ACKNOWLEDGEMENT

Firstly we would like to express our gratitude towards our guide- Dr. Varsha Turkar for providing us with help and assistance whenever we asked for it. She allowed us to experiment and encouraged creative thinking, thereby allowing us to grow and mature. She would always point out areas where we can improve ourselves. Her expertise and time was of immense importance to us and the project as a whole.Secondly, we would like to acknowledge our college, Thakur College of Science and Commerce, for providing us with the technical support as and when we required. Without these support systems our presentations would have not been as effective.Last but not the least, special thanks to our parents and all our class mates for being such a support, they have contributed greatly with their supportive dialogues to nish the project.Finally, as our project -2D Face Detection and Recognition is an amalgamation of knowledge acquired from various sources which garnered our creativity and ability to experiment, we would like to acknowledge Google search engine.

Thanking you... Khan SanaMital Motani

ABSTRACT

ABSTRACT2D Face detection and Recognition is based on identifying a human face in images regardless of size, position, and condition Simple features such as colors, motion, and texture are used for the face detection in early researches. However, these methods break down easily because of the complexity of the real world. Face detection proposed by Viola and Jones is most popular among the face detection approaches based on statistic methods. Algorithm introduced a method to accurately and rapidly detect faces within an image. This technique can be adapted to accurately detect facial features. By regionalizing the detection area, false positives are eliminated and the speed of detection is increased due to the reduction of the area examined. Face is also a complex multidimensional structure and needs good computing techniques for recognition. The approach treats face recognition as a two-dimensional recognition. In this scheme face recognition is done by Principal Component Analysis (PCA). The Eigen faces approach uses the PCA for recognition of the images. The system performs by projecting pre extracted face image onto a set of face space that represents significant variations among known face images. Face will be categorized as known or unknown face after matching with the present database. If the user is new to the face recognition system then his/her template will be stored in the database else matched against the templates stored in the database. The variable reducing theory of PCA accounts for the smaller face space than the training set of face.

TABLE OF CONTENTSSR NO.TitlesPG NO.

1Introduction8-10

1.1Problem Definition11-12

1.2Significance13-14

1.3Scope 15

1.4Literature Review16-17

1.5Existing System Overview18

1.6Proposed System19-23

1.7Image Database24

1.8Assumptions24

2System Requirements25-26

3Technology Used27-28

4Structured Approach For System Analysis And Design29-39

4.1Iterative Process Model30-31

4.2UML Diagram32

4.2.1Use-Case Diagram33-34

4.2.2Activity Diagram35

4.2.3Sequence Diagram36

4.3Flow Chart37-38

4.4Gantt Chart39

5Implementation Phase40

5.1Screen Shots41-48

6Testing 49-52

7Cost Estimation Analysis53-56

8Conclusion57-59

8.1Applications58

8.2Future Enhancement59

9Bibliography 60

9.1References61

LIST OF FIGURESFig 1.5: Basic working model of proposed system.Fig 4.1: Flow of Iterative Model.Fig 4.2.1: Use-Case DiagramFig 4.2.2: Activity DiagramFig 4.2.3: Sequence DiagramFig 4.3: Flow chart DiagramFig 4.4: Gantt chart.Fig 5.1: Shows the basic GUI for Face Detection and RecognitionFig 5.2: Captures the image from the folder Fig 5.3 Shows a person image for Face Detection that is been selected by userFig 5.4: Detecting the face using the algorithm mentioned above Fig 5.5: The user Face is been Detected successfullyFig 5.6: The Detected Face is been matched with the Database for Recognition processFig: 5.7: Redirect the user to Home screen for more process

LIST OF TABLES1. Dynamic View2. 2D Face Detection and Recognition Condition Test (6.1)3. 2D Face Detection and Recognition Condition Test (6.2)4. 2D Face Detection and Recognition Condition Test (6.3)5. 2D Face Detection and Recognition Condition Test (6.4)

ABBREVIATIONS USEDPCA- Principal Component AnalysisFR -Face recognition

INTRODUCTION

1 INTRODUCTION

The propose a system for 2D Face Detection and Recognition that describes a systematic approach by taking 2D image as input & then by applying an algorithm for 2D Face Detection & Recognition.Face detection is a preprocessing step for face Recognition algorithms. It is the localization of face/faces in an image or image sequence. Face detection is a challenging computer vision problem because of lighting conditions, a high degree of variability in size, shape, background, color, etc. The existing methods for face detection can be divided into image based methods and feature based methods. We have used an intermediate system, using a algorithm to train a images which is capable of processing image rapidly while having high detection rates. The family of simple classifiers contains simple rectangular wavelets which are reminiscent of the Haar basis. Their simplicity and a new image representation called Integral Image allow a very quick computing of these Haar-like features. For this, classifiers with an increasingly complexity are combined sequentially. This improves both, the detection speed and the detection efficiency. The detection of faces in input images is preceded using a scanning window at different scales which permits to detect faces of every size. The human face is not a unique rigid object. There are billions of different faces and each of them can assume a variety of deformations. Inter-personal variations can be due to race, identity, or genetics while intra-personal variations can be due to deformations, expression, aging, facial hair, cosmetics and facial paraphernalia.The detected face varies in rotation, brightness, size, and etc. in different images even for the same person. After the face detection, face recognition process has been performed. Its main objective is to detect a face which helps in increasing the face recognition rate. Face recognition (FR) is the preferred mode of identity recognition by humans: It is natural, robust and un-intrusive. The images are then compared with images in Database. If Image is found then appropriate image is displayed.The goal is to implement the system for a particular face and distinguish it from a large number of stored faces with some real-time variations as well. The Eigenface approach uses Principal Component Analysis (PCA) algorithm for the recognition of the images. It gives us efficient way to find the lower dimensional space.

The algorithm is based on an Eigenface approach which represents a PCA method in which a small set of significant features are used to describe the variation between face images. Experimental results for different numbers of Eigenface are shown to verify the viability of the proposed method. The output of the detection and recognition system has to be accurate. A recognition system has to associate an identity for each face it comes across by matching it to a large database of individuals. Simultaneously, the system must be robust to typical image-acquisition problems such as noise, video-camera distortion and image resolution

1.1 PROBLEM DEFINITION

Face DetectionFace detection is an essential application of visual object detection and it is one of the main components of face analysis and understanding with face localization and face recognition. Automatic face detection is a complex problem which consists in detecting one or many faces in an image. Faces are non-rigid objects. Face appearance may vary between two different persons but also between two photographs of the same person, depending on the lightning conditions, the emotional state of the subject and pose. That is why so many methods have been developed during last few years. Facial expression is also directly affected by a person's facial expression.Faces may be partially occluded by other objects. Orientation of face appearance directly vary for different rotations about the camera's optical axisImaging conditions such as lighting (spectra, source distribution and intensity) and camera characteristics (sensor response, gain control, lenses), resolution

Face RecognitionA facial recognition system is a computer application for automatically identifying or verifying a person from a digital image. One of the ways to do this is by comparing selected facial features from the image and a facial database.Therefore, face recognition is applied in many important areas such as security systems, identification of criminals, and verification of credit cards and so on. Unfortunately, many face features make development of facial recognition systems difficult. This problem is solved by the method called Principal Component Analysis. PCA is a projection technique that finds a set of projection vectors designed such that the projected data retains the most information about the original data. The most representative vectors are eigenvectors corresponding to highest eigenvalues of the covariance matrix. This method reduces the dimensionality of data space by projecting data from M-dimensional space to P-dimensional space, where P