ABSTRACT:

• An automatic invocation of hardware via a Image processing system controlled by cloud devices is the core idea of the project. The entire idea of the project is to identify the information forensics is used to promote activities within the board technical area of information forensics and security.
• The existing pattern is to identify the face recognition and fractal recognition through networking. The idea is to find image status and hack the hardware and update to the user.
• This models enables us to represent the building individual image sets followed by measuring the similarity metric and compare the models.
• We represents the input images as the set of values for authentication, which is available in a linear or affine feature space and characterize each individual image set by a convex geometric region spanned by its feature points. Set dissimilarity is measured by geometric distances (distances of closest approach) between convex models.

EXISTING SYSTEM:

• The existing system focuses on the image processing and recognition.
• We don’t have innovative system to activate the web camera dynamically and extract the photographs online.
• The greatest thing in the system is, the webcam will get activated once the instructions is provided through the network.
• IN addition, this system involves the fractal recognition in identifying the individuals.
• It’s the greatest milestone in the field of cyber security and thefts.

PROPOSED SYSTEM:

• The proposed system involves the following,
• Remote activation of the computer
• Enable the webcam of remote computer
• Activate the image capture in the webcam
• Enable fractal recognition of the captured images
• Identify the image and find the threat and theft person

ARCHITECTURE:

SCREENSHOT:

SAMPLE CODE:

using System;
using System.Collections.Generic;
using System.Text;
using System.Drawing;
using System.Drawing.Drawing2D;

namespace MultiFaceRec
{
public class ImageModifier
{
private string _bitmapPath;
private Bitmap _currentBitmap;
private Bitmap _bitmapbeforeProcessing;

MicrosoftFame.MicrosoftFame MicrosoftFame = new MicrosoftFame.MicrosoftFame();

public ImageModifier()
{

}

public Bitmap CurrentBitmap
{
get
{
if (_currentBitmap == null)
_currentBitmap = new Bitmap(1, 1);
return _currentBitmap;
}
set { _currentBitmap = value; }
}

public Bitmap BitmapBeforeProcessing
{
get { return _bitmapbeforeProcessing; }
set { _bitmapbeforeProcessing = value; }
}

public string BitmapPath
{
get { return _bitmapPath; }
set { _bitmapPath = value; }
}

public enum ColorFilterTypes
{
Red,
Green,
Blue
};
namespace Emgu.CV
{

public class ObjectDetector
{
private Image[] _objectImages;
private Image _modImage;
private Matrix[] _objectValues;
private string[] _labels;
private double _objectDistanceThreshold;

/// 

/// Get the vectors that form the space
/// 
/// The set method is primary used for deserialization, do not attemps to set it unless you know what you are doing
public Image[] ObjectImages
{
get { return _objectImages; }
set { _objectImages = value; }
}

/// 

/// Get or set the labels for the corresponding training image
/// 
public String[] Labels
{
get { return _labels; }
set { _labels = value; }
}


public double ObjectDistanceThreshold
{
get { return _objectDistanceThreshold; }
set { _objectDistanceThreshold = value; }
}

public Image ModerateImage
{
get { return _modImage; }
set { _modImage = value; }
}

public Matrix[] ObjectValues
{
get { return _objectValues; }
set { _objectValues = value; }
}

private ObjectDetector()
{
}


public ObjectDetector(Image[] images, ref MCvTermCriteria termCrit)
: this(images, GenerateLabels(images.Length), ref termCrit)
{
}

private static String[] GenerateLabels(int size)
{
String[] labels = new string[size];
for (int i = 0; i < size; i++)
labels[i] = i.ToString();
return labels;
}

public ObjectDetector(Image[] images, String[] labels, ref MCvTermCriteria termCrit)
: this(images, labels, 0, ref termCrit)
{
}

public ObjectDetector(Image[] images, String[] labels, double objectDistanceThreshold, ref MCvTermCriteria termCrit)
{
Debug.Assert(images.Length == labels.Length, "The number of images should equals the number of labels");
Debug.Assert(objectDistanceThreshold >= 0.0, "Distance threshold should always >= 0.0");

