Techniques for Face Detection & Recognition Systema Comprehensive ReviewIOSR Journals
Abstract: Face detection and Facial recognition technology has emerged as a striking solution to address
many contemporary prerequisites for identification and the verification of identity prerogatives. It brings
together the potential of supplementary biometric systems, which attempt to link identity to individually
distinctive features of the body, and the more acquainted functionality of visual surveillance systems. In current
decades face recognition has experienced significant consideration from both research communities and the
marketplace, conversely still remained very electrifying in real applications. The assignment of face detection
and recognition has been dynamically researched in current eternities. This paper offers a conversant
evaluation of foremost human face recognition research. We first present a summary of face detection, face
recognition and its solicitations. Then, a literature review of the predominantly used face recognition techniques
is accessible.
Clarification and restrictions of the performance of these face recognition algorithms are specified.
Here we present a vital assessment of the current researches concomitant with the face recognition process. In
this paper, we present a broad range review of major researches on face recognition process based on various
circumstances. In addition, we present a summarizing description of Face detection and recognition process
and development along with the techniques connected with the various influences that affects the face
recognition process.
Keywords: Face Detection, Face Recognition System, Biometric System, Review Research.
Multi Local Feature Selection Using Genetic Algorithm For Face IdentificationCSCJournals
Face recognition is a biometric authentication method that has become more significant and relevant in recent years. It is becoming a more mature technology that has been employed in many large scale systems such as Visa Information System, surveillance access control and multimedia search engine. Generally, there are three categories of approaches for recognition, namely global facial feature, local facial feature and hybrid feature. Although the global facial-based feature approach is the most researched area, this approach is still plagued with many difficulties and drawbacks due to factors such as face orientation, illumination, and the presence of foreign objects. This paper presents an improved offline face recognition algorithm based on a multi-local feature selection approach for grayscale images. The approach taken in this work consists of five stages, namely face detection, facial feature (eyes, nose and mouth) extraction, moment generation, facial feature classification and face identification. Subsequently, these stages were applied to 3065 images from three distinct facial databases, namely ORL, Yale and AR. The experimental results obtained have shown that recognition rates of more than 89% have been achieved as compared to other global-based features and local facial-based feature approaches. The results also revealed that the technique is robust and invariant to translation, orientation, and scaling.
MULTIMODAL BIOMETRICS RECOGNITION FROM FACIAL VIDEO VIA DEEP LEARNINGcsandit
Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics.
In this paper, we present a novel multimodal recognition system that trains a Deep Learning Network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denosing autoencoders to automatically extract robust and non-redundant features.The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. Experiments conducted on the constrained facial video
dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% rank-1 recognition rates, respectively. The multimodal recognition
accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, non-planar movement, and pose variations present in the video clips.
Techniques for Face Detection & Recognition Systema Comprehensive ReviewIOSR Journals
Abstract: Face detection and Facial recognition technology has emerged as a striking solution to address
many contemporary prerequisites for identification and the verification of identity prerogatives. It brings
together the potential of supplementary biometric systems, which attempt to link identity to individually
distinctive features of the body, and the more acquainted functionality of visual surveillance systems. In current
decades face recognition has experienced significant consideration from both research communities and the
marketplace, conversely still remained very electrifying in real applications. The assignment of face detection
and recognition has been dynamically researched in current eternities. This paper offers a conversant
evaluation of foremost human face recognition research. We first present a summary of face detection, face
recognition and its solicitations. Then, a literature review of the predominantly used face recognition techniques
is accessible.
Clarification and restrictions of the performance of these face recognition algorithms are specified.
Here we present a vital assessment of the current researches concomitant with the face recognition process. In
this paper, we present a broad range review of major researches on face recognition process based on various
circumstances. In addition, we present a summarizing description of Face detection and recognition process
and development along with the techniques connected with the various influences that affects the face
recognition process.
Keywords: Face Detection, Face Recognition System, Biometric System, Review Research.
Multi Local Feature Selection Using Genetic Algorithm For Face IdentificationCSCJournals
Face recognition is a biometric authentication method that has become more significant and relevant in recent years. It is becoming a more mature technology that has been employed in many large scale systems such as Visa Information System, surveillance access control and multimedia search engine. Generally, there are three categories of approaches for recognition, namely global facial feature, local facial feature and hybrid feature. Although the global facial-based feature approach is the most researched area, this approach is still plagued with many difficulties and drawbacks due to factors such as face orientation, illumination, and the presence of foreign objects. This paper presents an improved offline face recognition algorithm based on a multi-local feature selection approach for grayscale images. The approach taken in this work consists of five stages, namely face detection, facial feature (eyes, nose and mouth) extraction, moment generation, facial feature classification and face identification. Subsequently, these stages were applied to 3065 images from three distinct facial databases, namely ORL, Yale and AR. The experimental results obtained have shown that recognition rates of more than 89% have been achieved as compared to other global-based features and local facial-based feature approaches. The results also revealed that the technique is robust and invariant to translation, orientation, and scaling.
MULTIMODAL BIOMETRICS RECOGNITION FROM FACIAL VIDEO VIA DEEP LEARNINGcsandit
Biometrics identification using multiple modalities has attracted the attention of many researchers as it produces more robust and trustworthy results than single modality biometrics.
