At the present time, hand gestures recognition system could be used as a more expected and useable
approach for human computer interaction. Automatic hand gesture recognition system provides us a new
tactic for interactive with the virtual environment. In this paper, a face and hand gesture recognition
system which is able to control computer media player is offered. Hand gesture and human face are the key
element to interact with the smart system. We used the face recognition scheme for viewer verification and
the hand gesture recognition in mechanism of computer media player, for instance, volume down/up, next
music and etc. In the proposed technique, first, the hand gesture and face location is extracted from the
main image by combination of skin and cascade detector and then is sent to recognition stage. In
recognition stage, first, the threshold condition is inspected then the extracted face and gesture will be
recognized. In the result stage, the proposed technique is applied on the video dataset and the high
precision ratio acquired. Additional the recommended hand gesture recognition method is applied on static
American Sign Language (ASL) database and the correctness rate achieved nearby 99.40%. also the
planned method could be used in gesture based computer games and virtual reality.
Abstract
Human–computer interaction is a discipline concerned with the design, evaluation and implementation of interactive computing systems for human use. The field formally emerged out of computer science, cognitive psychology and industrial design through the 1960s, formulating guidelines for the development of interactive computer systems highlighting usability concerns for improved interfaces. Computing devices are becoming more prevalent and integrated into both our social and work spaces.HCI therefore plays an important role in ensuring that computer systems are not only functional but also respect the needs and capabilities of the humans that use them.
HCI encompasses not only ease of use but also new interaction techniques. It involves input and output devices and the interaction techniques that use them; presentation of information, control and monitoring of computer’s actions and the processes that developers follow when creating interfaces. In this seminar, emphasis is laid on the movement of a user’s eyes which can provide a convenient, natural, and high-bandwidth source of additional user input. Some of the human factors and technical considerations that arise in trying to use eye movements as an input medium and the first eye movement-based interaction techniques are discussed in this section.
AYUSHA PATNAIK,
SEM - 6th
TRIDENT ACADEMY OF TECHNOLOGY,
BBSR
Gesture recognition using artificial neural network,a technology for identify...NidhinRaj Saikripa
This paper contains a technology for identifying any type of body motions commonly originating from hand and face using artificial neural network.This include identifying sign language also.This technology is for speech impaired individuals.
// I have shared a presentation in this topic
Survey on Human Computer interaction for disabled persons Muhammad Bilal
At present, there are many solutions which facilitate the interaction of computers to handicapped people. In recent years there has been an increased interest in HumanComputer Interaction Systems allowing for more natural communication with machines. Such systems are especially important for elderly and disabled persons. In this paper we have conducted a survey of ten different techniques and tried to analyze how these techniques are working. The techniques have also been compared on the basis of many features which give an insight into the effectiveness of these techniques
You can contact me on Gmai
bilal.professional786@gmail.com
You can also contact me on Facebook
https://www.facebook.com/bilalbakhtawar
Abstract
Human–computer interaction is a discipline concerned with the design, evaluation and implementation of interactive computing systems for human use. The field formally emerged out of computer science, cognitive psychology and industrial design through the 1960s, formulating guidelines for the development of interactive computer systems highlighting usability concerns for improved interfaces. Computing devices are becoming more prevalent and integrated into both our social and work spaces.HCI therefore plays an important role in ensuring that computer systems are not only functional but also respect the needs and capabilities of the humans that use them.
HCI encompasses not only ease of use but also new interaction techniques. It involves input and output devices and the interaction techniques that use them; presentation of information, control and monitoring of computer’s actions and the processes that developers follow when creating interfaces. In this seminar, emphasis is laid on the movement of a user’s eyes which can provide a convenient, natural, and high-bandwidth source of additional user input. Some of the human factors and technical considerations that arise in trying to use eye movements as an input medium and the first eye movement-based interaction techniques are discussed in this section.
AYUSHA PATNAIK,
SEM - 6th
TRIDENT ACADEMY OF TECHNOLOGY,
BBSR
Gesture recognition using artificial neural network,a technology for identify...NidhinRaj Saikripa
This paper contains a technology for identifying any type of body motions commonly originating from hand and face using artificial neural network.This include identifying sign language also.This technology is for speech impaired individuals.
// I have shared a presentation in this topic
Survey on Human Computer interaction for disabled persons Muhammad Bilal
At present, there are many solutions which facilitate the interaction of computers to handicapped people. In recent years there has been an increased interest in HumanComputer Interaction Systems allowing for more natural communication with machines. Such systems are especially important for elderly and disabled persons. In this paper we have conducted a survey of ten different techniques and tried to analyze how these techniques are working. The techniques have also been compared on the basis of many features which give an insight into the effectiveness of these techniques
You can contact me on Gmai
bilal.professional786@gmail.com
You can also contact me on Facebook
https://www.facebook.com/bilalbakhtawar
SMARCOS Abstract Paper submitted to ICCHP 2012Smarcos Eu
This study is part of the European project "Smarcos" (http://www.smarcos-project.eu/) that includes among its goals the development of services which are specifically designed and accessible for blind users.
In this paper we present the prototype application designed to make the main phone features available in a way which is accessible for a blind user. The prototype has been developed to firstly evaluate the interaction modalities based on gestures, audio and vibro-tactile feedback.
Human Computer Interaction is the Study of Interaction between Human & Computer to Design Human-Centered Skills, So that there are Principles & Methods to Create Excellent Interfaces with any Technology.
Introduction to Human Computer Interface (HCI)Edneil Jocusol
This topic is based on the article published by Whitworth and Ahmad in Interaction-Design. It covers topics such as Evolution of Computing Systems, Computing Level (in terms of Mechanical, Informational, Psychological, and Socio-Technical Systems), Human Physiological Needs, and Design Level Combination.
Communication among blind, deaf and dumb PeopleIJAEMSJORNAL
Now-a-days Science and Technology have made the human world so easy but still some physically and visually challenged people suffer from communication with others. In this project, we are going to propose a new system prototype called communication among Blind, deaf and dumb people .This will helps the disabled people to overcome their difficulties in communicating with some other people with disabilities or normal people. The blind people will communicate through the speakers, the deaf and dumb people will see through it and reply through typing in a terminal .These are all done as an application , so that will be easily understand by the people with disabilities.
Ambient Intelligence perspective from IoT insightPrasan Dutt
This presentation was given to National Institute of Technology, Tiruchirapally (NITT) during Version'16 which is an all India MCA meet. The theme of the meet was Ambient Intelligence which was termed as WITURA by organizing team.
(There is not any copyright violation intended in this slide and purely intended for educational purpose. )
Multimodal interaction provides the user with multiple modes of interacting with a system. A multimodal interface provides several distinct tools for input and output of data.
An alter ego (Latin for "other I") means alternative self, which is believed to be distinct from a person's normal or true original personality. Finding one's alter ego will require finding one's other self, one with different personality.
