Viola-Jones method using Haar features and Shi and Thomasi algorithm for extracting feature points. It also surveys different papers on face recognition and describes methods like color-based, motion-based, blink detection, and feature detection techniques. The document provides details on active shape models, low-level analysis using skin color, gray scale, and edges. It also discusses feature analysis methods like Viola-Jones and Gabor filters as well as the constellation method.
EarAuthCam: Personal Identification and Authentication Method Using Ear Image...
Earphones are now used for longer hours than before with the advancement in wireless technology and miniaturization. In addition, the application of earphones has become more diverse, and opportunities to access highly confidential information through them have increased. We propose a method comprising a hearable device equipped with a small camera for user authentication from ear images. This method improves the security of the hearable device. Ear images are first captured with the camera. The ear regions in the images are then extracted using a mask region-based convolutional neural network. Finally, the user is identified using histograms of oriented gradient features and a support vector machine (SVM). Our method was able to identify 18 participants with an accuracy of 84.1%. Users are authenticated through unsupervised anomaly detection using an autoencoder with an error rate of 8.36%. This method facilitates hands- and eye-free operations without requiring any explicit authentication action by the user.
Bio shiv
The document is a seminar presentation on biometrics. It defines biometrics as the science of measuring and analyzing biological data, particularly for authentication purposes such as fingerprints, eye retinas, and facial patterns. It discusses how biometrics can be used for identification and verification. It also covers different categories of biometrics like fingerprints, facial recognition, iris recognition, and more. It discusses the advantages like uniqueness and disadvantages like cost. In conclusion, biometrics is widely used due to its convenience, flexibility, and stability over time.
Ijetcas14 598
This document compares various biometric methods for identification and verification. It discusses fingerprint recognition, face recognition, voice recognition, and iris recognition as some of the main biometric techniques. For each method, it describes how the biometric data is captured and analyzed, the advantages and disadvantages, and examples of applications where the technique can be used. The document provides an overview of the history of biometrics and the typical modules involved in a biometric system, such as sensors, feature extraction, matching, and template databases.
Biometrics
The document discusses biometrics, which is the study of methods for uniquely recognizing humans based on physical and behavioral traits. Some examples of physiological biometrics are fingerprint, face recognition, DNA, hand and palm geometry, and iris recognition. Behavioral biometrics include typing rhythm, gait, and voice. The document then explains the process of biometric systems which involves capturing biometric data, creating a template, storing it in a database, and comparing new captures against stored templates to authenticate users. It discusses some challenges with biometric technologies including privacy issues, discrimination concerns, and the permanence of biometrics.
Automatic Attendance system using Facial Recognition
It is a boimetric based App,which is gradually evolving in the universal boimetric solution with a virtually zero effort from the user end when compared with other boimetric options.
Audiometry A Model-View-Viewmodel (MVVM) Application Framework For Hearing I...
Audiometry is an open-source application framework for diagnosing hearing impairment. It was created using the Model-View-ViewModel (MVVM) pattern to separate the user interface from backend logic. Audiometry allows users to accurately store, search, analyze, and visualize data from various hearing tests. It supports tuning fork tests, speech audiometry, pure-tone audiometry, and other advanced tests. The framework also enables extension of functionality and independent use with different hearing test hardware. An otolaryngologist found Audiometry helpful for more methodical, swift and accurate hearing impairment diagnosis.
Preserving Global and Local Features for Robust FaceRecognition under Various...
The increasing use of biometric technologies in high-security applications and beyond has stressed the requirement for highly dependable face recognition systems. Much research on face recognition considering the large variations in the visual stimulus due to illumination conditions, viewing directions or poses, facial expressions, aging, and disguises such as facial hair, glasses, or cosmetics has been done earlier. However, in reality the noises that may embed into an image document during scanning, printing or image capturing process will affect the performance of face recognition algorithms. Though different filtering algorithms are available for noise reduction, applying a filtering algorithm that is sensitive to one type of noise to an image which has been degraded by another type of noise lead to unfavourable results. In turn, these conditions stress the importance of the design of robust face recognition algorithms that retain recognition rates even under noisy conditions. In reality, many face recognition algorithms exist and produce good results for noiseless environments but not with various noises. In this work, numerous experiments have been conducted to analyze the robustness of our proposed Combined Global and Local Preserving Features (CGLPF) algorithm along with other existing conventional algorithms under different types of noises such as Gaussian noise, speckle noise, salt and pepper noise and quantization noise.
Biometrics
This document discusses biometrics, which uses human body characteristics for authentication purposes. It describes biometric devices that scan and digitize characteristics like fingerprints, irises, and facial patterns. Biometrics can be physiological (fingerprints, iris scans) or behavioral (signatures, voice). To work, characteristics must be universal, unique, and permanent for each individual. Biometric systems enroll users by storing their data, and then verify identities by matching live scans to enrolled data. Examples of biometric technologies discussed include fingerprint recognition, face recognition using facial features, voice recognition, iris recognition using iris patterns, and signature verification.