In this paper, we present a novel multimodal recognition system that trains a Deep Learning Network to automatically learn features after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in the facial video clips, we train supervised denosing autoencoders to automatically extract robust and non-redundant features.The automatically learned features are then used to train modality specific sparse classifiers to perform the multimodal recognition. Experiments conducted on the constrained facial video
dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and 97.14% rank-1 recognition rates, respectively. The multimodal recognition
accuracy demonstrates the superiority and robustness of the proposed approach irrespective of the illumination, non-planar movement, and pose variations present in the video clips.
An Efficient Face Recognition Using Multi-Kernel Based Scale Invariant Featur...CSCJournals
Face recognition has gained significant attention in research community due to its wide range of commercial and law enforcement applications. Due to the developments in the past few decades, in the current scenario, face recognition is employing advanced feature identification techniques and matching methods. In spite of vast research done, face recognition still remains an open problem due to the challenges posed by illumination, occlusions, pose variation, scaling, etc. This paper is aimed at proposing a face recognition technique with high accuracy. It focuses on face recognition based on improved SIFT algorithm. In the proposed approach, the face features are extracted using a novel multi-kernel function (MKF) based SIFT technique. The classification is done using SVM classifier. Experimental results shows the superiority of the proposed algorithm over the SIFT technique. Evaluation of the proposed approach is done on CVL face database and experimental results shows that the proposed approach has a recognition rate of 99%.
MULTIMODAL BIOMETRICS RECOGNITION FROM FACIAL VIDEO VIA DEEP LEARNINGsipij
Biometrics identification using multiple modalities has attracted the attention of many researchers as it
produces more robust and trustworthy results than single modality biometrics. In this paper, we present a
novel multimodal recognition system that trains a Deep Learning Network to automatically learn features
after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing
different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in
the facial video clips, we train supervised denoising auto-encoders to automatically extract robust and nonredundant
features. The automatically learned features are then used to train modality specific sparse
classifiers to perform the multimodal recognition. Experiments conducted on the constrained facial video
dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and
97.14% rank-1 recognition rates, respectively. The multimodal recognition accuracy demonstrates the
superiority and robustness of the proposed approach irrespective of the illumination, non-planar
movement, and pose variations present in the video clips.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Artículo presentado por la Universidad de Vigo durante la jornada HOIP'10 organizada por la Unidad de Sistemas de información e interacción de TECNALIA.
Más información en http://www.tecnalia.com/es/ict-european-software-institute/index.htm
Driving support systems, such as car navigation systems are becoming common and they
support driver in several aspects. Non-intrusive method of detecting Fatigue and drowsiness
based on eye-blink count and eye directed instruction controlhelps the driver to prevent from
collision caused by drowsy driving. Eye detection and tracking under various conditions such as
illumination, background, face alignment and facial expression makes the problem
complex.Neural Network based algorithm is proposed in this paper to detect the eyes efficiently.
In the proposed algorithm, first the neural Network is trained to reject the non-eye regionbased
on images with features of eyes and the images with features of non-eye using Gabor filter and Support Vector Machines to reduce the dimension and classify efficiently. In the algorithm, first the face is segmented using L*a*btransform color space, then eyes are detected using HSV and Neural Network approach. The algorithm is tested on nearly 100 images of different persons under different conditions and the results are satisfactory with success rate of 98%.The Neural Network is trained with 50 non-eye images and 50 eye images with different angles using Gabor filter. This paper is a part of research work on “Development of Non-Intrusive system for realtime Monitoring and Prediction of Driver Fatigue and drowsiness” project sponsored by Department of Science & Technology, Govt. of India, New Delhi at Vignan Institute of Technology and Sciences, Vignan Hills, Hyderabad.
A comparative review of various approaches for feature extraction in Face rec...Vishnupriya T H
Four feature extraction algorithms are discussed here.
1. Principal Component Analysis
2. Discreet LInear Transform
3. Independent Component Analysis
4. Linear Discriminant Aalysis
Abstract: This paper presents a new face parts information analyzer, as a promising model for detecting faces and locating the facial features in images. The main objective is to build fully automated human facial measurements systems from images with complex backgrounds. Detection of facial features such as eye, nose, and mouth is an important step for many subsequent facial image analysis tasks. The main study of face detection is detect the portion of part and mention the circle or rectangular of the every portion of body. In this paper face detection is depend upon the face pattern which is match the face from the pattern reorganization. The study present a novel and simple model approach based on a mixture of techniques and algorithms in a shared pool based on viola jones object detection framework algorithm combined with geometric and symmetric information of the face parts from the image in a smart algorithm.Keywords: Face detection, Video frames, Viola-Jones, Skin detection, Skin color classification, Face reorganization, Pattern reorganization. Skin Color.
Title: Face Detection Using Modified Viola Jones Algorithm
Author: Alpika Gupta, Dr. Rajdev Tiwari
International Journal of Recent Research in Mathematics Computer Science and Information Technology
ISSN 2350-1022
Paper Publications
The paper explores iris recognition for personal identification and verification. In this paper a new iris recognition technique is proposed using (Scale Invariant Feature Transform) SIFT. Image-processing algorithms have been validated on noised real iris image database. The proposed innovative technique is computationally effective as well as reliable in terms of recognition rates.