Artificial Intelligence (ai) and Deep Learning ppt (By Shahrukh Shakeel)shahrukh1211
Artificial Intelligence (Ai) and Deep Learning with pictorial illustrations of Ai classifications and Machine Learning. This is a Research Paper Presentation on topic (Deep Learning Previous and Present Applications)
SMARCOS Abstract Paper submitted to ICCHP 2012Smarcos Eu
This study is part of the European project "Smarcos" (http://www.smarcos-project.eu/) that includes among its goals the development of services which are specifically designed and accessible for blind users.
In this paper we present the prototype application designed to make the main phone features available in a way which is accessible for a blind user. The prototype has been developed to firstly evaluate the interaction modalities based on gestures, audio and vibro-tactile feedback.
Human Computer Interaction is the Study of Interaction between Human & Computer to Design Human-Centered Skills, So that there are Principles & Methods to Create Excellent Interfaces with any Technology.
Introduction to Human Computer Interface (HCI)Edneil Jocusol
This topic is based on the article published by Whitworth and Ahmad in Interaction-Design. It covers topics such as Evolution of Computing Systems, Computing Level (in terms of Mechanical, Informational, Psychological, and Socio-Technical Systems), Human Physiological Needs, and Design Level Combination.
Communication among blind, deaf and dumb PeopleIJAEMSJORNAL
Now-a-days Science and Technology have made the human world so easy but still some physically and visually challenged people suffer from communication with others. In this project, we are going to propose a new system prototype called communication among Blind, deaf and dumb people .This will helps the disabled people to overcome their difficulties in communicating with some other people with disabilities or normal people. The blind people will communicate through the speakers, the deaf and dumb people will see through it and reply through typing in a terminal .These are all done as an application , so that will be easily understand by the people with disabilities.
Ambient Intelligence perspective from IoT insightPrasan Dutt
This presentation was given to National Institute of Technology, Tiruchirapally (NITT) during Version'16 which is an all India MCA meet. The theme of the meet was Ambient Intelligence which was termed as WITURA by organizing team.
(There is not any copyright violation intended in this slide and purely intended for educational purpose. )
Multimodal interaction provides the user with multiple modes of interacting with a system. A multimodal interface provides several distinct tools for input and output of data.
An alter ego (Latin for "other I") means alternative self, which is believed to be distinct from a person's normal or true original personality. Finding one's alter ego will require finding one's other self, one with different personality.
Artificial Intelligence (ai) and Deep Learning ppt (By Shahrukh Shakeel)shahrukh1211
Artificial Intelligence (Ai) and Deep Learning with pictorial illustrations of Ai classifications and Machine Learning. This is a Research Paper Presentation on topic (Deep Learning Previous and Present Applications)
http://inarocket.com
Learn BEM fundamentals as fast as possible. What is BEM (Block, element, modifier), BEM syntax, how it works with a real example, etc.
How to Build a Dynamic Social Media PlanPost Planner
Stop guessing and wasting your time on networks and strategies that don’t work!
Join Rebekah Radice and Katie Lance to learn how to optimize your social networks, the best kept secrets for hot content, top time management tools, and much more!
Watch the replay here: bit.ly/socialmedia-plan
Content personalisation is becoming more prevalent. A site, it's content and/or it's products, change dynamically according to the specific needs of the user. SEO needs to ensure we do not fall behind of this trend.
Lightning Talk #9: How UX and Data Storytelling Can Shape Policy by Mika Aldabaux singapore
How can we take UX and Data Storytelling out of the tech context and use them to change the way government behaves?
Showcasing the truth is the highest goal of data storytelling. Because the design of a chart can affect the interpretation of data in a major way, one must wield visual tools with care and deliberation. Using quantitative facts to evoke an emotional response is best achieved with the combination of UX and data storytelling.
Succession “Losers”: What Happens to Executives Passed Over for the CEO Job?
By David F. Larcker, Stephen A. Miles, and Brian Tayan
Stanford Closer Look Series
Overview:
Shareholders pay considerable attention to the choice of executive selected as the new CEO whenever a change in leadership takes place. However, without an inside look at the leading candidates to assume the CEO role, it is difficult for shareholders to tell whether the board has made the correct choice. In this Closer Look, we examine CEO succession events among the largest 100 companies over a ten-year period to determine what happens to the executives who were not selected (i.e., the “succession losers”) and how they perform relative to those who were selected (the “succession winners”).
We ask:
• Are the executives selected for the CEO role really better than those passed over?
• What are the implications for understanding the labor market for executive talent?
• Are differences in performance due to operating conditions or quality of available talent?
• Are boards better at identifying CEO talent than other research generally suggests?
Day by day lots of efforts are been taken towards
developing an intelligent and natural interface between computer
system and users. And looking at the technologies now a day’s it
has become possible by means of variety of media information like
visualization, audio, paint etc. Gesture has become important part
of human communication to convey the information. Thus In this
paper we proposed a method for HAND GESTURE
RECOGNIZATION which includes Hand Segmentation, Hand
Tracking and Edge Traversal Algorithm. We have designed a
system which is limited to the hardware parts such as computer
and webcam. The system consists of four modules: Hand
Tracking and Segmentation, Feature Extraction, Neural
Training, and Testing. The objective of this system to explore the
utility of a neural network-based approach to the recognition of
the hand gestures that create a system that will easily identify the
gesture and use them for device control and convey information
instead of normal inputs devices such as mouse and keyboard.
Hand Gesture Recognition System for Human-Computer Interaction with Web-Camijsrd.com
This paper represents a comparative study of exiting hand gesture recognition systems and gives the new approach for the gesture recognition which is easy cheaper and alternative of input devices like mouse with static and dynamic hand gestures, for interactive computer applications. Despite the increase in the attention of such systems there are still certain limitations in literature. Most applications require different constraints like having distinct lightning conditions, usage of a specific camera, making the user wear a multi-coloured glove or need lots of training data. The use of hand gestures provides an attractive alternative to cumbersome interface devices for human-computer interaction (HCI). This interface is simple enough to be run using an ordinary webcam and requires little training.
Development of Sign Signal Translation System Based on Altera’s FPGA DE2 BoardWaqas Tariq
The main aim of this paper is to build a system that is capable of detecting and recognizing the hand gesture in an image captured by using a camera. The system is built based on Altera’s FPGA DE2 board, which contains a Nios II soft core processor. Image processing techniques and a simple but effective algorithm are implemented to achieve this purpose. Image processing techniques are used to smooth the image in order to ease the subsequent processes in translating the hand sign signal. The algorithm is built for translating the numerical hand sign signal and the result are displayed on the seven segment display. Altera’s Quartus II, SOPC Builder and Nios II EDS software are used to construct the system. By using SOPC Builder, the related components on the DE2 board can be interconnected easily and orderly compared to traditional method that requires lengthy source code and time consuming. Quartus II is used to compile and download the design to the DE2 board. Then, under Nios II EDS, C programming language is used to code the hand sign translation algorithm. Being able to recognize the hand sign signal from images can helps human in controlling a robot and other applications which require only a simple set of instructions provided a CMOS sensor is included in the system.
