Abstract
We present a novel approach to ear recognition that utilizes ring-projection method for reducing the dimensions of two-dimensional image into one-dimensional information, that consists of the summation of all pixels that lie on the boundary of a circle with radius ‘r’ and center at the centroid of the image. As a 2-D image is transformed into a 1-D signal, so less memory is required and it is faster than existing 2-D descriptors in the recognition process. Also, ring-projection technique is rotation-invariant. The 1-D information, obtained in the ring-projection method, is normalized so as to make a new wavelet which is named as self-adaptive wavelet. Features are extracted using this wavelet by the process of decomposition. Neural Network based classifiers such as Back Propagation Neural Network (BPNN) and Probabilistic Neural Network (PNN) are used to obtain the recognition rate. A survey of various other techniques has also been discussed in this paper.
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Bhardwaj, J., Sharma, R. (2018). Ear Recognition Using Self-adaptive Wavelet with Neural Network Classifier. In: Satapathy, S., Bhateja, V., Raju, K., Janakiramaiah, B. (eds) Data Engineering and Intelligent Computing. Advances in Intelligent Systems and Computing, vol 542 . Springer, Singapore. https://doi.org/10.1007/978-981-10-3223-3_5
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DOI: https://doi.org/10.1007/978-981-10-3223-3_5
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