An Efficient Face Recognition Using Multi-Kernel Based Scale Invariant Featur...CSCJournals
Face recognition has gained significant attention in research community due to its wide range of commercial and law enforcement applications. Due to the developments in the past few decades, in the current scenario, face recognition is employing advanced feature identification techniques and matching methods. In spite of vast research done, face recognition still remains an open problem due to the challenges posed by illumination, occlusions, pose variation, scaling, etc. This paper is aimed at proposing a face recognition technique with high accuracy. It focuses on face recognition based on improved SIFT algorithm. In the proposed approach, the face features are extracted using a novel multi-kernel function (MKF) based SIFT technique. The classification is done using SVM classifier. Experimental results shows the superiority of the proposed algorithm over the SIFT technique. Evaluation of the proposed approach is done on CVL face database and experimental results shows that the proposed approach has a recognition rate of 99%.
MULTIMODAL BIOMETRICS RECOGNITION FROM FACIAL VIDEO VIA DEEP LEARNINGsipij
Biometrics identification using multiple modalities has attracted the attention of many researchers as it
produces more robust and trustworthy results than single modality biometrics. In this paper, we present a
novel multimodal recognition system that trains a Deep Learning Network to automatically learn features
after extracting multiple biometric modalities from a single data source, i.e., facial video clips. Utilizing
different modalities, i.e., left ear, left profile face, frontal face, right profile face, and right ear, present in
the facial video clips, we train supervised denoising auto-encoders to automatically extract robust and nonredundant
features. The automatically learned features are then used to train modality specific sparse
classifiers to perform the multimodal recognition. Experiments conducted on the constrained facial video
dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a 99.17% and
97.14% rank-1 recognition rates, respectively. The multimodal recognition accuracy demonstrates the
superiority and robustness of the proposed approach irrespective of the illumination, non-planar
movement, and pose variations present in the video clips.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Artículo presentado por la Universidad de Vigo durante la jornada HOIP'10 organizada por la Unidad de Sistemas de información e interacción de TECNALIA.
Más información en http://www.tecnalia.com/es/ict-european-software-institute/index.htm
Driving support systems, such as car navigation systems are becoming common and they
support driver in several aspects. Non-intrusive method of detecting Fatigue and drowsiness
based on eye-blink count and eye directed instruction controlhelps the driver to prevent from
collision caused by drowsy driving. Eye detection and tracking under various conditions such as
illumination, background, face alignment and facial expression makes the problem
complex.Neural Network based algorithm is proposed in this paper to detect the eyes efficiently.
In the proposed algorithm, first the neural Network is trained to reject the non-eye regionbased
on images with features of eyes and the images with features of non-eye using Gabor filter and Support Vector Machines to reduce the dimension and classify efficiently. In the algorithm, first the face is segmented using L*a*btransform color space, then eyes are detected using HSV and Neural Network approach. The algorithm is tested on nearly 100 images of different persons under different conditions and the results are satisfactory with success rate of 98%.The Neural Network is trained with 50 non-eye images and 50 eye images with different angles using Gabor filter. This paper is a part of research work on “Development of Non-Intrusive system for realtime Monitoring and Prediction of Driver Fatigue and drowsiness” project sponsored by Department of Science & Technology, Govt. of India, New Delhi at Vignan Institute of Technology and Sciences, Vignan Hills, Hyderabad.
A comparative review of various approaches for feature extraction in Face rec...Vishnupriya T H
Four feature extraction algorithms are discussed here.
1. Principal Component Analysis
2. Discreet LInear Transform
3. Independent Component Analysis
4. Linear Discriminant Aalysis
Abstract: This paper presents a new face parts information analyzer, as a promising model for detecting faces and locating the facial features in images. The main objective is to build fully automated human facial measurements systems from images with complex backgrounds. Detection of facial features such as eye, nose, and mouth is an important step for many subsequent facial image analysis tasks. The main study of face detection is detect the portion of part and mention the circle or rectangular of the every portion of body. In this paper face detection is depend upon the face pattern which is match the face from the pattern reorganization. The study present a novel and simple model approach based on a mixture of techniques and algorithms in a shared pool based on viola jones object detection framework algorithm combined with geometric and symmetric information of the face parts from the image in a smart algorithm.Keywords: Face detection, Video frames, Viola-Jones, Skin detection, Skin color classification, Face reorganization, Pattern reorganization. Skin Color.
Title: Face Detection Using Modified Viola Jones Algorithm
Author: Alpika Gupta, Dr. Rajdev Tiwari
International Journal of Recent Research in Mathematics Computer Science and Information Technology
ISSN 2350-1022
Paper Publications
The paper explores iris recognition for personal identification and verification. In this paper a new iris recognition technique is proposed using (Scale Invariant Feature Transform) SIFT. Image-processing algorithms have been validated on noised real iris image database. The proposed innovative technique is computationally effective as well as reliable in terms of recognition rates.
IOSR Journal of Applied Chemistry (IOSR-JAC) is an open access international journal that provides rapid publication (within a month) of articles in all areas of applied chemistry and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in Chemical Science. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A study of techniques for facial detection and expression classificationIJCSES Journal
Automatic recognition of facial expressions is an important component for human-machine interfaces. It
has lot of attraction in research area since 1990's.Although humans recognize face without effort or
delay, recognition by a machine is still a challenge. Some of its challenges are highly dynamic in their
orientation, lightening, scale, facial expression and occlusion. Applications are in the fields like user
authentication, person identification, video surveillance, information security, data privacy etc. The
various approaches for facial recognition are categorized into two namely holistic based facial
recognition and feature based facial recognition. Holistic based treat the image data as one entity without
isolating different region in the face where as feature based methods identify certain points on the face
such as eyes, nose and mouth etc. In this paper, facial expression recognition is analyzed with various
methods of facial detection,facial feature extraction and classification.
