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.

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please go through the following threads for more information on face recognition using neural network .

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Please send me the full project report of FACE RECOGNITION USING NEURAL NETWORKS

Face Recognition using Neural Networks
The face recognition is performed by using the main features of the face to identify the given face. It is a major biometric method. here, a neural based algorithm is used to detect the frontal views of the faces. A Principal component analysis (PCA) is used to reduce the dimensionality of the face. Back propagation Neural Network (BPNN) is used in the detection of the face. For the performance metrics, about 200 faces are taken from the yale database and the Acceptance ratio and Execution time are calculated for the database for comparison. The results show that the neural based face recognition algorithms are very efficient with an acceptance ratio of 90%. Usually a face recognition system identifies a human face from database images automatically. Nowadays the digital cameras implement a face detection feature. all the possible appearance variations caused by facial features, occlusions, change in illumination etc. must be taken into account. a Neural and PCA based algorithm for efficient and robust face recognition is discussed in this article.
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