A human ear recognition method using nonlinear curvelet feature subspace

Basit, Abdul; Shoaib, Muhammad

doi:10.1080/00207160.2013.800194

Cited by 40 publications

(10 citation statements)

References 15 publications

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“…These algorithms allow for partial matching and are robust to partial occlusion because they only operate on keypoints, but global ear structure information is discarded. Other local techniques densely calculate the local descriptors of the entire image, such as wavelet [34], curvelet [35], Gabor filters [36], log-Gabor filters [37], local binary patterns [38], and histogram oriented gradients [39].…”

Section: A Ear Recognition Techniquesmentioning

confidence: 99%

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

2022

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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.

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Section: A Ear Recognition Techniquesmentioning

confidence: 99%

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

2022

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“…However, the performance of DMS-BSIF is lower than the feature used in Omara et al (2016) for IITD-1 database. As the ear consists of several geometric structures such as curvatures, the proposed approaches of Basit and Shoaib (2014) and Omara et al (2016) based on geometric measurements give interesting results in terms of accuracy.…”

Section: Experiments #3mentioning

confidence: 99%

“…Their dimensions are then reduced using the Laplacian eigenmaps method. Basit and Shoaib (2014) used the wrapping technique based on a fast discrete curvelet transform (FDCT) to extract the invariant feature scale and orientation vectors. Each feature vector is then computed from eight directions, containing also two coefficients, which are: the approximate curvelet coefficient and its second-coarsest level.…”

Section: Introductionmentioning

confidence: 99%

A novel discriminant multiscale representation for ear recognition

Doghmane

Boukrouche

Boubchir

2019

IJBM

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This paper proposes a novel representation for ear recognition. It introduces a new alternative of binarised statistical image features based on multiscale framework. The proposed representation allows capturing the image content at multiple resolutions. The recognition accuracy can be enhanced by the following steps. First, for a given ear image, a set of multiscale response images are derived from the bank of binarised statistical image features (B-BSIF) filter. Second, the obtained response images are summarised by concatenating their histograms, which are obtained at each scale. Finally, a discriminative ear image representation is build by projecting the above mentioned histograms into a linear discriminant analysis subspace. The proposed representation is applied on three public databases: IIT Delhi-1, IIT Delhi-2 and USTB. The obtained recognition accuracy confirms its performance than the recent existing methods.

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“…The results indicate that the LBP descriptor does not achieve good results with smaller scale sizes, and that level-3 of the spatial pyramid division provides the best recognition rates with all the descriptors. As mentioned in Section 5, the [32] one-dimensional quadrature filter Hamming distance 94.72 2D quadrature filter Hamming distance 96.08 Kumer [33] sparse representation of local grey-level orientations sparse representation 97.73 Mamta [34] traditional principal component analysis inner product classifier (IPC) 79 local principal independent components inner product classifier (IPC) 97.2 Basit [35] nonlinear Table 6 Summary of related and recent work on personal identification using two-dimensional palmprint images References Feature extraction Recognition rate…”

Section: Experiments #2mentioning

confidence: 99%

Multimodal biometric recognition using human ear and palmprint

Hezil

Boukrouche

2017

IET biom.

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Combining multiple human trait features is a proven and effective strategy for biometric-based personal identification. In this study, the authors investigate the fusion of two biometric modalities, i.e. ear and palmprint, at feature-level. Ear and palmprint patterns are characterised by a rich and stable structure, which provides a large amount of information to discriminate individuals. Local texture descriptors, namely local binary patterns, weber local descriptor, and binarised statistical image features, were used to extract the discriminant features for robust human identification. The authors' extensive experimental analysis based on the benchmark IIT Delhi-2 ear and IIT Delhi palmprint databases confirmed that the proposed multimodal biometric system is able to increase recognition rates compared with that produced by single-modal biometrics, attaining a recognition rate of 100%.

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A human ear recognition method using nonlinear curvelet feature subspace

Cited by 40 publications

References 15 publications

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

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

A novel discriminant multiscale representation for ear recognition

Multimodal biometric recognition using human ear and palmprint

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