Face detection is one of the most suitable applications for image processing and biometric programs. Artificial neural networks have been used in the many field like image processing, pattern recognition, sales forecasting, customer research and data validation. Face detection and recognition have become one of the most popular biometric techniques over the past few years. There is a lack of research literature that provides an overview of studies and research-related research of Artificial neural networks face detection. Therefore, this study includes a review of facial recognition studies as well systems based on various Artificial neural networks methods and algorithms.
CDS is the criminal face identification by capsule neural network.
Solving the common problems in image recognition such as illumination problem, scale variability, and to fight against a most common problem like pose problem, we are introducing Face Reconstruction System.
Innovative Analytic and Holistic Combined Face Recognition and Verification M...ijbuiiir1
Automatic recognition and verification of human faces is a significant problem in the development and application of Human Computer Interaction (HCI).In addition, the demand for reliable personal identification in computerized access control has resulted in an increased interest in biometrics to replace password and identification (ID) card. Over the last couple of years, face recognition researchers have been developing new techniques fuelled by the advances in computer vision techniques, Design of computers, sensors and in fast emerging face recognition systems. In this paper, a Face Recognition and Verification System has been designed which is robust to variations of illumination, pose and facial expression but very sensitive to variations of the features of the face. This design reckons in the holistic or global as well as the analyticor geometric features of the face of the human beings. The global structure of the human face is analysed by Principal Component Analysis while the features of the local structure are computed considering the geometric features of the face such as the eyes, nose and the mouth. The extracted local features of the face are trained and later tested using Artificial Neural Network (ANN). This combined approach of the global and the local structure of the face image is proved very effective in the system we have designed as it has a correct recognition rate of over 90%.
Multimodal Biometrics Recognition from Facial Video via Deep Learning cscpconf
Biometrics identification using multiple modalities has attracted the attention of many
researchers as it produces more robust and trustworthy results than single modality biometrics.
In this paper, we present a novel multimodal recognition system that trains a Deep Learning
Network to automatically learn features after extracting multiple biometric modalities from a
single data source, i.e., facial video clips. Utilizing different modalities, i.e., left ear, left profile
face, frontal face, right profile face, and right ear, present in the facial video clips, we train
supervised denosing autoencoders to automatically extract robust and non-redundant features.
The automatically learned features are then used to train modality specific sparse classifiers to
perform the multimodal recognition. Experiments conducted on the constrained facial video
dataset (WVU) and the unconstrained facial video dataset (HONDA/UCSD), resulted in a
99.17% and 97.14% rank-1 recognition rates, respectively. The multimodal recognition
accuracy demonstrates the superiority and robustness of the proposed approach irrespective of
the illumination, non-planar movement, and pose variations present in the video clips.
Human Face Detection and Tracking for Age Rank, Weight and Gender Estimation ...IJERA Editor
This paper describes the technique for real time human face detection and tracking for age rank, weight and gender estimation. Face detection is involved with finding whether there are any faces in a given image and if there are any faces present, track the face and returns the face region with features of each face. Here it describes a simple and convenient hardware implementation of face detection method using Raspberry Pi Processor, which itself is a minicomputer of a credit card size. This paper presents a cost-sensitive ordinal hyperplanes ranking algorithm for human age evaluation based on face images. Two main components for building an efficient age estimator are facial feature extraction and estimator learning. Using feature extraction and comparing with our input database in which we have different age group face images with weight is specified according to that we also specify weight category i.e. under weight, normal weight and overweight . In this article we present gender estimation technique, which effectively integrates the head as well as mouth motion information with facial appearance by taking advantage of a unified probabilistic framework. Facial appearance as well as head and mouth motion possess a potentially relevant discriminatory power, and that the integration of different sources of biometric data from video sequences is the key approach to develop more precise and reliable realization systems.
Face Recognition based on STWT and DTCWT using two dimensional Q-shift Filters IJERA Editor
The Biometrics is used to recognize a person effectively compared to traditional methods of identification. In this paper, we propose a Face recognition based on Single Tree Wavelet Transform (STWT) and Dual Tree Complex Wavelet transform (DTCWT). The Face Images are preprocessed to enhance quality of the image and resize. DTCWT and STWT are applied on face images to extract features. The Euclidian distance is used to compare features of database image with test face images to compute performance parameters. The performance of STWT is compared with DTCWT. It is observed that the DTCWT gives better results compared to STWT technique.
Techniques for Face Detection & Recognition Systema Comprehensive ReviewIOSR Journals
Abstract: Face detection and Facial recognition technology has emerged as a striking solution to address
many contemporary prerequisites for identification and the verification of identity prerogatives. It brings
together the potential of supplementary biometric systems, which attempt to link identity to individually
distinctive features of the body, and the more acquainted functionality of visual surveillance systems. In current
decades face recognition has experienced significant consideration from both research communities and the
marketplace, conversely still remained very electrifying in real applications. The assignment of face detection
and recognition has been dynamically researched in current eternities. This paper offers a conversant
evaluation of foremost human face recognition research. We first present a summary of face detection, face
recognition and its solicitations. Then, a literature review of the predominantly used face recognition techniques
is accessible.