Social Service Robot using Gesture recognition techniqueChristo Ananth
A robot is a machine that can automatically do a task or a series of tasks based on its programming and environment. They are artificially built machines or devices that can perform activities with utmost accuracy and precision minimizing time constraints. Service robots are technologically advanced machines deployed to service and maintain certain activities. Research findings convey the essential fact that serving robots are now being deployed worldwide. Social robotics is one such field that heavily involves an interaction between humans and an artificially built machine. These man-built machines interact with humans and can also understand social terms and words. Modernization has bought changes in design and mechanisms due to this ever-lasting growth in technology and innovation. Therefore, food industries are also dynamically adapting to the new changes in the field of automation to reduce human workload and increase the quality of service. Deployment of a robot in the food industries which help to aid deaf and mute people who face social constraints is an evergrowing challenge faced by engineers for the last few decades. Moreover, a contactless form of speedy service system which accomplishes its task with at most precision and reduced complexity is a feat yet to be perfected. Preservation of personal hygiene, a better quality of service, and reduced labour costs is achieved.
Real Time Vision Hand Gesture Recognition Based Media Control via LAN & Wirel...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Natural Hand Gestures Recognition System for Intelligent HCI: A SurveyEditor IJCATR
Gesture recognition is to recognizing meaningful expressions of motion by a human, involving the hands, arms, face, head,
and/or body. Hand Gestures have greater importance in designing an intelligent and efficient human–computer interface. The applications
of gesture recognition are manifold, ranging from sign language through medical rehabilitation to virtual reality. In this paper a survey on
various recent gesture recognition approaches is provided with particular emphasis on hand gestures. A review of static hand posture
methods are explained with different tools and algorithms applied on gesture recognition system, including connectionist models, hidden
Markov model, and fuzzy clustering. Challenges and future research directions are also highlighted.
Real time hand gesture recognition system for dynamic applicationsijujournal
Virtual environments have always been considered as a means for more visceral and efficient human computer interaction by a diversified range of applications. The spectrum of applications includes analysis of complex scientific data, medical training, military simulation, phobia therapy and virtual prototyping.
Evolution of ubiquitous computing, current user interaction approaches with keyboard, mouse and pen are
not sufficient for the still widening spectrum of Human computer interaction. Gloves and sensor based trackers are unwieldy, constraining and uncomfortable to use. Due to the limitation of these devices the useable command set based diligences is also limited. Direct use of hands as an input device is an
innovative method for providing natural Human Computer Interaction which has its inheritance from textbased interfaces through 2D graphical-based interfaces, multimedia-supported interfaces, to full-fledged multi-participant Virtual Environment (VE) systems. Conceiving a future era of human-computer
interaction with the implementations of 3D application where the user may be able to move and rotate objects simply by moving and rotating his hand - all without help of any input device.
Real time hand gesture recognition system for dynamic applicationsijujournal
Virtual environments have always been considered as a means for more visceral and efficient human computer interaction by a diversified range of applications. The spectrum of applications includes analysis of complex scientific data, medical training, military simulation, phobia therapy and virtual prototyping. Evolution of ubiquitous computing, current user interaction approaches with keyboard, mouse and pen are not sufficient for the still widening spectrum of Human computer interaction. Gloves and sensor based trackers are unwieldy, constraining and uncomfortable to use. Due to the limitation of these devices the useable command set based diligences is also limited. Direct use of hands as an input device is an innovative method for providing natural Human Computer Interaction which has its inheritance from textbased interfaces through 2D graphical-based interfaces, multimedia supported interfaces, to full-fledged multi-participant Virtual Environment (VE) systems. Conceiving a future era of human-computer interaction with the implementations of 3D application where the user may be able to move and rotate objects simply by moving and rotating his hand - all without help of any input device. The research effort centralizes on the efforts of implementing an application that employs computer vision algorithms and gesture recognition techniques which in turn results in developing a low cost interface device for interacting with objects in virtual environment using hand gestures. The prototype architecture of the application comprises of a central computational module that applies the camshift technique for tracking of hands and its gestures. Haar like technique has been utilized as a classifier that is creditworthy for locating hand position and classifying gesture. The patterning of gestures has been done for recognition by mapping the number of defects that is formed in the hand with the assigned gestures. The virtual objects are produced using Open GL library. This hand gesture recognition technique aims to substitute the use of mouse for interaction with the virtual objects. This will be useful to promote controlling applications like virtual games, browsing images etc in virtual environment using hand gestures.
Hand Gesture Recognition using OpenCV and Pythonijtsrd
Hand gesture recognition system has developed excessively in the recent years, reason being its ability to cooperate with machine successfully. Gestures are considered as the most natural way for communication among human and PCs in virtual framework. We often use hand gestures to convey something as it is non verbal communication which is free of expression. In our system, we used background subtraction to extract hand region. In this application, our PCs camera records a live video, from which a preview is taken with the assistance of its functionalities or activities. Surya Narayan Sharma | Dr. A Rengarajan "Hand Gesture Recognition using OpenCV and Python" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38413.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/38413/hand-gesture-recognition-using-opencv-and-python/surya-narayan-sharma
ENHANCING ENGLISH WRITING SKILLS THROUGH INTERNET-PLUS TOOLS IN THE PERSPECTI...ijfcstjournal
This investigation delves into incorporating a hybridized memetic strategy within the framework of English
composition pedagogy, leveraging Internet Plus resources. The study aims to provide an in-depth analysis
of how this method influences students’ writing competence, their perceptions of writing, and their
enthusiasm for English acquisition. Employing an explanatory research design that combines qualitative
and quantitative methods, the study collects data through surveys, interviews, and observations of students’
writing performance before and after the intervention. Findings demonstrate a beneficial impact of
integrating the memetic approach alongside Internet Plus tools on the writing aptitude of English as a
Foreign Language (EFL) learners. Students reported increased engagement with writing, attributing it to
the use of Internet plus tools. They also expressed that the memetic approach facilitated a deeper
understanding of cultural and social contexts in writing. Furthermore, the findings highlight a significant
improvement in students’ writing skills following the intervention. This study provides significant insights
into the practical implementation of the memetic approach within English writing education, highlighting
the beneficial contribution of Internet Plus tools in enriching students' learning journeys.
