An Identification System Using Eye Detection Based On Wavelets And Neural Networks
This work addresses the need for reliable biometric identification in security applications, but it is incremental as it combines existing methods (wavelets and neural networks) for a specific task.
The paper tackled the problem of automatic human identification by developing an eye detection system using wavelets and neural networks, achieving 90% accuracy in classifying eye regions from face images under various conditions.
The randomness and uniqueness of human eye patterns is a major breakthrough in the search for quicker, easier and highly reliable forms of automatic human identification. It is being used extensively in security solutions. This includes access control to physical facilities, security systems and information databases, Suspect tracking, surveillance and intrusion detection and by various Intelligence agencies through out the world. We use the advantage of human eye uniqueness to identify people and approve its validity as a biometric. . Eye detection involves first extracting the eye from a digital face image, and then encoding the unique patterns of the eye in such a way that they can be compared with pre-registered eye patterns. The eye detection system consists of an automatic segmentation system that is based on the wavelet transform, and then the Wavelet analysis is used as a pre-processor for a back propagation neural network with conjugate gradient learning. The inputs to the neural network are the wavelet maxima neighborhood coefficients of face images at a particular scale. The output of the neural network is the classification of the input into an eye or non-eye region. An accuracy of 90% is observed for identifying test images under different conditions included in training stage.