Clarification and restrictions of the performance of these face recognition algorithms are specified.
Here we present a vital assessment of the current researches concomitant with the face recognition process. In
this paper, we present a broad range review of major researches on face recognition process based on various
circumstances. In addition, we present a summarizing description of Face detection and recognition process
and development along with the techniques connected with the various influences that affects the face
recognition process.
Keywords: Face Detection, Face Recognition System, Biometric System, Review Research.
Event Management System Vb Net Project Report.pdfKamal Acharya
In present era, the scopes of information technology growing with a very fast .We do not see any are untouched from this industry. The scope of information technology has become wider includes: Business and industry. Household Business, Communication, Education, Entertainment, Science, Medicine, Engineering, Distance Learning, Weather Forecasting. Carrier Searching and so on.
My project named “Event Management System” is software that store and maintained all events coordinated in college. It also helpful to print related reports. My project will help to record the events coordinated by faculties with their Name, Event subject, date & details in an efficient & effective ways.
In my system we have to make a system by which a user can record all events coordinated by a particular faculty. In our proposed system some more featured are added which differs it from the existing system such as security.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
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A Fast Recognition Method for Pose and Illumination Variant Faces on Video Sequences
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 10, Issue 1 (Mar. - Apr. 2013), PP 08-18
www.iosrjournals.org
www.iosrjournals.org 8 | Page
A Fast Recognition Method for Pose and Illumination Variant
Faces on Video Sequences
Mahesh Prasanna K1
, Nagaratna Hegde2
1
(ISE, Alva’s Institute of Engineering & Technology, India)
2
(CSE, Vasavi College of Engineering, India)
Abstract: Video face recognition is a widely used method in which security is essential that recognizes the
human faces from subjected videos. Unlike traditional methods, recent recognition methods consider practical
constraints such as pose and illumination variations on the facial images. Our previous work also considers such
constraints in which face recognition was performed on videos that were highly subjected pose and illumination
variations. The method asserted good performance however; it suffers due to high computational cost. This
work overcomes such drawback by proposing a simple face recognition technique in which a cost efficient
Active Appearance Model (AAM) and lazy classification are deployed. The deployed AAM avoids nonlinear
programming, which is the cornerstone for increased computational cost. Experimental results prove that the
proposed method is better than the conventional technique in terms of recognition measures and computational
cost.
Keywords — Active Appearance Model (AAM), lazy classification, shape, model, appearance, recognition,
computation.
I. INTRODUCTION
Face recognition can be termed as a technique of differentiating faces or confirming one or more
individuals in a particular still or video image using a stored database [1] [2] [3]. It finds numerous applications
in surveillance, human-computer interactions, authentication and security [4]. It is broadly classified into two
types namely geometric feature-based and appearance-based [6]. The facial parts are considered as geometrical
parameters and are utilized by geometric feature-based methods, for instance, elastic bunch graph matching [7]
and active appearance model [8], while in appearance-based methods intensity or intensity-derived parameters
are utilized [1] [17]. In a given video fragment, the process that is performed is associated with the image and is
repeated for every frame. For this reason, we refer some face recognition approaches for a given image also
when discussing about the recognition technique in video. The primary two stages of a face recognition
technique is face detection and face identification [4]. Initially in the face detection stage, face images present in
a given input image are located. Then to recognize the registered individuals of the system, faces located in the
input image are then used by the face identification stage. This shows the sensible significance of having both
face detection algorithms and face identification algorithms [13].
In face recognition, the foremost concerned characteristics are variations in illumination, pose, identity
[5], facial expression, hair style, aging, make-up, scale etc,. The variation is challenging under severe
illumination condition even for humans accurate recognition of faces because the same person appears
extremely different [12]. As a solution, to overcome the problem and to manage pose variations in face
recognition view-based method is principally used. In this method, the images are captured from diverse view
angles to recognize the face images of the persons [15] [16]. Using the images of the same view an eigen space
model is constructed for each view. By using the view-specific eigen model, a person in a different pose can be
recognized effectively [14].
Only few years before the true video based face recognition methods that consistently use both spatial
and temporal information began in which recognition of faces from video sequence still requires more
recognition which is a direct extension of still-image-based recognition [9]. A typical video-based face
recognition system determines the face regions automatically by extracting features from the video and
distinguishing facial identities, which is often a difficult task [10]. In contrast to this, utilizing motion as a cue
identification of the segmentation of a moving person is easier if a video sequence is available. Apart from this,
a range of various factors like illumination, pose, identity, facial expression, hair style, aging, make-up, scale
etc. have been focused in the previous research works. However, illumination and pose are the two major factors
that influence face recognition.
The chief limitation of the pose-invariance recognition approach is the necessity to achieve and
accumulate a large number of views for each face. This technique is unsuitable for conditions where only a
small number of views of the face to be recognized are available. One of the most challenging problems is
varying illumination and the most important drawback of illuminations is computational cost. To address and
2. A Fast Recognition Method for Pose and Illumination Variant Faces on Video Sequences
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solve these problems, various algorithms have been developed for face recognition. A review on handful of
research works has been presented and the problems are addressed in the following Section. The proposed
technique to solve the addressed problem is detailed in Section 3. The results are discussed in Section 4 and
Section 5 concludes the paper.