A SURVEY TO REAL-TIME MESSAGE-ROUTING NETWORK SYSTEM WITH KLA MODELLINGijfcstjournal
Messages routing over a network is one of the most fundamental concept in communication which requires
simultaneous transmission of messages from a source to a destination. In terms of Real-Time Routing, it
refers to the addition of a timing constraint in which messages should be received within a specified time
delay. This study involves Scheduling, Algorithm Design and Graph Theory which are essential parts of
the Computer Science (CS) discipline. Our goal is to investigate an innovative and efficient way to present
these concepts in the context of CS Education. In this paper, we will explore the fundamental modelling of
routing real-time messages on networks. We study whether it is possible to have an optimal on-line
algorithm for the Arbitrary Directed Graph network topology. In addition, we will examine the message
routing’s algorithmic complexity by breaking down the complex mathematical proofs into concrete, visual
examples. Next, we explore the Unidirectional Ring topology in finding the transmission’s
“makespan”.Lastly, we propose the same network modelling through the technique of Kinesthetic Learning
Activity (KLA). We will analyse the data collected and present the results in a case study to evaluate the
effectiveness of the KLA approach compared to the traditional teaching method.
A COMPARATIVE ANALYSIS ON SOFTWARE ARCHITECTURE STYLESijfcstjournal
Software architecture is the structural solution that achieves the overall technical and operational
requirements for software developments. Software engineers applied software architectures for their
software system developments; however, they worry the basic benchmarks in order to select software
architecture styles, possible components, integration methods (connectors) and the exact application of
each style.
The objective of this research work was a comparative analysis of software architecture styles by its
weakness and benefits in order to select by the programmer during their design time. Finally, in this study,
the researcher has been identified architectural styles, weakness, and Strength and application areas with
its component, connector and Interface for the selected architectural styles.
SYSTEM ANALYSIS AND DESIGN FOR A BUSINESS DEVELOPMENT MANAGEMENT SYSTEM BASED...ijfcstjournal
A design of a sales system for professional services requires a comprehensive understanding of the
dynamics of sale cycles and how key knowledge for completing sales is managed. This research describes
a design model of a business development (sales) system for professional service firms based on the Saudi
Arabian commercial market, which takes into account the new advances in technology while preserving
unique or cultural practices that are an important part of the Saudi Arabian commercial market. The
design model has combined a number of key technologies, such as cloud computing and mobility, as an
integral part of the proposed system. An adaptive development process has also been used in implementing
the proposed design model.
AN ALGORITHM FOR SOLVING LINEAR OPTIMIZATION PROBLEMS SUBJECTED TO THE INTERS...ijfcstjournal
Frank t-norms are parametric family of continuous Archimedean t-norms whose members are also strict
functions. Very often, this family of t-norms is also called the family of fundamental t-norms because of the
role it plays in several applications. In this paper, optimization of a linear objective function with fuzzy
relational inequality constraints is investigated. The feasible region is formed as the intersection of two
inequality fuzzy systems defined by frank family of t-norms is considered as fuzzy composition. First, the
resolution of the feasible solutions set is studied where the two fuzzy inequality systems are defined with
max-Frank composition. Second, some related basic and theoretical properties are derived. Then, a
necessary and sufficient condition and three other necessary conditions are presented to conceptualize the
feasibility of the problem. Subsequently, it is shown that a lower bound is always attainable for the optimal
objective value. Also, it is proved that the optimal solution of the problem is always resulted from the
unique maximum solution and a minimal solution of the feasible region. Finally, an algorithm is presented
to solve the problem and an example is described to illustrate the algorithm. Additionally, a method is
proposed to generate random feasible max-Frank fuzzy relational inequalities. By this method, we can
easily generate a feasible test problem and employ our algorithm to it.
LBRP: A RESILIENT ENERGY HARVESTING NOISE AWARE ROUTING PROTOCOL FOR UNDER WA...ijfcstjournal
Underwater detector network is one amongst the foremost difficult and fascinating analysis arenas that
open the door of pleasing plenty of researchers during this field of study. In several under water based
sensor applications, nodes are square measured and through this the energy is affected. Thus, the mobility
of each sensor nodes are measured through the water atmosphere from the water flow for sensor based
protocol formations. Researchers have developed many routing protocols. However, those lost their charm
with the time. This can be the demand of the age to supply associate degree upon energy-efficient and
ascendable strong routing protocol for under water actuator networks. During this work, the authors tend
to propose a customary routing protocol named level primarily based routing protocol (LBRP), reaching to
offer strong, ascendable and energy economical routing. LBRP conjointly guarantees the most effective use
of total energy consumption and ensures packet transmission which redirects as an additional reliability in
compare to different routing protocols. In this work, the authors have used the level of forwarding node,
residual energy and distance from the forwarding node to the causing node as a proof in multicasting
technique comparisons. Throughout this work, the authors have got a recognition result concerning about
86.35% on the average in node multicasting performances. Simulation has been experienced each in a
wheezy and quiet atmosphere which represents the endorsement of higher performance for the planned
protocol.
STRUCTURAL DYNAMICS AND EVOLUTION OF CAPSULE ENDOSCOPY (PILL CAMERA) TECHNOLO...ijfcstjournal
This research paper examined and re-evaluates the technological innovation, theory, structural dynamics
and evolution of Pill Camera(Capsule Endoscopy) technology in redirecting the response manner of small
bowel (intestine) examination in human. The Pill Camera (Endoscopy Capsule) is made up of sealed
biocompatible material to withstand acid, enzymes and other antibody chemicals in the stomach is a
technology that helps the medical practitioners especially the general physicians and the
gastroenterologists to examine and re-examine the intestine for possible bleeding or infection. Before the
advent of the Pill camera (Endoscopy Capsule) the colonoscopy was the local method used but research
showed that some parts (bowel) of the intestine can’t be reach by mere traditional method hence the need
for Pill Camera. Countless number of deaths from stomach disease such as polyps, inflammatory bowel
(Crohn”s diseases), Cancers, Ulcer, anaemia and tumours of small intestines which ordinary would have
been detected by sophisticated technology like Pill Camera has become norm in the developing nations.
Nevertheless, not only will this paper examine and re-evaluate the Pill Camera Innovation, theory,
Structural dynamics and evolution it unravelled and aimed to create awareness for both medical
practitioners and the public.
AN OPTIMIZED HYBRID APPROACH FOR PATH FINDINGijfcstjournal
Path finding algorithm addresses problem of finding shortest path from source to destination avoiding
obstacles. There exist various search algorithms namely A*, Dijkstra's and ant colony optimization. Unlike
most path finding algorithms which require destination co-ordinates to compute path, the proposed
algorithm comprises of a new method which finds path using backtracking without requiring destination
co-ordinates. Moreover, in existing path finding algorithm, the number of iterations required to find path is
large. Hence, to overcome this, an algorithm is proposed which reduces number of iterations required to
traverse the path. The proposed algorithm is hybrid of backtracking and a new technique(modified 8-
neighbor approach). The proposed algorithm can become essential part in location based, network, gaming
applications. grid traversal, navigation, gaming applications, mobile robot and Artificial Intelligence.