CalcObjects(images, ref termCrit, out _objectImages, out _modImage);


_objectValues = Array.ConvertAll, Matrix>(images,
delegate(Image img)
{
return new Matrix(ObjectDecomposite(img, _objectImages, _modImage));
});

_labels = labels;

_objectDistanceThreshold = objectDistanceThreshold;
}

#region static methods
public static void CalcObjects(Image[] pictures, ref MCvTermCriteria termCrit, out Image[] objectImages, out Image avg)
{
int width = pictures[0].Width;
int height = pictures[0].Height;

IntPtr[] inObjs = Array.ConvertAll, IntPtr>(pictures, delegate(Image img) { return img.Ptr; });

if (termCrit.max_iter <= 0 || termCrit.max_iter > pictures.Length)
termCrit.max_iter = pictures.Length;

int maxObjs = termCrit.max_iter;

#region initialize images
objectImages = new Image[maxObjs];
for (int i = 0; i < objectImages.Length; i++)
objectImages[i] = new Image(width, height);
IntPtr[] eigObjs = Array.ConvertAll, IntPtr>(objectImages, delegate(Image img) { return img.Ptr; });
#endregion

avg = new Image(width, height);

CvInvoke.cvCalcEigenObjects(
inObjs,
ref termCrit,
eigObjs,
null,
avg.Ptr);
}

public static float[] ObjectDecomposite(Image src, Image[] objectImages, Image avg)
{
return CvInvoke.cvEigenDecomposite(
src.Ptr,
Array.ConvertAll, IntPtr>(objectImages, delegate(Image img) { return img.Ptr; }),
avg.Ptr);
}
#endregion

public Image ObjectProjection(float[] objectValue)
{
Image res = new Image(_modImage.Width, _modImage.Height);
CvInvoke.cvEigenProjection(
Array.ConvertAll, IntPtr>(_objectImages, delegate(Image img) { return img.Ptr; }),
objectValue,
_modImage.Ptr,
res.Ptr);
return res;
}

public float[] GetObjectDistances(Image image)
{
using (Matrix objectValue = new Matrix(ObjectDecomposite(image, _objectImages, _modImage)))
return Array.ConvertAll, float>(_objectValues,
delegate(Matrix objectValueI)
{
return (float)CvInvoke.cvNorm(objectValue.Ptr, objectValueI.Ptr, Emgu.CV.CvEnum.NORM_TYPE.CV_L2, IntPtr.Zero);
});
}

public void FindMostSimilarObject(Image image, out int index, out float objectDistance, out String label)
{
float[] dist = GetObjectDistances(image);

index = 0;
objectDistance = dist[0];
for (int i = 1; i < dist.Length; i++)
{
if (dist[i] < objectDistance)
{
index = i;
objectDistance = dist[i];
}
}
label = Labels[index];
}

public String Recognize(Image image)
{
int index;
float objectDistance;
String label;
FindMostSimilarObject(image, out index, out objectDistance, out label);

return (_objectDistanceThreshold <= 0 || objectDistance < _objectDistanceThreshold )  ? _labels[index] : String.Empty;
}
}
}
namespace MultiFaceRec
{
public class PropertyClass
{
static string username;

public static string UserName
{
get
{
return username;
}
set
{
username = value;
}
}

static string designation;

public static string Designation
{
get
{
return designation;
}
set
{
designation = value;
}
}

static int userType;

public static int UserType
{
get
{
return userType;
}
set
{
userType = value;
}
}
static string ImagePath;

public static string Imagepath
{
get
{
return ImagePath;
}
set
{
ImagePath = value;
}
}


