kites - FACE RECOGNITION USING CONVOLUTION FILTERS AND...

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FACE RECOGNITION USING CONVOLUTION FILTERS AND NEURAL NETWORKS (Paper presentation) SUBMITED BY N.N.V.K.Visweswara rao Y.Sathish III/IV C.S.E III/ IV C.S.E Vissu.5a9 @gmail.com [email protected]                                                                                                                                                            Department  of                      Computer Science and  Engineering Technology
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            Seetharampuram, Narsapur – 534 280
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Abstract--
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What is Face Recognition? Human Face Recognition has become a potential method of biometric authentication because of its most non-intrusive and user-friendly nature. Instead of requiring people to place their hand on a reader or precisely position their eye in front of a scanner, face recognition systems unobtrusively take pictures of people's faces as they enter a defined area. There is no intrusion or delay, and in most cases the subjects are entirely unaware of the process. They do not feel "under surveillance" or that their privacy has been invaded. Automatic face recognition poses various challenges due to: (a) inherent variability of face due to age, gender and race; (b) different facial expressions and orientations of same person’s face; and (c) images containing faces have high degree of variability in size, texture, background and illumination. The proposed model uses a two pass method in which the input image is first processed using conventional image processing filters to enhance it and then the edges are detected in the image using convolution mask; which is further followed by face recognition using neural network. This helps the model to achieve more accuracy and the whole process to be more efficient than simply applying neural network model for face recognition. Hard to fool: Face recognition is also very difficult to fool. It works by comparing facial landmarks - specific proportions and angles of defined facial features - which cannot easily be concealed by beards, eyeglasses or makeup. The ideal solution All of this makes face recognition ideal for high traffic areas open to the general public, such as: - Airports and railway stations - Casinos - Cash points - Stadiums -public transportation - Financial institutions - Government offices - Businesses of all kinds Keywords — Neural network, face recognition, classification, convolution filters. 1 Introduction:
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Face Recognition has been recently the most favored subject due to its most non-intrusive nature and wide range of applications. Its applications are incriminvestigations, security (biometric authentication), etc. But the whole process depends on the
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kites - FACE RECOGNITION USING CONVOLUTION FILTERS AND...

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