EAGRO CROP MARKETING FOR FARMING COMMUNITYijfcstjournal
The Major Occupation in India is the Agriculture; the people involved in the Agriculture belong to the poor
class and category. The people of the farming community are unaware of the new techniques and Agromachines, which would direct the world to greater heights in the field of agriculture. Though the farmers
work hard, they are cheated by agents in today’s market. This serves as a opportunity to solve
all the problems that farmers face in the current world. The eAgro crop marketing will serve as a better
way for the farmers to sell their products within the country with some mediocre knowledge about using
the website. This would provide information to the farmers about current market rate of agro-products,
their sale history and profits earned in a sale. This site will also help the farmers to know about the market
information and to view agricultural schemes of the Government provided to farmers.
EDGE-TENACITY IN CYCLES AND COMPLETE GRAPHSijfcstjournal
It is well known that the tenacity is a proper measure for studying vulnerability and reliability in graphs.
Here, a modified edge-tenacity of a graph is introduced based on the classical definition of tenacity.
Properties and bounds for this measure are introduced; meanwhile edge-tenacity is calculated for cycle
graphs and also for complete graphs.
COMPARATIVE STUDY OF DIFFERENT ALGORITHMS TO SOLVE N QUEENS PROBLEMijfcstjournal
This Paper provides a brief description of the Genetic Algorithm (GA), the Simulated Annealing (SA)
Algorithm, the Backtracking (BT) Algorithm and the Brute Force (BF) Search Algorithm and attempts to
explain the way as how the Proposed Genetic Algorithm (GA), the Proposed Simulated Annealing (SA)
Algorithm using GA, the Backtracking (BT) Algorithm and the Brute Force (BF) Search Algorithm can be
employed in finding the best solution of N Queens Problem and also, makes a comparison between these
four algorithms. It is entirely a review based work. The four algorithms were written as well as
implemented. From the Results, it was found that, the Proposed Genetic Algorithm (GA) performed better
than the Proposed Simulated Annealing (SA) Algorithm using GA, the Backtracking (BT) Algorithm and
the Brute Force (BF) Search Algorithm and it also provided better fitness value (solution) than the
Proposed Simulated Annealing Algorithm (SA) using GA, the Backtracking (BT) Algorithm and the Brute
Force (BF) Search Algorithm, for different N values. Also, it was noticed that, the Proposed GA took more
time to provide result than the Proposed SA using GA.
PSTECEQL: A NOVEL EVENT QUERY LANGUAGE FOR VANET’S UNCERTAIN EVENT STREAMSijfcstjournal
In recent years, the complex event processing technology has been used to process the VANET’s temporal
and spatial event streams. However, we usually cannot get the accurate data because the device sensing
accuracy limitations of the system. We only can get the uncertain data from the complex and limited
environment of the VANET. Because the VANET’s event streams are consist of the uncertain data, so they
are also uncertain. How effective to express and process these uncertain event streams has become the core
issue for the VANET system. To solve this problem, we propose a novel complex event query language
PSTeCEQL (probabilistic spatio-temporal constraint event query language). Firstly, we give the definition
of the possible world model of VANET’s uncertain event streams. Secondly, we propose an event query
language PSTeCEQL and give the syntax and the operational semantics of the language. Finally, we
illustrate the validity of the PSTeCEQL by an example.
CLUSTBIGFIM-FREQUENT ITEMSET MINING OF BIG DATA USING PRE-PROCESSING BASED ON...ijfcstjournal
Now a day enormous amount of data is getting explored through Internet of Things (IoT) as technologies
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Assuring Contact Center Experiences for Your Customers With ThousandEyes
Real time human-computer interaction
1. International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.4, No.4, July 2014
DOI:10.5121/ijfcst.2014.4403 37
REAL-TIME HUMAN-COMPUTER INTERACTION
BASED ON FACE AND HAND GESTURE
RECOGNITION
Reza Azad1
, Babak Azad2
, Nabil Belhaj Khalifa3
, Shahram Jamali4
1
Department of Electrical and Computer Engineering, Shahid Rajaee Teacher Training
University, Tehran, Iran
2
Computer Engineering Department, University of Mohaghegh Ardabili, Ardabil, Iran
3
Blaise Pascal University, Clermont Ferrand, France
4
Associate professor, Department of Computer Engineering, University of Mohaghegh
Ardabili, Ardabil, Iran
ABSTRACT
At the present time, hand gestures recognition system could be used as a more expected and useable
approach for human computer interaction. Automatic hand gesture recognition system provides us a new
tactic for interactive with the virtual environment. In this paper, a face and hand gesture recognition
system which is able to control computer media player is offered. Hand gesture and human face are the key
element to interact with the smart system. We used the face recognition scheme for viewer verification and
the hand gesture recognition in mechanism of computer media player, for instance, volume down/up, next
music and etc. In the proposed technique, first, the hand gesture and face location is extracted from the
main image by combination of skin and cascade detector and then is sent to recognition stage. In
recognition stage, first, the threshold condition is inspected then the extracted face and gesture will be
recognized. In the result stage, the proposed technique is applied on the video dataset and the high
precision ratio acquired. Additional the recommended hand gesture recognition method is applied on static
American Sign Language (ASL) database and the correctness rate achieved nearby 99.40%. also the
planned method could be used in gesture based computer games and virtual reality.
KEYWORDS
Human computer interaction; hand gesture recognition; hand tracking; computer music controlling.
1. INTRODUCTION
In the existing world, the communication with the intelligent devices has progressive to such a
magnitude that as humans it has become essential and we cannot live without its capability. The
new machinery has become so embedded into our regular lives that we use it to shop, work,
interconnect and even interest our self [1]. It has been extensively supposed that the calculating,
communiqué and presentation machineries progress extra, but the current systems may become a
holdup in the effective operation of the existing information flow. For efficiently using of these
systems, most computer applications need more and more communication. For that motive,
human-computer interaction (HCI) has been a dynamic field of study in the last decades. Initially
systems that are used for graphically HCI system are mouse and keyboards. Even if the
innovation of the mouse and keyboard is a great development, there are still circumstances in
which these devices are irreconcilable for HCI. This is principally the case for the communication
with 3D objects.
2. International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.4, No.4, July 2014
38
The two points of freedom of the mouse could not suitably emulate the 3 dimensions of space.
The use of hand gestures offers a smart and natural optional to these burdensome interface tools
for human computer communication. With use of hands gesture recognition system can help
people to interactive with computers in a more intuitive mode. Hand gesture recognition owns
wide applications in sign language recognition [2], [3], computer games [4], virtual reality [5] and
HCI systems [6]. There were numerous gesture recognition methods established for tracking and
recognizing numerous hand gestures. Each one of them has their advantage and disadvantage.