II. RELATED WORKS
S.Venkatesan and S.SrinivasaRaoMadane [18] have proposed a face recognition system. The system
used Genetic and Ant colony Optimization algorithm to identify faces in images and video tracks faces,
distinguish faces from galleries of well-known people. YileiXuet al. [19] have proposed a framework called
analysis-by-synthesis framework. In the framework the face was recognized from video sequences under
various facial pose and in lighting situation. Connolly, J.F et al. [20] proposed an adaptive classification system
(ACS) for video-based face recognition. The system was a combination of fuzzy ARTMAP neural network
classifier, dynamic particle swarm optimization (DPSO) algorithm, and a long term memory (LTM).
Unsang Park et al. [21] introduced the adaptive use of multiple face matchers to improve the
performance of face recognition in video. In their method, active appearance model (AAM) and a Factorization
based 3D face reconstruction technique were exploited to estimate the active information of facial poses in
different frames. Huifuang Ng et al. [14] proposed an approach, which was not much sensitive to direction of
views and required only one sample view per person. Xiujuan Chai, Shiguang Shan et al. [22] proposed a
Locally Linear regression (LLR) method in which an essential frontal view is generated from a given non frontal
face image. In order to accomplish this, they exploited linear mapping and the assessment of the linear mapping
was formulated as a forecasting problem.
OgnjenArandjelovicet al. [23] presented a recognition method based on simple image processing filters
to produce a single matching score between two different faces. They implicitly estimated the difference of the
illumination conditions between query input and training dataset in the method. Sheikh et. al. [24] proposed a
framework for human face retrieval in which face detection was performed using Viola and Jones frontal
detector and the features were extracted using fast Haar transformation. Sheikh et. al. [25] converts the query
image to a 3D- ellipsoid model based on the viola and jones frontal face detector. They used chamfer distance
measure to performing recognition.
The review of works clearly interprets that the video faces are highly variable, deformable objects, and
different appearances in frame depending on pose, lighting, expression, and the identity of the person. Besides
that, the frame can have different backgrounds, differences in image resolution, contrast, brightness, sharpness,
and color balance. This means that interpretation of such video face requires the ability to understand this
variability in order to extract useful information and this extracted information must be of some manageable
size. However, the recent related works have focused on such practical constraints such as pose and illumination
variations of the facial images. In our previous work, we had developed a solution for considering both pose and
illumination variation, however the methodology is highly complex despite it performed well.
In this work, we intend to propose a simple face recognition technique, which is simpler in terms of
computational complexity, using Active Appearance Model (AAM). Active Appearance Model is well known
for its differentiation and integration ability under different poses of subjected faces. Conventionally, the AAM
is exploited along with a non-linear programming to extract features. The extracted features require a classifier;
mostly an intelligent classifier is required for further recognition process. This again increases the computational
complexity despite the features that distinguish pose variations. In order to avoid such complexities, we propose
active appearance features without incorporation of nonlinear programming and a lazy classifier to training
complexities.
III. Proposed Face Recognition Technique
The proposed technique is comprised of two stages, (i) Development of Feature Library and (ii)
Recognition Stage. A high level block diagram of the proposed face recognition system is given in Figure 1.
3. A Fast Recognition Method for Pose and Illumination Variant Faces on Video Sequences
www.iosrjournals.org 10 | Page
A. Development of Feature Library
Let us consider a facial database NiVV i ,,1 , where N is the number of videos in the
database. In the database, iV is
th
i video, which is of length T seconds and holds pN number of poses. For
every pose, a sample frame ipf : 1,,1,0 pNp is selected and then the combined appearance model is
extracted. In order to extract a combined appearance model for every
th
i video, individual shape and appearance
models are extracted and subsequent combining processing is performed. In both the shape and appearance
model extraction, manual intervention is required as like in the AAM, in which the active portions of faces are
manually marked and then they are subjected to shape and appearance model extraction. The manually marked
active portions of some sample faces are given in Figure 2.
Figure 2: Active marks on sample images
B. Shape Model Extraction
Generally, shape model is a statistical model, which depicts the shape features of a deformable object.
Conventional shape model [26] performs iterative procedures to determine precise statistical relationship among
the facial portions; however the complexity is increased because of the iterative procedure. The propose method
extracts a simple shape model without any iterative procedures, which is given as follows
4. A Fast Recognition Method for Pose and Illumination Variant Faces on Video Sequences
www.iosrjournals.org 11 | Page
1
1
0
aipN
ip
ip
ip
S
S
S
S
(1)
y
ipqiq
y
ipqipq
x
ipqiq
x
ipqipq
y
ipq
x
ipq
ipq
YYE
XXE
S
S
S
(2)
where, ipqS is the shape model of th
q active portion of th
p pose in th
i video, x
ipqS and y
ipqS are the shape
model of x and y co-ordinates, ipqX and ipqY are a vector of x co-ordinates of th
q active portion of th
p pose
in th
i video, respectively, x
iq , y
iq , iqX and iqY are the local mean and global mean shape model of th
q active
portion in th
i video, respectively. The local mean and global mean shape models can be determined as follows
ipq
N
pp
iq X
N
X
p
1
0
1
(3)
)(5.0 iqipqiq XX
(4)
C. Appearance Model Extraction
Active Appearance Model is a statistical model that defines shape and appearance of an object. In our
work, we exploit the benefits of appearance model simply by considering the gray levels of the interconnected
landmark points. An interconnection of land mark points that depict appearance of a face is shown below.