}
}
namespace MultiFaceRec
{
public partial class Register : Form
{

Interop.Recognition.Recognition recognition = new Interop.Recognition.Recognition();
//Declararation of all variables, vectors and haarcascades
Image currentFrame;
Capture grabber;
HaarCascade face;
HaarCascade eye;
MCvFont font = new MCvFont(FONT.CV_FONT_HERSHEY_TRIPLEX, 0.5d, 0.5d);
Image result, TrainedFace = null;
Image gray = null;
List> pictures = new List>();
List labels= new List();
List NamePersons = new List();
int ContTrain, NumLabels, t;
string name, names = null;

public Register()
{
InitializeComponent();

face = new HaarCascade(recognition.FaceFileName());
eye = new HaarCascade(recognition.EyeFileName());
try
{
string Labelsinfo = File.ReadAllText(Application.StartupPath + recognition.TrainedLabels());
string[] Labels = Labelsinfo.Split('%');

NumLabels = Convert.ToInt16(Labels[0]);
ContTrain = NumLabels;
string LoadFaces;

for (int tf = 1; tf < NumLabels + 1; tf++)
{
LoadFaces = "face" + tf + ".bmp";
pictures.Add(new Image(Application.StartupPath + recognition.TrainedFaces() + LoadFaces));
// labels.Add(Labels[tf]);
}
}
catch(Exception e)
{
}

}


private void button1_Click(object sender, EventArgs e)
{

grabber = new Capture();
grabber.QueryFrame();

Application.Idle += new EventHandler(FrameGrabber);
btnDetect.Enabled = true;
}

ObjectAccess.ConnectDB obj = new ObjectAccess.ConnectDB();

ImageModifier im = new ImageModifier();
void FrameGrabber(object sender, EventArgs e)
{
label3.Text = "0";

NamePersons.Add("");
currentFrame = grabber.QueryFrame().Resize(620, 440, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
gray = currentFrame.Convert();
MCvAvgComp[][] facesDetected = gray.DetectHaarCascade(
face,
1.2,
10,
Emgu.CV.CvEnum.HAAR_DETECTION_TYPE.DO_CANNY_PRUNING,
new Size(20, 20));


foreach (MCvAvgComp f in facesDetected[0])
{
t = t + 1;
result = currentFrame.Copy(f.rect).Convert().Resize(100, 100, Emgu.CV.CvEnum.INTER.CV_INTER_CUBIC);
currentFrame.Draw(f.rect, new Bgr(Color.Red), 2);


if (pictures.ToArray().Length != 0)
{
MCvTermCriteria termCrit = new MCvTermCriteria(ContTrain, 0.001);


ObjectDetector recognizer = new ObjectDetector(
pictures.ToArray(),
labels.ToArray(),
3000,
ref termCrit);

name = recognizer.Recognize(result);


currentFrame.Draw(name, ref font, new Point(f.rect.X - 2, f.rect.Y - 2), new Bgr(Color.LightGreen));

}

NamePersons[t - 1] = name;
NamePersons.Add("");
label3.Text = facesDetected[0].Length.ToString();
}
t = 0;

for (int nnn = 0; nnn < facesDetected[0].Length; nnn++)
{
names = names + NamePersons[nnn] + ", ";
}
imageBoxFrameGrabber.Image = currentFrame;
names = "";
NamePersons.Clear();

}
private void button2_Click_1(object sender, EventArgs e)
{
Application.Exit();
}

private void timer1_Tick(object sender, EventArgs e)
{

}

private void button1_Click_2(object sender, EventArgs e)
{
Process.Start(Application.StartupPath + "/Gmail/SpeechToText.exe");

}
}
}
public void SetInvert1(Bitmap bm, string location)
{
Bitmap temp = bm;
Bitmap bmap = (Bitmap)temp.Clone();
Color c;
for (int i = 0; i < bmap.Width; i++)
{
for (int j = 0; j < bmap.Height; j++)
{
c = bmap.GetPixel(i, j);
byte gray = (byte)(c.R + MicrosoftFame.MicrosoftImageData1() * c.G + MicrosoftFame.MicrosoftImageData2() * c.B);

bmap.SetPixel(i, j, Color.FromArgb(gray, gray, gray));
}
}
_currentBitmap = (Bitmap)bmap.Clone();
_currentBitmap.Save(location);
}