Wired technology is the oldest one, in which in order to connect or interface with the computer
system, users need to tie up themselves with the help of wire. User cannot freely move in the
room in wired technology as they connected with the computer system via wire and limited with
the length of wire. The best example for the wired technology is the instrumented gloves -also
called data gloves or electronics gloves. These electronics gloves have some sensors, and thanks
to these sensors they provide information related to location of the hand, position orientation of
finger etc. Output results of these data gloves are well but for the wide range common
application using propose they are expensive through to being an electronic device [3].
After the Data gloves, the optical markers is appeared. The optical markers detect the location of
hand or tips of fingers by projecting Infra-Red light and reflect this light on screen. These
organizations also offer the worthy result but need a very complex configuration. nowadays some
new methods have been proposed for hand gesture recognition, such as Image based systems
which needs processing of image structures like texture, color etc. the approaches on optical
markers are very luxurious and have very difficult configuration [3]. Also the technique based on
image processing is weedy against under diverse illumination situation, color texture modifying,
which leads to variations in observed outcomes. For improving the image processing based
technique for hand gestures recognition scheme we planned current paper method. In this paper,
we use only a video camera and a PC to progress a hand gesture based HCI system. Our
methodology for HCI is contain of four stages, I. face and hand detection based on fusion of skin
detection and cascade detector; II. extracting hand new position based on particle filter algorithm;
III. measuring threshold condition based on hand new position and applying face & hand
recognition stage; IV. Controlling smart device (in this paper we considered computer music
player) by extracted information from third stage.
The rest of the paper is prepared as follows. In section two proposed methods is offered and in
section three and four the practical result and conclusion are detailed respectively
2. PROPOSED METHOD FOR HCI
In the current paper, we advance a human-computer interaction scheme using a video camera to
acquire images. Overall chart of the suggested method is depicted in Fig. 1. In the first stage of
projected method, for hand and face detection, first of all we tag the regions of an image using
skin colors, which performance as nominees for the face and hand. Next, connected components
are exposed from these image regions. Third, we fixed a threshold for the connected components
for removing noise, rejecting the zones which are tiny to be candidates for the face or hand. For
the outstanding succeeded nominees, first, the length of each of these connected component is
found and two area that has the highest length is nominated as face and hand candidate. Then
between these two candidates the face location is selected by Viola jones detector.
2.1. Stage 1: Face & Hand Detection
In order to extract the face and hand from the images, a skin pixel finder, connected component
(CC) generation and Viola jones detector has been implemented.
3. International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.4, No.4, July 2014
39
Figure 1. Overall illustration of the proposed method
2.1.1. Skin region detection
Skin region extracting is one of the greatest and primary step in face detection purpose [7]. In this
respect, many approaches have been suggested to detect skin which showed high detection
amount. In this paper we used our skin detection method that we cited earlier in [8]. Fig. 2(b)
shows the result of this detector on the entry image.
2.1.2. Creating Connected Component by mathematical morphology
Mathematical morphology is one of the divisions of image processing that contends about
structure and impression of abject in images [9]. After applying the skin detector, all possible skin
areas are extracted and represented as white pixels. It’s possible that the small noisy regions will
be existing, but we undertake that the region related to the hand and faces are the biggest.
Consequently we first destroy the noise by “disk” erosion. Those small noisy areas, wrongly
discovered as hand skin, are typically skin-color like objects under certain light situation. The
erosion procedure could successfully erase those small noisy areas. Nonetheless the real areas
Stage2:Tracking&measuringthreshold
Input Video
Skin Region Detection Extracting Connected Component and
Noise Removing
Extracting Connected Component and
Noise Removing
Face& hand Detection based on
Boosted Cascade of Simple Features
Stage 1: Face & Hand Detection on First Frame
No
Frame= Next Frame
Track Hand by Particle Filter Algorithm in each Frame
Extract Face Location by
Boosted Cascade of Simple
Features
Generate Hand New
Location by Particle Filter
Threshold
Yes
Extracted Hand Gesture Extracted Face Image
PCA Feature Set Extraction
Distance & Angle Feature on
Convex hull Points
Classification by ME & Generating Face Owner, Hand Sign
Stage 3: Face & Hand Recognition
Controlling Smart Devise (like computer music
player) by Extracted Information
Stage 4:
4. International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.4, No.4, July 2014
40
related to the hand and face may also shrink with considering the erosion procedure planned for
noise. Accordingly we applied the dilation to strength the major discovered area, help to improve
the anticipated hand and face detection. Meanwhile the hand and face are close areas; the holes
are filled in this phase. After this progression, the two big areas extracted as face and hand
candidate and rest of the area reduced. Fig. 2(c) shows the outcome.
2.1.3. Viola and Jones Detector for Face Detection
The Viola and Jones approached for object detection that introduced in 2001 has become one of
the most common real-time frameworks. This method is basically a cascade of binary linear
classifiers which are consequently applied to the sliding window input. For more detail see the
[10]. In current paper we qualified the Viola-Jones framework with a lot of human faces, such as:
natural, tilted, side view, darken and blurred with considering any distance and lighting condition.
After training the classifier, at the running time the face area is nominated between two object
that extracted by the last step. In Fig. 2(d) the blue square selected as face by applying this
detector.
Figure 2. (a): entrance image (b): detected skin region (c): result of mathematical morphology (d): detected
face and hand region
2.2. Stage 2: Tracking & measuring threshold condition
In this stage, the new position of hand is extracted by particle filter algorithm. For this respect, we
used the extracted hand image (from first frame), as input for particle filter algorithm. By
applying this algorithm we achieved hand new position in each frame. Also we extracted face
new position in each frame by Viola jones detector. After extracting face and hand new position
we used (1) for measuring distance between these objects.
= ( − ) − ( − ) (1)
In top relation are the face center coordinates and are the hand center
coordinates in 2D space. By extracting the distance we used the following rule, also Fig. 3 shows
the sample of threshold condition.
≤ ℎ ℎ
ℎ : 3
: Repeat this stage again on new frame.
5. International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.4, No.4, July 2014
41
Figure 3. (a): distance between face and hand is bigger than threshold (b): distance between face and hand
is smaller than threshold
2.3. Stage 3: Face & Hand Recognition
2.3.1. Hand gesture feature set
In our scheme we calculated features with use of angel and distance of intersections points of the
images as follows: First the minimum rectangle containing the hand gesture location is extracted.
Then for improving the accuracy and removing the dependency of features to size, position we
transformed each image to a standard size of 100×100 pixels. We selected this normalized value
based of several experiments. Then we take out the outline point of images. Finally we used the
Graham algorithm ( ( )) for extracting the intersections points. Fig. 4 shows the sample of
these intersections points.
