Non-Decimated Wavelet Based Multi-Band Ear Recognition Using Principal Component Analysis

Zarachoff, Matthew; Sheikh-Akbari, Akbar; Monekosso, Dorothy

doi:10.1109/access.2021.3139684

Cited by 13 publications

(4 citation statements)

References 47 publications

(83 reference statements)

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“…• MATTHEW et al [18] presented a novel method for ear recognition, called the 2D Wavelet based Multi-Band PCA (2D-WMBPCA) technique. Inspired by PCA-based methods for multispectral and hyperspectral images, the proposed method involves a 2D non-decimated wavelet transform applied to the input image, resulting in wavelet subbands.…”

Section: Handcrafted Featuresmentioning

confidence: 99%

Human Recognition Using Ear Features: A Review

Rajab,

Hashim

2023

J. Al-Qadisiyah Comp. Sci. Math.

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Over the past few years, ear biometrics has attracted a lot of attention. It is a trusted biometric for the identification and recognition of humans due to its consistent shape and rich texture variation. The ear presents an attractive solution since it is visible, ear images are easily captured, and the ear structure remains relatively stable over time. In this paper, a comprehensive review of prior research was conducted to establish the efficacy of utilizing ear features for individual identification through the employment of both manually-crafted features and deep-learning approaches. The objective of this model is to present the accuracy rate of person identification systems based on either manually-crafted features such as DCT, DWT, DFT, PCA, LBP, SURF, SIFT, etc., or deep learning techniques such as CNN, DNN, Alex Net CNN, VGG-16, SVM, Squeeze Net, Google Net, MobileNetV2, etc. The effort will make it easier for researchers, especially those who are new to the field, to have a brief understanding of the trend of employing deep learning in a trustworthy biometric for the identification and recognition of human identification.

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Section: Handcrafted Featuresmentioning

confidence: 99%

Human Recognition Using Ear Features: A Review

Rajab,

Hashim

2023

J. Al-Qadisiyah Comp. Sci. Math.

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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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“…Application of the multi-band image processing for ear recognition on non-decimated wavelet subbands of ear images utilizing Principal Component Analysis (PCA) shows the effectiveness of multi-band image processing in recognition. In [18], the authors showed that the intersection of the number of feature graphs and the Eigenvector energy defines the optimum number of bands for recognition, where increasing the number of multi-band images changes the distribution of the energy across image Eigenvectors, and consolidates most of the image Eigenvectors' energy into a smaller number of Eigenvectors. The result of this was an increased accuracy in recognition.…”

Section: Introductionmentioning

confidence: 99%

Chainlet-Based Ear Recognition Using Image Multi-Banding and Support Vector Machine

2022

Self Cite

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This paper introduces the Chainlet-based Ear Recognition algorithm using Multi-Banding and Support Vector Machine (CERMB-SVM). The proposed technique splits the gray input image into several bands based on the intensity of its pixels, similar to a hyperspectral image. It performs Canny edge detection on each generated normalized band, extracting edges that correspond to the ear shape in each band. The generated binary edge maps are then combined, creating a single binary edge map. The resulting edge map is then divided into non-overlapping cells and the Freeman chain code for each group of connected edges within each cell is determined. A histogram of each group of contiguous four cells is computed, and the generated histograms are normalized and linked together to create a chainlet for the input image. The created chainlet histogram vectors of the images of the dataset are then utilized for the training and testing of a pairwise Support Vector Machine (SVM). Results obtained using the two benchmark ear image datasets demonstrate that the suggested CERMB-SVM method generates considerably higher performance in terms of accuracy than the principal component analysis based techniques. Furthermore, the proposed CERMB-SVM method yields greater performance in comparison to its anchor chainlet technique and state-of-the-art learning-based ear recognition techniques.

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Non-Decimated Wavelet Based Multi-Band Ear Recognition Using Principal Component Analysis

Cited by 13 publications

References 47 publications

Human Recognition Using Ear Features: A Review

Human Recognition Using Ear Features: A Review

Multi-band PCA based ear recognition technique

Chainlet-Based Ear Recognition Using Image Multi-Banding and Support Vector Machine

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