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 usingneural 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 identifyingparticular 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 ofimage samples into a topological space where inputs that are nearby in the original space arealso in the output space, thereby providing dimensionality reduction and invariance to minorchanges in the image sample. All these features are implemented using MATLAB v 6.5. Theconvolutional neural network provides for the partial invariance to translational, rotation,scale, and deformation. Hence it is analyzed that by implementing face recognition insecurity systems, the business transaction via Internet can be improved.
NOTE : The Matlab Codes will be shown at the time of presentation.
The paper presents a hybrid neural network solution, which compares favorably withother 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 isavailable 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 theauthorized person’s face, which is used in security monitoring system. Faces representcomplex, multidimensional, meaningful visual stimuli and developing a computational modelfor face recognition is difficult.Face has certain distinguishable landmarks that are the peaks and valleys that sum upthe different facial features. There are about 80 peaks and valleys on a human face. Thefollowing are a few of the peaks and valleys that are measured by the software:
Distance between eyes
Width of nose
Depth of eye sockets
These peaks and valleys are measured to give a numerical code, a string of numbers, whichrepresents 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 couldbe used by the system to capture and compare images:
When the system is attached to a video surveillance system, the Recognition softwaresearches the field of view of a video camera for faces. Once the face is in view, it is detectedwithin a fraction of a second. A multi-scale algorithm, which is a program that provides a setof 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.
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 toregister it.
The image of the head is scaled and rotated so that it can be Registered and mappedinto an appropriate size and pose. Normalization is performed irrespective of the head'slocation and distance from the camera. Light does not have any impact on the normalizationprocess.
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.
The newly acquired facial data is compared to the stored data and (ideally) linked toat least one stored facial representation. Briefly, the use of local image sampling and atechnique for partial lighting invariance, a self-organizing map (SOM) for projection of theimage 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) forcomparison with the convolutional network is explored.