IRJET-Human Face Detection and Identification using Deep Metric Learning
This document discusses a project that uses deep metric learning techniques for human face detection and identification in images and videos. Deep metric learning outputs a real-valued vector rather than a single classification. It uses libraries like OpenCV, Dlib, scikit-learn and Keras to build neural networks for facial recognition. The goals are to develop a system that can identify faces even from low quality images with variations in illumination, expression, angle and occlusions. Existing face recognition has challenges in these conditions, so the aim is to improve accuracy rates for normal and non-ideal images through deep metric learning approaches.
Digital orthodontics
Digital Orthodontics: Technological invention, clear aligners therapy, robotics, tele-orthodontics, laser orthodontics, artificial intelligence, Digital impression , intra oral scanners, CAD-CAM
Face Recognition Research Report
This report is based on research. This whole research content are taken by books and websites. you can learn about face recognition history, how's it is work traditional and in technical way, introduction of some face recognition software and devices. we also add face recognition algorithm in report.
Biometrics
This document discusses biometrics, which uses human body characteristics to authenticate identity. It describes how biometric devices work by scanning a trait, converting it to digital form, and comparing it to stored data. There are two main classes of biometrics: physiological (face, fingerprints, iris) and behavioral (signature, voice). Biometrics has advantages like accuracy but also disadvantages like cost and the possibility that some traits may change over time.
IRJET- Persons Identification Tool for Visually Impaired - Digital Eye
This document presents a face detection and recognition system to help visually impaired people identify individuals. The system uses computer vision techniques like convolutional neural networks and cascade classifiers for face detection with high accuracy. It then performs face recognition on pre-trained image datasets to determine a person's identity, as well as their emotion, age and gender. The system was tested on a combined dataset of images and achieved 95.7% accuracy in identifying faces, even when there were many faces present. This person identification tool aims to help the visually impaired better interact with others by audibly providing the name and attributes of detected individuals.
Biometrics Starts
Introduction
The term “biometrics” is derived from the Greek words “bio” (life) and “metrics” (to measure). Automated biometric systems have only become available over the last few decades, due to significant advances in the field of computer processing. Many of these new automated techniques, however, are based on ideas that were originally conceived hundreds, even thousands of years ago.
One of the oldest and most basic examples of a characteristic that is used for recognition by humans is the face. Since the beginning of civilization, humans have used faces to identify known (familiar) and unknown (unfamiliar) individuals. This simple task became increasingly more challenging as populations increased and as more convenient methods of travel introduced many new individuals into- once small communities. The concept of human-to-human recognition is also seen in behavioral-predominant biometrics such as speaker and gait recognition. Individuals use these characteristics, somewhat unconsciously, to recognize known individuals on a day-to-day basis.
Similar to Face And Ear Composite Recognition Tool (20)
Pattern recognition multi biometrics using face and ear
Pattern recognition multi biometrics using face and ear
Performance of Gabor Mean Feature Extraction Techniques for Ear Biometrics Re...
Performance of Gabor Mean Feature Extraction Techniques for Ear Biometrics Re...
EarAuthCam: Personal Identification and Authentication Method Using Ear Image...
EarAuthCam: Personal Identification and Authentication Method Using Ear Image...
Automatic Attendance system using Facial Recognition
Automatic Attendance system using Facial Recognition
Audiometry A Model-View-Viewmodel (MVVM) Application Framework For Hearing I...
Audiometry A Model-View-Viewmodel (MVVM) Application Framework For Hearing I...
Preserving Global and Local Features for Robust FaceRecognition under Various...
Preserving Global and Local Features for Robust FaceRecognition under Various...
IRJET-Human Face Detection and Identification using Deep Metric Learning
IRJET-Human Face Detection and Identification using Deep Metric Learning
IRJET- Persons Identification Tool for Visually Impaired - Digital Eye
IRJET- Persons Identification Tool for Visually Impaired - Digital Eye
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Mind map of terminologies used in context of Generative AI
Mind map of common terms used in context of Generative AI.
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In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
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Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
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Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
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TrustArc Webinar - 2024 Global Privacy Survey
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
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20 Comprehensive Checklist of Designing and Developing a Website
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Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
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Gopinath Rebala
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GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
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Artificial Intelligence for XMLDevelopment
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
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The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdf
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A tale of scale & speed: How the US Navy is enabling software delivery from l...
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20240609 QFM020 Irresponsible AI Reading List May 2024
Everything I found interesting about the irresponsible use of machine intelligence in May 2024
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Mind map of terminologies used in context of Generative AI
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Face And Ear Composite Recognition Tool
- 1. Face and Ear Composite Recognition Tool Abhineet Bhamra (0131322804) Ashish Goel (0181322804) Jaideep Singh (0161322804) Karan Gogna (0361322804)
- 12. Thank You