Figure 3: Extraction of Appearance Model (i) Face with Landmark points, (ii) Mesh structure Landmark points
and (iii) Mesh Model of Face Appearance
1
1
0
aipN
ip
ip
ip
A
A
A
A
(5)
From figure 3, it can be seen that an appearance structure of the face is firstly landmarked, secondly a mesh
structure is formed and eventually, the mesh model is extracted. The position of the such mesh nodes are
considered as shape model in the previous Section and the gray portions are considered as the appearance model
along with the interconnect information. The extracted appearance model can be represented as in equation (5)
in which ipipqip SAA
D. Recognition Stage
The Recognition stage performs most of the similar processes as done in the feature library and then performs a
lazy classification to recognize the acquired face belongs to an authorized person or not. The recognition
systems may either work in online or in offline, anyhow the face is acquired by video acquisition system and the
face localization is performed. Once the localized face is grabbed, then the further process of extracting the
5. A Fast Recognition Method for Pose and Illumination Variant Faces on Video Sequences
www.iosrjournals.org 12 | Page
features takes place. As stated earlier, the further extraction of facial shape and appearance model takes as
performed in Section B & C and test feature set is determined as follows.
E. Lazy Classification
This work exploits lazy classification as because the lazy classification requires no training process, which can
highly reduce the system complexity. Despite lazy classification is not assured for recognition accuracy, the
features are extracted in such a way that improved recognition accuracy can be accomplished from the
introduced lazy classifier. The lazy classification process is performed with the aid of Squared Euclidean
Distance (SED). The classification model is given as follows
TN
i
A
i
S
i
A
i
S
i
i
DD
DDN
:minarg
1
0 (6)
where,
1
0
1
0
2
p a
N
p
N
q
test
qipq
S
i SSD
(7)
1
0
1
0
2
p a
N
p
N
q
tset
qipq
A
i AAD
(8)
In equation (6),
is the classification distance, T is the distance threshold, S
iD and A
iD are the shape and
correlation parameters, respectively.
As per the model, which is given in equation (6), the classification is performed and the face
shall be a
recognized face, whereas if decision would be unrecognized. Eventually a recognized face is identified
for its identification number and the approved credentials are displayed through output device.
IV. Experimental Results
The proposed face recognition technique is implemented in the working platform of MATLAB
(version 7.11) and tested in a machine, which has Intel Core i5 Processor, 4GB RAM and a clock speed of 3.20
GHz. The performance of the proposed technique is analyzed using UPC Face Database. For simplicity and
visualize the performance in depth, we divide the database into four subsets and we organize in such a way that
each subset has different pose and illumination views of ten persons. In every database, ten-fold cross-validation
is performed and the performance is observed in terms of statistical measures and computational time.
A. Performance Analysis
The cross-validation results of the proposed and conventional face recognition technique [27] on the
benchmark datasets are given as confusion matrices and statistical measures in Table I and II, respectively.
TABLE I: Confusion Matrices of proposed and conventional methods for (i) Dataset 1, (ii) Dataset 2, (iii)
Dataset 3 and (iv) Dataset 4
(i)
(a)Proposed Method (b) Conventional Method
4 0
1 5
2 0
3 5
6. A Fast Recognition Method for Pose and Illumination Variant Faces on Video Sequences
www.iosrjournals.org 13 | Page
(ii)
(a)Proposed Method (b) Conventional Method
(iii)
(a)Proposed Method (b) Conventional Method
(iv)
(a)Proposed Method (b) Conventional Method
TABLE II: Cross-validation results between proposed and conventional face recognition systems on (i) Dataset
I, (ii) Dataset II, (iii) Dataset III and (iv) Dataset IV
(i)
4 1
1 4
1 1
4 4
3 1
2 4
1 3
4 2
3 1
2 4
1 0
4 5
Performance Metrics Proposed Method Conventional Method
Accuracy (in %) 90 70
Sensitivity (in %) 80 40
Specificity (in %) 100 100
False Positive Rate (FPR) (in %) 0 0
Positive Predictive Value (PPV) (in
%)
100 100
Negative Predictive Value (NPV)
(in %)
83.3 62.5
False Discovery Rate (FDR) (in %) 0 0
Mathew Correlation Coefficient
(MCC)
0.82 0.5
7. A Fast Recognition Method for Pose and Illumination Variant Faces on Video Sequences
www.iosrjournals.org 14 | Page
(ii)
(iii)
(iv)
Performance Metrics Proposed Method Conventional Method
Accuracy (in %) 80 50
Sensitivity (in %) 80 20
Specificity (in %) 80 80
False Positive Rate (FPR) (in %) 20 20
Positive Predictive Value (PPV) (in
%)
80 50
Negative Predictive Value (NPV)
(in %)
80 50
False Discovery Rate (FDR) (in %) 20 50
Mathew Correlation Coefficient
(MCC)
0.48 0
Performance Metrics Proposed Method Conventional Method
Accuracy (in %) 70 30
Sensitivity (in %) 60 20
Specificity (in %) 80 40
False Positive Rate (FPR) (in %) 20 60
Positive Predictive Value (PPV) (in
%)
75 25
Negative Predictive Value (NPV) (in
%)
66.66 33.33
False Discovery Rate (FDR) (in %) 25 75
Mathew Correlation Coefficient
(MCC)
0.3265 -0.163
Performance Metrics Proposed Method Conventional Method