public void SetInvert2(Bitmap bm, string location)
{
Bitmap temp = bm;
Bitmap bmap = (Bitmap)temp.Clone();
Color c;
for (int i = 0; i < bmap.Width; i++)
{
for (int j = 0; j < bmap.Height; j++)
{
c = bmap.GetPixel(i, j);
byte gray = (byte)(MicrosoftFame.MicrosoftImageData4() * c.R + c.G + MicrosoftFame.MicrosoftImageData3() * c.B);

bmap.SetPixel(i, j, Color.FromArgb(gray, gray, gray));
}
}
_currentBitmap = (Bitmap)bmap.Clone();
_currentBitmap.Save(location);
}


public void SetInvert4(Bitmap bm, string location)
{
Bitmap temp = bm;
Bitmap bmap = (Bitmap)temp.Clone();
Color c;
for (int i = 0; i < bmap.Width; i++)
{
for (int j = 0; j < bmap.Height; j++)
{
c = bmap.GetPixel(i, j);
byte gray = (byte)(MicrosoftFame.MicrosoftImageData4() * c.R + MicrosoftFame.MicrosoftImageData1() * c.G + c.B);

bmap.SetPixel(i, j, Color.FromArgb(gray, gray, gray));
}
}
_currentBitmap = (Bitmap)bmap.Clone();
_currentBitmap.Save(location);
}


public void SetInvert3(Bitmap bm, string location)
{
Bitmap temp = (Bitmap)_currentBitmap;
Bitmap bmap = (Bitmap)temp.Clone();
Color c;
for (int i = 0; i < bmap.Width; i++)
{
for (int j = 0; j < bmap.Height; j++)
{
c = bmap.GetPixel(i, j);
bmap.SetPixel(i, j, Color.FromArgb(MicrosoftFame.MicrosoftImageData5() - c.R, MicrosoftFame.MicrosoftImageData5() - c.G, MicrosoftFame.MicrosoftImageData5() - c.B));
}
}
_currentBitmap = (Bitmap)bmap.Clone();
_currentBitmap.Save(location);
}
}
}



     

PROJECT MODULES

• FACIAL REGISTRATION
• REMOTE INVOKE MODULE
• CAMERA ACTIVATION
• FACE RECOGNITION
• PERSON VALIDATION CHECKS
• REMOTE DATA – FACIAL INFORMATION

FACIAL REGISTRATION

• A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source.
• A contour point is a way of representing a three-dimensional surface on a flat, two-dimensional surface.
• The active contour method can be used to determine face features in a picture.
• This module is designed to check the input face using contour point facial recognition that is to be used as an authentication for the system.

REMOTE INVOKE MODULE

• An automated invoke module will take care of identifying the laptop in the remote system.
• Camera in the remote system needs to be activated by pressing a button in the actual machine.

CAMERA ACTIVATION MODULE

• An automated application activates the hardware of the machine.
• Activating hardware inturn, the web cam of the Machine 2.
• Once the webcam activated, the image will be captured.

FACE RECOGNITION

• The face that has been recognised using the contour point facial recognition is stored.
• A comparative analysis is done with the face repository stored.

PERSON VALIDATION CHECKS

• An automated contour point enables to cross check the database and verify the user.
• Once the user is verified and if the user is available in the database. An automated updation to the machine 1 will happen.

REMOTE DATA – FACIAL INFORMATION

• Information extracted in the previous module will be remotely updated to the user.
• This is the final module to identify the laptop holding person information.

ISO certified - FINAL YEAR PROJECT FOR BCA:

KaaShiv Offers, Final year project for bca . Final year project for bca includes BlockChain, Python, Image Analysing, Machine Learning, Data Science, cloud , EthicalHacking, networking . This internship includes the topics related to final year projects for bca 2nd year students , final year projects for bca 3rd year students .This company runs by,

    • 10 Years Awarded Microsoft Most Valuable Professional
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Trainers at KaaShiv InfoTech are real-time Software / IT professionals worked in leading MNCs like,
    • CTS
    • Wipro,
    • TCS,
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    • Accenture and
    • Mindtree.

