Face Recognization

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A PROJECT REPORT ON FACE RECOGNIZATION.

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TABLE OF CONTENT

CHAPTER NO.

TITLE

ABSTRACT LIST OF TABLES LIST OF ABREVATIONS 1. INTODUCTION 1.1 Problem Definition System Environment 2. SYSTEM ANALYSIS 2.1 Existing System 2.1.1 Principle Component Analysis 2.1.2 Linear Discriminant Analysis (LDA) 2.2 Proposed System 2.3 System Requirement 2.4 System Analysis Method 2.5 Feasibility Study 2.5.1 Technical Analysis 2.5.2 Economic Analysis 2.5.3 Performance Analysis

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2.5.4 Efficiency Analysis 3.SYSTEM DESIGN

3.1 Project Module. 3.1.1 Read\Write Module. 3.1.2 Resizing Module. 3.1.3 Image Manipulation 3.1.4 Testing Module. 3.2 System Development4. IMPLEMENTATION

4.1 Implementation Details 4.2 Coding 4.2.1 Form Design 4.2.2 Input Design 4.2.3 Menu Design 4.2.4 Database Design 4.2.5 Code Design5. SYSTEM TESTING

5.1 Software Testing 5.2 Efficiency of Laplacian Algorithm 5.2.1. Experimental Result 5.2.2 Face Recognition Using Laplacianfaces 5.2.3 Yale Database

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5.2.4 PIE Database 5.2.5 MSRA Database

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CONCLUSION SNOPSHOT REFERENCES

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ABSTRACT

We propose an appearance-based face recognition method called the Laplacianface approach. By using Locality Preserving Projections (LPP), the face images are mapped into a face subspace for analysis. Different from Principal Component Analysis (PCA) and Linear Discriminate Analysis (LDA) which effectively see only the Euclidean structure of face space, LPP finds an embedding that preserves local information, and obtains a face subspace that best detects the essential face manifold structure. The Laplacianfaces are the optimal linear approximations to the eigen functions of the Laplace Beltrami operator on the face manifold. In this way, the unwanted variations resulting from changes in lighting, facial expression, and pose may be eliminated or reduced. Theoretical analysis shows that PCA, LDA, and LPP can obtained from different graph models. We compare the proposed Laplacianface approach with Eigen face and Fisher face methods on three different face data sets. Experimental results suggest that the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition.

LIST OF TABLES5

TABLE NO

TITLE

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Performance Comparison on the Yale Database

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Performance Comparison on the PIE Database

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Performance Comparison on the MSRA Database

LIST OF FIGURES

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FIGURE NO

CAPTION

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Designing Flow Diagram Laplacian Images Reduced Representation of Laplacian Images Eigen Value of LPP Images in Yale Database Original and Cropped Image Image in Yale Database. Error Rate Versus Dimensionality Reduction. Face Images in PIE Database Face Images in MISRA Database

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LIST OF ABBREVIATIONS

PCA LDA LLP FRR FAR

: : : : :

Principle Component Analysis Linear Discriminant Analysis Locality Preserving Projections False Rejection Rate False Acceptance Rate

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CHAPTER-1INTRODUCTION A smart environment is one that is able to identify people, interpret their actions, and react appropriately. Thus, one of the most important building blocks of smart environments is a person identification system. Face recognition devices are ideal for such systems, since they have recently become fast, cheap, unobtrusive, and, when combined with voice-recognition, are very robust against changes in the environment. Moreover, since humans primarily recognize each other by their faces and voices, they sense relaxed interact with an situation that does the same. Facial recognition system is built on CPU program that analyze images of human faces for the purpose of identifying them. The programs take a facial image, measure characteristics such as the distance between the eyes, the length of the nose, and the angle of the jaw, and generate a single file called a "template." Using templates, the software then compares that image with another image and produce a score that actions how like the images are to each other. Typical sources of images for use in facial recognition include video camera signals and preexisting photos such as those in driver's license databases. Facial identification system is computer-based refuge system that are able to automatically detect and identify human faces. These systems depend on a gratitude algorithm, such as eigenface or the hidden Markov model. The first step for a facial recognition system is to recognize human features and remove it for the rest of the scene. Next, the system measures nodal points on the face, such as the reserve linking the eye, the figure of the cheekbones and other distinguishable features. These nodal points are then compared to the nodal points computed from a folder of movies in order to locate a match. Obviously, such a system is incomplete base on the position of the face captured and the lighting conditions present. New technologies are currently in development to create three-dimensional models of a person's face based on a digital photograph in order to create more nodal points for comparison. However, such technology is inherently susceptible to error given that the computer is extrapolating a three-dimensional model from a twodimensional photograph. Principle module study is an eigenvector method calculated to model linear variation in high-dimensional data. PCA performs dimensionality reduction by projecting the

Original n-dimensional data onto the k