Unconstrained Ear Recognition Using Deep Scattering Wavelet Network

Birajadar, Parmeshwar; Haria, Meet; Sangodkar, S.G.; Gadre, Vikram M.

doi:10.1109/ibssc47189.2019.8973055

Cited by 6 publications

(1 citation statement)

References 17 publications

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“…Researchers have explored this topic extensively over the last two decades, investigating techniques for extracting features from ear images and their subsequent comparison [10,28]. Successful feature extraction techniques in ear recognition and other biometrics include Principal Component Analysis-(PCA) [29][30][31][32]37], wavelet-based [5,13,18,25], Support Vector Machine (SVM) [4,26,27] and neural network-based and other [1,2,7,9,11,15,22,24,27,33,39,40] methods. Amongst these techniques, PCA has been used for both feature extraction in the form of eigenvectors and dimensionality reduction.…”

Section: Introductionmentioning

confidence: 99%

Multi-band PCA based ear recognition technique

Zarachoff

Sheikh-Akbari

Monekosso

2022

Multimed Tools Appl

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Principal Component Analysis (PCA) has been successfully applied to many applications, including ear recognition. This paper presents a Two Dimensional Multi-Band PCA (2D-MBPCA) method, inspired by PCA based techniques for multispectral and hyperspectral images, which have demonstrated significantly higher performance to that of standard PCA. The proposed method divides the input image into a number of images based on the intensity of the pixels. Three different methods are used to calculate the pixel intensity boundaries, called: equal size, histogram, and greedy hill climbing based techniques. Conventional PCA is then applied on the resulting images to extract their eigenvectors, which are used as features. The optimal number of bands was determined using the intersection of number of features and total eigenvector energy. Experimental results on two benchmark ear image datasets demonstrate that the proposed 2D-MBPCA technique significantly outperforms single image PCA by up to 56.41% and the eigenfaces technique by up to 29.62% with respect to matching accuracy on images from two benchmark datasets. Furthermore, it gives very competitive results to those of learning based techniques at a fraction of their computational cost and without a need for training.

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