Abstract
Principal Component Analysis (PCA) has been successfully applied to many applications, including ear recognition. This paper presents a 2D Wavelet based Multi-Band Principal Component Analysis (2D-WMBPCA) ear recognition method, inspired by PCA based techniques for multispectral and hyperspectral images. The proposed 2D-WMBPCA method performs a 2D non-decimated wavelet transform on the input image, dividing it into its wavelet subbands. Each resulting subband is then divided into a number of frames based on its coefficient’s values. The multi frame generation boundaries are calculated using either equal size or greedy hill climbing techniques. Conventional PCA is applied on each subband’s resulting frames, yielding its eigenvectors, which are used for matching. The intersection of the energy of the eigenvectors and the total number of features for each subband shows the number of bands which yield the highest matching performance. Experimental results on the images of two benchmark ear datasets, called IITD II and USTB I, demonstrated that the proposed 2D-WMBPCA technique significantly outperforms Single Image PCA by up to 56.79% and the eigenfaces technique by up to 20.37% with respect to matching accuracy. Furthermore, the proposed technique achieves very competitive results to those of learning based techniques at a fraction of their computational time and without needing to be trained.
Highlights
E AR recognition, a field within biometrics, concerns itself with the use of images of the ears to identify individuals
The proposed 2D-WMBPCA method using the two boundary selection algorithms described in Section IV, Single Image Principal Component Analysis (PCA), and the eigenfaces technique were applied to the images of both datasets
The Rank-1 accuracy for 2D-WMBPCA on the Institute of Technology Delhi II (IITD II) and University of Science and Technology Beijing I (USTB I) datasets increased by 4.36% and 20.37% when compared to eigenfaces, respectively
Summary
E AR recognition, a field within biometrics, concerns itself with the use of images of the ears to identify individuals. Successful feature extraction techniques in ear recognition include Principal Component Analysis (PCA) [2], [4]–[7], wavelet based [8], and neural network based methods [9]–[11]. The authors previously introduced a single image multi-band PCA based technique for ear recognition in [13], inspired by hyperspectral PCA based techniques [14] such as Segmented PCA [15] and Folded PCA [16], which solely use the extracted principal components as features. To the authors’ knowledge, no similar techniques that utilize wavelets have been reported in the literature This has inspired the authors to propose a single image, PCA based method for ear recognition, called 2D Wavelet based MultiBand PCA (2D-WMBPCA), which was originally introduced in EUSIPCO in [17]. The rest of the paper is organized as follows: Section II gives an overview of current techniques for ear recognition and hyperspectral PCA algorithms, Section III introduces the proposed 2D-WMBPCA technique, Section IV illustrates the multiple frame generation techniques, Section V describes the benchmark datasets used, Section VI discusses the experimental results, and Section VII concludes the paper
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