Figure 4. Intersection points of hand gesture
Then we extracted angle and distance feature set by (2) and (3) respectively:
(a ) = θ(b ,b ) i = 1,2,3,…. (2)
(y ) = b − b − b − b = 1,2,3,… (3)
In top relation is angel feature set for any hand gesture and θ(b ,b ) is angle of two
intersection points to image horizontal level and is distance feature set for any hand gesture
and = 1,2,3,… is intersection points of hand gesture. By using this instruction we achieved 6
features for each gesture. As second feature set we mined the Fourier descriptors. Fourier
descriptor is a moral descriptor of the outlines, and is rotational, scale and translation invariance.
Fourier descriptors are achieved by computing Fourier factors of the arrangement of gestures
edge point. The technique telling the gesture feature has nothing to do with the initial point in
boundary and recognizes hand gesture fast. By using a specific point on the boundary of
subdivision gesture as the initial point, coordinate structure of the boundary is reached by
counter-clockwise as mentioned in (4):
( ) = [ ( ), ( )], = 0,1,. . , − 1 (4)
6. International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.4, No.4, July 2014
42
And the plural formula is as (5):
( ) = ( ) + ( ), ( = 0,1,… , − 1), = √1 − (5)
2D problem will be transformed into a 1D problem. The border of 1D Discrete Fourier coefficient
sequence is demarcated as (6):
( ) = ∑ ( ) exp − , = 0,1,… , − 1 (6)
The Fourier descriptors of example gestures is shown in Fig. 5 and attained as Table.1.
Figure 5. Contour of gesture. (a): gesture A (b): gesture B (c): gesture C (d): gesture D
Table 1. The Fourier descriptors of the hand gestures
Hand Gesture
Class
Fourier Feature set
A 1.000 0.3529 0.1970 0.0587 0.0095 0.0975 0.1143
B 1.000 0.1514 0.0046 0.0067 0.0062 0.0028 0.0010
C 1.000 22.513 0.7957 0.4742 1.5620 0.0111 0.0827
D 1.000 0.0513 0.0010 0.0272 0.0121 0.0073 0.0001
2.3.2. Face feature set
There are many approaches for face recognition field, such as [11], [12]. In this part we used
Principal Component Analysis (PCA) technique for feature extraction. The PCA was offered by
Karl Pearson and Harold Hotelling to transform a set of feasibly correlated variables into a
reduced set of uncorrelated variables [13]. The notion is that a high-dimensional database is
frequently designated by correlated variables and for this reasons only a few significant
dimensions account for maximum of the information. The PCA approaches find the directions
with the highest variance in the data, called principal components.
2.3.3. Classification bay Mixture of Experts
In order to combining classifiers, there are two main strategies: selection and fusion. In classifier
selection, every member is assigned to learn a part the feature space, whereas in classifier fusion,
it is supposed that on the whole feature space, each ensemble member is trained. The mixture of
experts (ME) is one of the most popular methods of classifier selection, which originally
7. International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.4, No.4, July 2014
43
proposed by Jacobs et al. [14]. Expert combination actually is a classic strategy that has been
broadly used in various problem solving tasks [15-17]. A group of single with diverse and
complementary skills tackles a task jointly such that a performance higher than any single
individual can make is achieved via integrating the strengths of individuals [18].
Architecture of the ME is composed of N local experts and also, for defining the outputs expert
weights conditioned on the input, there is a gating network. In our proposed method, there is a
hidden layer for every expert - a multi-layer perceptron (MLP) neural network-, which computes
an output as a function of the input stimuli vector , and also, there are output layers, a
sigmoid activation function, and a set of hidden weights. We suppose that in a different area of
the input space each expert specializes. A weight assigned to each of the expert’s output, O
by the gating network. Also, the g = {g , g , … , g } determined as a function of the input
vector x, and a set of parameters determined as weights of its hidden; and hole of output layers
and a sigmoid activation function, determined by the gating network. Where the expert i can
generate the desired output y, every element gi of g can be interpreted as estimates of the prior
probability. The MLP neural network and softmax nonlinear operator are the gating networks two
constitutive layers. Thus the gating network computes the output vector of the MLP layer of the
gating network- τ = {τ , τ , … , τ }-, then applies the softmax function to get (7):
g =
( )
∑ ( )
, i = 1,2,… , N (7)
Here g s are non-negative and sum to 1, and N is the number of expert networks. The final mixed
output of the entire network is (8):
T = ∑ o g , i = 1,2,… . , N (8)
By the error Back Propagation (BP) algorithm, the weights of MLPs are learned. For the gating
network and each expert i, the weights are updated according to the (9):
Δ = η h (y − O ) O (1 − O ) V ,
Δ = η h W (y − O ) O (1 − O ) V (1 − O ) x,
Δ = η (h − g) τ(1 − τ) ϑ ,
Δ = η ξ (h − g) τ(1 − τ) ϑ (1 − ϑ)x, (9)
For the expert and the gating networks, the rate of learning shown by η and η . Weight matrices
of input to hidden and hidden to output layer, shown by the ω and w respectively. ζ and ξ are the
weight matrices of hidden to output layer and input to hidden, respectively, for the gating
network. V and ϑ are the transpose of νi and ϑ, the output matrices of the hidden layer of expert
and gating networks, respectively. In the above formulas h = {h , h ,… , h } is a vector such
that each hi is an estimate of the posterior probability that expert i can generate the desired
outputy, and is computed as (10):
h =
( ) ( )
∑ ( )
(10)
2.4. Stage 4: Controlling smart device
After extracting information (face owner and hand sign) from last stage, we used this information
for controlling computer music player by Matlab functions. Also this stage could be used as
application in: smart device controlling, smart TV, robots, computer game and etc.
8. International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.4, No.4, July 2014
44
3. PRACTICAL AND IMPLEMENTATION RESULT
Our mentioned method has been done on Intel Core i3-2330M CPU, 2.20 GHz with 2 GB RAM
with use of Matlab software. In Fig. 6 the face of worked systems is shown. Data achievement,
mixture of experts’ configuration and performance of the proposed system are labels in the next
subsections.
Figure 6. Human computer interaction system implemented in Matlab software
3.1. Data Achievement
The database which we used for human hand gesture recognition while moving comprises five
types of gesture obtained from five persons with dissimilar scene. The video sequence has been
arranged using a fixed place 3 Mega Pixel Nokia 5233 while the person is moving toward it. Fig.
7 shows examples of these gestures.
