Abstract:

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 feel comfortable interacting with an environment that does the same.

Facial recognition systems are built on computer programs 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 create a unique file called a “template.” Using templates, the software then compares that image with another image and produces a score that measures how similar the images are to each other. Typical sources of images for use in facial recognition include video camera signals and pre-existing photos such as those in driver’s license databases.

Facial recognition systems are computer-based security systems that are able to automatically detect and identify human faces. These systems depend on a recognition algorithm, such as eigenface or the hidden Markov model. The first step for a facial recognition system is to recognize a human face and extract it for the rest of the scene. Next, the system measures nodal points on the face, such as the distance between the eyes, the shape of the cheekbones and other distinguishable features.

These nodal points are then compared to the nodal points computed from a database of pictures in order to find a match. Obviously, such a system is limited based on the angle 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 two-dimensional photograph.

Principle Component Analysis is an eigenvector method designed to model linear variation in high-dimensional data. PCA performs dimensionality reduction by projecting the original n-dimensional data onto the k << n -dimensional linear subspace spanned by the leading eigenvectors of the data’s covariance matrix. Its goal is to find a set of mutually orthogonal basis functions that capture the directions of maximum variance in the data and for which the coefficients are pair wise decorrelated. For linearly embedded manifolds, PCA is guaranteed to discover the dimensionality of the manifold and produces a compact representation.

Existing System:

   Face Identification systems are computer-based security systems that are able to automatically detect and identify human faces. These systems depend on a recognition algorithm. Principal Component Analysis (PCA) is a statistical method under the broad title of factor analysis. The purpose of PCA is to reduce the large dimensionality of the data space (observed variables) to the smaller intrinsic dimensionality of feature space (independent variables), which are needed to describe the data economically. This is the case when there is a strong correlation between observed variables. The jobs which PCA can do are prediction, redundancy removal, feature extraction, data compression, etc. Because PCA is a known powerful technique which can do something in the linear domain, applications having linear models are suitable, such as signal processing, image processing, system and control theory, communications, etc.

The main idea of using PCA for Face Identificationis to express the large 1-D vector of pixels constructed from 2-D face image into the compact principal components of the feature space. This is called eigenspace projection. Eigenspace is calculated by identifying the eigenvectors of the covariance matrix derived from a set of fingerprint images (vectors).

But the most of the algorithm considers some what global data patterns while recognition process. This will not yield accurate recognition system.

• Less accurate
• Does not deal with manifold structure
• It doest not deal with biometric characteristics.

   2.2 Proposed System :

PCA and LDA aim to preserve the global structure. However, in many real-world applications, the local structure is more important. In this section, we describe Locality Preserving Projection (LPP), a new algorithm for learning a locality preserving subspace. The complete derivation and theoretical justifications of LPP can be traced back to. LPP seeks to preserve the intrinsic geometry of the data and local structure. The objective function of LPP is as follows:

LPP is a general method for manifold learning. It is obtained by finding the optimal linear approximations to the eigenfunctions of the Laplace Betrami operator on the

manifold. Therefore, though it is still a linear technique, it seems to recover important aspects of the intrinsic nonlinear manifold structure by preserving local structure. Based on LPP, we describe our Laplacianfaces method for

face representation in a locality preserving subspace. In the face analysis and recognition problem, one is confronted with the difficulty that the matrix XDXT is sometimes singular. This stems from the fact that sometimes the number of images in the training set ðnÞ is much smaller than thenumberof pixels in eachimageðmÞ. Insuch a case, the rank ofXDXT is at most n, whileXDXT is anm _ mmatrix, which implies that XDXT is singular. To overcome the complication of a singular XDXT , we first project the image set to a PCA subspace so that the resulting matrix XDXT is nonsingular. Another consideration of using PCA as preprocessing

is for noise reduction. This method, we call Laplacianfaces, can learn an optimal subspace for face representation and recognition. The algorithmic procedure of Laplacianfaces is formally stated below:

1. PCA projection. We project the image set fxig into the PCA subspace by throwing away the smallest principal components. In our experiments, we kept 98 percent information in the sense of reconstruction error. For the sake of simplicity, we still use x to denote the images in the PCA subspace in the following steps. We denote by WPCA the transformation matrix of PCA.

2. Constructing the nearest-neighbour graph. Let G denote a graph with n nodes. The ith node corresponds to the face image xi. We put an edge between nodes i and j if xi and xj are “close,” i.e., xj is among k nearest neighbours of xi, or xi is among k nearest neighbours of xj. The constructed nearest neighbour graph is an approximation of the local manifold structure. Note that here we do not use the “-neighbourhood to construct the graph. This is simply because it is often difficult to choose the optimal ” in the real-world applications, while k nearest-neighbour graph can be constructed more stably. The disadvantage is that the k nearest-neighbour search will increase the computational complexity of our algorithm. When the computational complexity is a major concern, one can switch to the “-neighbourhood.

SOFTWARE REQUIREMENTS:
• Web Technologies : HTML, CSS, JS. JSP
• Programming Language : Java
• Database Connectivity : JDBC
• Backend Database : MySQL
• Operating System : Windows 08/10
HARDWARE REQUIREMENTS:
• Pentium processor : Core I3
• RAM Capacity : 2GB
• Hard Disk : 250GB
• Monitor : 15’’ Color Monitor