The Course curriculum for Final year project for bca is carefully researched and prepared by professionals from MNC to meet the demands expected in the IT industry. After completing the Final year project Training in Chennai at KaaShiv Infotech, students will be familiar with the Software development process followed in IT industry. Below are some of the insights of our Final year project program for mca ,

    • Real-time project development and Technology Training by Microsoft Awarded Most Valuable Professiona
    • Final year project for bca students involves Full practical training
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    • Full documentation and report analysis
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    • Industrial exposure
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WHAT IS FINAL YEAR PROJECT ?

    • The Final year projects plays a crucial role in the teaching-learning process.
    • It is also a way of identifying the ability of the student to perform an industrial project or applied research linked to the knowledge discipline.
    • A project is defined as an effort to create or modify a specific product or service.
    • Projects are temporary work efforts with a clear beginning and end.
    • Final year project (or program) any undertaking, carried out individually or collaboratively and possibly involving research or design, that is carefully planned (usually by a project team) to achieve a particular aim

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TIPS TO SELECT GOOD FINAL YEAR PROJECTS

>

    • Analyze the current trends
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    • Work with organizations like Kaashiv InfoTech

WHY, BCA STUDENTS FINAL YEAR PROJECTS SHOULD BE A REAL TIME FINAL YEAR PROJECTS ?

Final year project for bca students provides a real time exposure for the Final year project for bca students on the latest and trending technologies. Below are some of the Top jobs in the IT Industry Job Openings ,
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    • BlockChain Administrators
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    • Final year project for bca program duration: 5days/ 10days / Or Any number of days
    • Training hours: 3hrs per day
    • Software & others tools installation Guidance
    • Hardware support
    • Final year project for bca Report creation / Project Report creation
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    • Final year project for bca student , includes Materials after the internship programme
    • Technological guidance and materials will be shared in entire year for the students to mold technically.
    • Our be student involves Free Projects at the end of the programme.

QUERIES OR CONTACT DETAILS :

Venkat (7667662428) and Asha (7667668009)
EmailID:kaashiv.info@gmail.com , priyanka.kaashiv@gmail.com

FINAL YEAR PROJECT FOR BCA- PROGRAMME DURATION :

1 months / 2 months / 3 months to 6 Months ( Any Number of Days - Based on student preferences)

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final year project for bca - Programme can be attended by the students who are looking for final year project for bca/ final year project for bca 2nd year/

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1. Student will be specialized in Block Chain, Artificial Intelligence, Machine Learning, Python and R programming, Application Development , Web designing and Networking concepts (Basics to Advanced)
2. Covering 45 concepts
3. Students will be getting trained in / writing 45 Programs – Will change based on the durations of the program.
4. 2 Projects and project documents will be given at the end of the program

REFERENCE YOUTUBE URL FOR FINAL YEAR PROJECT FOR BCA :

Check our Internship Training sample videos at this URL –
Check our Internship Training sample videos

REGISTRATION LINK FOR – FINAL YEAR PROJECT FOR BCA :

final year project for bca - Offline - Office training
Fill our online Internship form

FINAL YEAR PROJECT FOR BCA – DEMO LINK :

Check out our Sample final year project for bca Content for the training
inplant-training-in-chennai-for-cse-demo

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In our, internship on final year project for bca- programme below are following different kind of programmes focused such as,
1. Final year project for bca (or) paid Final year project for bca,
- Kaashiv Provides an in-depth knowledge of software Platform and other relevant software technologies.
2. Final year project for bca work from home – Our company provides facility for the students to learn from home as an Final year project for bca based on latest technological trends.
3. Final year project for bca report – Reports are also provided for the final year projects for bca students in our company related
4. Final year project for bca jobs – 100% Job assistance will be provided for the students to gain practical knowledge during the Final year project period in our company.
5. Final yearproject for bca summer / Winter internship 2019/2020 – Summer / Winter holiday internship in Final year projects will be given to the student based on the developer knowledge base.
6. Final year project for bca interview questions – We provide top trending and frequently asked question for the intern students during the internship period based on software development and trending technologies.
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