This Electronics Seminar Topic deals with the following:

Today, Internet rules the world. The Internet is used to access the complete facility of transferring the information, besides maintaining the secrecy of the document. Since the network is considered to be insecure, the encryption and authentication are used to protect the data while it is being transmitted. The security is insufficient when the codes for encryption and decryption are revealed. There comes the necessity of increasing the security through face recognition using neural network. Though it is costlier, it provides the high advantage of tight security. This paper deals with the recognition of images using neural networks. It is used in identifying particular people in real time or allows access to a group of people and denies access to the rest.

The system combines local image sampling, the self-organizing map neural network, and a convolutional neural network. The self-organizing map provides the quantization of image samples into a topological space where inputs that are nearby in the original space are also in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample. All these features are implemented using MATLAB v 6.5. The convolutional neural network provides for the partial invariance to translational, rotation, scale, and deformation. Hence it is analyzed that by implementing face recognition in security systems, the business transaction via Internet can be improved.

INTRODUCTION
The paper presents a hybrid neural network solution, which compares favorably with other methods and recognizes a person within a large database of  faces. These neuralsystems typically return a list of most likely people in the database. Often only one image is available per person.First a database is created, which contains images of various persons. In the nextstage, the available images are trained and stored in the database. Finally it classifies the authorized person’s face, which is used in security monitoring system. Faces represent complex, multidimensional, meaningful visual stimuli and developing a computational model for face recognition is difficult.

Face has certain distinguishable landmarks that are the peaks and valleys that sum up the different facial features. There are about 80 peaks and valleys on a human face. The following are a few of the peaks and valleys that are measured by the software:

• Distance between eyes
• Width of nose
• Depth of eye sockets
• Cheekbones
• Jaw line
• Chin

These peaks and valleys are measured to give a numerical code, a string of numbers, which represents the face in a database. This code is called a face print. Here the detecting, capturing and storing faces by the system is dealt with. Below is the basic process that could be used by the system to capture and compare images:

1. DETECTION
When the system is attached to a video surveillance system, the Recognition software searches the field of view of a video camera for faces. Once the face is in view, it is detected within a fraction of a second. A multi-scale algorithm, which is a program that provides a set of instructions to accomplish a specific task, is used to search for faces in low resolution. . The system switches to a high-resolution search only after a head-like shape is detected.

2. ALIGNMENT
Once a face is detected, the head’s position, size and pose is the first thing that is determined. A face needs to be turned at least 35 degrees toward the camera for the system to register it.

3. NORMALIZATION
The image of the head is scaled and rotated so that it can be Registered and mapped into an appropriate size and pose. Normalization is performed irrespective of the head’s location and distance from the camera. Light does not have any impact on the normalization process.

4. REPRESENTATION
Translation of facial data into unique code is done by the system. This Coding process supports easier comparison of the newly acquired facial data to stored facial data.

5. MATCHING
The newly acquired facial data is compared to the stored data and (ideally) linked to at least one stored facial representation. Briefly, the use of local image sampling and a technique for partial lighting invariance, a self-organizing map (SOM) for projection of the image sample representation into a quantized lower dimensional space, the Karhunen Loève (KL) transform for comparison with the self-organizing map, a convolutional network (CN) for partial translation and deformation invariance, and a multi-layer perceptron (MLP) for comparison with the convolutional network is explored.

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