Ear Recognition Using Block-Based Principal Component Analysis and Decision Fusion

Citation:
Alaa Tharwat, Abdelhameed Ibrahim, A. E. Hassanien, and G. Schaefer, "Ear Recognition Using Block-Based Principal Component Analysis and Decision Fusion", 6th International Conference Pattern Recognition and Machine Intelligence (PReMI 2015:), Warsaw, Poland, 2 July, 2015.

Date Presented:

2 July

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In this paper, we propose a fast and accurate ear recognition system based on principal component analysis (PCA) and fusion at classification and feature levels. Conventional PCA suffers from time and space complexity when dealing with high-dimensional data sets. Our proposed algorithm divides a large image into smaller blocks, and then applies PCA on each block separately, followed by classification using a minimum distance classifier. While the recognition rates on small blocks are lower than that on the whole ear image, combining the outputs of the classifiers is shown to increase the recognition rate. Experimental results confirm that our proposed algorithm is fast and achieves recognition performance superior to that yielded when using whole ear images.