Accuracy (in %) 70 60
Sensitivity (in %) 60 20
Specificity (in %) 80 100
False Positive Rate (FPR) (in %) 20 0
Positive Predictive Value (PPV) (in
%)
75 100
Negative Predictive Value (NPV)
(in %)
66.66 55.55
False Discovery Rate (FDR) (in %) 25 0
Mathew Correlation Coefficient
(MCC)
0.326599 0.3333
8. A Fast Recognition Method for Pose and Illumination Variant Faces on Video Sequences
www.iosrjournals.org 15 | Page
Figure 4: Averaged Comparative Chart between the proposed and conventional face recognition technique
In each confusion matrix (Table 1), the top left and right represents True positive (TP) and False
positive (FP), respectively and bottom left and right represents False Negative (FN) and True Negative (TN)
respectively. Here, the TP, FP, TN and FN values are determined as follows
TP – when an authorized person is identified correctly
FP – when an unauthorized person is identified as authorized
FN – when an authorized person is identified as unauthorized
TN – when an unauthorized person is identified correctly
Based on the confusion matrices, the statistical performance measures such as accuracy, sensitivity, specificity,
FPR, PPV, NPV, FDR and MCC are calculated and tabulated in Table II. In most cases, the performance of the
proposed technique is better than the conventional face recognition technique. Even in some instants, the
conventional face recognition technique is outperforming rather than the proposed technique, for instance, for
dataset 4, PPV of proposed method is only 75% of the conventional method, but when averaged data is
considered, the proposed technique outperforms. This can be visualized from the comparative chart, which is
illustrated in figure 4.
B. Complexity Analysis
The proposed face recognition technique is computationally less complex rather than the conventional face
recognition technique despite pose and illumination factors are considered in the process. The determined
computational time for testing is tabulated in Table III and the averaged comparative chart is given in Figure 5.
Table III: Time taken by the recognition stage of proposed and conventional method under various cross-
validation experiments
(i)
Experiment
No.
Proposed Method (in seconds) Conventional Method (in seconds)
1 0.046403 9.132254
2 0.045505 7.777212
3 0.046203 8.586688
4 0.048359 4.722249
5 0.047265 8.109439
6 0.046123 8.615386
7 0.043215 8.498807
8 0.048245 8.135393
9 0.049569 8.639124
10 0.049575 9.459281
9. A Fast Recognition Method for Pose and Illumination Variant Faces on Video Sequences
www.iosrjournals.org 16 | Page
(ii)
(iii)
(iv)
Figure 5: Averaged complexity comparison on proposed and conventional face recognition method
Experiment
No.
Proposed Method (in seconds) Conventional Method (in seconds)
1 0.003829 8.87961
2 0.003719 8.208001
3 0.003982 9.501837
4 0.003579 7.729446
5 0.003721 8.624749
6 0.003845 7.71216
7 0.003956 7.793763
8 0.003485 9.518724
9 0.003614 9.740584
10 0.003525 8.790306
Experiment
No.
Proposed Method (in seconds) Conventional Method (in seconds)
1 0.003693 7.150357
2 0.003591 7.618764
3 0.003489 7.200222
4 0.003562 7.734955
5 0.003548 7.60312
6 0.003674 7.234343
7 0.003697 7.497155
8 0.003789 7.594407
9 0.003815 7.1227
10 0.003835 7.281026
Experiment
No.
Proposed Method (in seconds) Conventional Method (in seconds)
1 0.003914 33.945641
2 0.003819 24.823843
3 0.003818 25.969141
4 0.003958 36.278052
5 0.003882 33.465799
6 0.004015 44.872417
7 0.003614 33.945641
8 0.003689 24.823843
9 0.003715 38.945641
10 0.003769 28.823843
10. A Fast Recognition Method for Pose and Illumination Variant Faces on Video Sequences
www.iosrjournals.org 17 | Page
From Table III and Figure 5, it can be seen that the proposed face recognition technique is very less
complex than the conventional face recognition technique. The comparison shows that there is tens of deviation
between them. In order further substantiate, averaged complexity is determined in which tens of deviation can
be seen for datasets 1, 2 and 3 and three tens of deviation for dataset 4. Moreover, proposed technique consumes
consistent time duration for evaluating an unknown image, which is an added advantage over the conventional
recognition method.
V. Conclusion
This paper introduced a simple face recognition method to perform precise recognition in least
computational cost. The work intended to solve the drawback of high computational cost of our previous
recognition method. The experimental results showed that the proposed method outperforms the previous work
in terms of both recognition accuracy and computational cost. This is mainly because of the fact that the usage
of appearance model parameters in a simple way without exploiting any nonlinear programming. Moreover, the
lazy classification deployed in the method further reduced the computational complexity. Even though, the work
was mainly focused on reducing the computational complexity on recognition stage, there could not be any
compromise on the precision of recognition. However, as the analysis was made on a limited datasets and not in
a common bench, the future scope of the work relies on careful and extensive analysis over the technique under
various test setups.
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