Figure 7. Hand gesture signs (a): Stop music (b): Play music (c): Next music (d): Volume up (e): Volume
down
3.2. Mixture of Experts Configuration
As proposed earlier, our neural network scheme contains several MLP neural networks that
perform the experts’ role and they are mixed through the mixture of experts’ methodology. The
training set comprises the intersections point’s features and Fourier descriptors features of the
9. International Journal in Foundations of Computer Science & Technology (IJFCST), Vol.4, No.4, July 2014
45
50% images of train set and the other 50% images are used for the testing. Subsequently the input
data are in the 17 dimensional space the topology of the planned network should have 17 nodes in
the input layer and also since the number of gestures is 5, then the quantity of the nodes used in
the output layer must be 5 (every node represents one hand gesture). Consequently, our proposed
neural networks topologies are differ only in the quantity of the hidden layer nodes. A lot of
configurations of the network by altering the complexity of the experts or the quantity of experts
are tested in implementing the neural network and also different values are used parameters
configuration. In whole the experimental result the gating learning rate was equal to 0.4 and the
amount of its hidden nodes was 40 nodes, the expert learning degree was 0.9, and the network
trained by 300 epochs. We tasted our system on different number of hidden nodes of experts and
the outcomes are detailed in table 2.
Table 2. The recognition degree based on diverse number of hidden nodes and experts on the hand gesture
database
Quantity of
Nodes
Hand Gesture Database
Two Experts Three Experts Four Experts
10 nodes 79.50% 81% 80%
15 nodes 90.23% 96% 96%
20 nodes 94.75% 99.20% 97.53%
30 nodes 95% 98.05% 96.86%
As it is clear from the table 2, the accuracy rate for the schemes having 10 nodes in their hidden
layer is proportionately low. With expanding the quantity of hidden nodes from 10 to 15 the
accuracy rate improves meaningfully, and in the two- experts system with 20 hidden nodes the
accuracy rate of 94.75% proof that the quantity of experts was deficient that can’t divide the input
space appropriately. In the four-expert organization with the similar number of hidden nodes
matching to the system with three-experts there are numerous free parameters that makes the
network too complex to get a well result than 3-experts. Thus we used the network with three-
experts and 20 numbers of hidden nodes as our classifier because it distributes the input space in
the greatest way and establish a balance of the amount of experts and hidden nodes.
3.3. Performance Evaluation of the Proposed Method
The experimental outcomes proofed that the proposed system has a robust recognition level in
detecting and recognition human computer interaction technique. Table 3 characterizes the
experimental results. Na, NCR and AR respectively refer to number of gesture in videos, number
of correct recognition and the accuracy rate.
Table 3. The recognition rate for various gestures in video sequence
Hand Gesture NA NCR AR
G1 (Stop music) 50 50 100%
G2 (Play music) 50 49 98%
G3 (Next music) 50 50 100%
G4 (Volume up) 50 49 98%
G5 (Volume down) 50 50 100%
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Furthermore, for well understanding of the wrong classification on outcomes we have depicted
the confusion matrix of the classifier yield in Table 4. Notice that G1-G4 respectively
representative of these hand sign gesture: stop music, play music, next music, volume up and
volume down.
Table 4. Confusion matrix of the proposed hand gesture recognition system
Classified As
G1 G2 G3 G4 G5
G1 50 0 0 0 0
G2 0 49 0 1 0
G3 0 0 50 0 0
G4 0 0 0 49 1
G5 0 0 0 0 50
Further for additional experimental enquiry, we had tested the above mention system on [3] static
hand gesture database. Table 5 shows the accuracy level for each hand gesture.
Table 5. Recognition rate of the proposed method for each static hand gesture
Hand
Gesture class
Number of
Input
Images
Recognized Image Precision Rate of Each
Technique
Technique
[3]
proposed
method
Technique
[3]
proposed
method
A 21 20 21 95.23% 100%
B 21 21 21 100% 100%
C 21 21 21 100% 100%
D 21 21 21 100% 100%
E 21 19 20 90.47% 95.23%
F 21 21 21 100% 100%
G 21 21 21 100% 100%
H 21 21 21 100% 100%
I 21 21 21 100% 100%
K 21 21 21 100% 100%
L 21 21 21 100% 100%
M 21 19 20 90.47% 95.23%
N 21 21 21 100% 100%
O 21 21 21 100% 100%
P 21 21 21 100% 100%
Q 21 21 21 100% 100%
R 21 21 21 100% 100%
S 21 20 21 95.23% 100%
T 21 21 21 100% 100%
U 21 21 21 100% 100%
V 21 21 20 100% 95.23%
W 21 21 21 100% 100%
X 21 21 21 100% 100%
Y 21 21 21 100% 100%
Total 504 498 501 98.80% 99.40%
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Great detection degree shows the quality of proposed methodology to use in every applications,
which are desired a HCI. Also we attained 100% accuracy in face recognition stage.
4. CONCLUSIONS
We offered a human-computer interaction (HCI) scheme using a PC and a video camera
established on face and hand gesture recognition. A face recognition stage was used for viewer
verification and the hand gesture recognition stage for monitoring computer media player. In our
suggested method, first, we extracted the hand and face location from the main image by
combination of skin discovery and Viola Jones detector. After extracting face and hand we used
particle filter algorithm and threshold condition for applying recognition stage. Finally in the
recognition stage the feature set for face and hand gesture extracted respectively and recognized
by the mixture of experts. In the result stage, our proposed method is tested on the video dataset
and we achieved proximally 99.20% accuracy rate. Auxiliary we applied the mentioned algorithm
on static American Sign Language (ASL) database and we obtained 99.40% correctness ratio.
ACKNOWLEDGEMENTS
This research is supported by the Blaise Pascal University and the SRTTU, Tehran, Iran
(No.22970060-9).
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Authors
Reza Azad obtained his B.Sc. degree with honor in computer software engineering from SRTTU in 2014.
He is IEEE & IEEE conference reviewer Member. Awarded as best student in 2013 and 2014 by the
SRTTU and awarded as best researcher in 2013 by the SRTTU. He achieved fourth place in Iranian
university entering exam. In addition he’s a member of Iranian elites. He has a lot of scientific papers in
international journal and conferences, such as IEEE, Springer and etc. his interested research are artificial
intelligence and computer vision.
Babak Azad is a researcher from Islamic Azad University. He achieved a lot of awards and publication on
scientific papers in international journals and conferences, during his B.Sc. education. His most interest
topics are machine learning and network.
Nabil BELHAJ KHALIFA is a student of Master Research at Blaise Pascal University, France. Actually,
he is an intern at LIMOS, a research laboratory in Clermont-Ferrand in France, in order to achieve his
M.Sc. degree in computer science in field of computer vision. He obtained his Engineering diploma in
computer science from ISSATSo university in Tunisia in 2012. And before, he get his B.Sc. degree in
computer software from ISSATSo, Tunisia. His research interests include image processing, computer
vision, computer graphics, machine learning and artificial neural networks.
Shahram Jamali is currently an Associate Professor in Mohaghegh Ardabili University, Ardebil, Iran. He
achieved his Ph.D degree in Architecture of Computer Systems in 2008 from Iran University of Science &
Technology, Tehran, Iran. He has more than 100 scientific papers in international journals and conferences,
such as IEEE, Elsevier, Springer and etc. His research topics are Network security and soft computing.