Human ear recognition using SIFT features

Anwar, Asmaa Sabet; Ghany, Kareem Kamal A.; Elmahdy, Hesham N.

doi:10.1109/icocs.2015.7483254

Cited by 13 publications

(7 citation statements)

References 18 publications

(18 reference statements)

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“…• Asmaa et al [16] proposed a novel algorithm that is dependent on (SIFT) features to recognize ear images. It involves extracting SIFT features from the ear image and creating an augmented vector of those characteristics for matches.…”

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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“…Anwar et al. [19] proposed an ear recognition method based on scale‐invariant feature transform (SIFT) features. First, in the preprocessing phase, images were converted to the greyscale.…”

Section: Related Work and Contributionmentioning

confidence: 99%

Corresponding keypoint constrained sparse representation three‐dimensional ear recognition via one sample per person

Zhu

Zhang

2022

IET Biometrics

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When only one sample per person (OSPP) is registered in the gallery, it is difficult for ear recognition methods to sufficiently and effectively reduce the search range of the matching features, thus resulting in low computational efficiency and mismatch problems. A 3D ear biometric system using OSPP is proposed to solve this problem. By categorising ear images by shape and establishing the corresponding relationship between keypoints from ear images and regions (regional cluster) on the directional proposals that can be arranged to roughly face the ear image, the corresponding keypoints are obtained. Then, ear recognition is performed by combining corresponding keypoints and a multikeypoint descriptor sparse representation classification method. The experimental results conducted on the University of Notre Dame Collection J2 dataset yielded a rank-1 recognition rate of 98.84%; furthermore, the time for one identification operation shared by each gallery subject was 0.047 ms.

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“…Due to the shortcomings of conventional methods of identification, it is more practicable to employ biometrics to identify individuals. It is possible to recognize persons automatically depending on their physiological features [1] with the use of biometrics recognition. An example of a physiological biometric is hand geometry, fingerprint, handprints, ear, eye, eyeball, and face identification.…”

Section: Introductionmentioning

confidence: 99%

Human ear identification system using shape and structural features based on SIFT and ANN classifier

Sathisha¹,

Shivaprasad²,

Madhu³

et al. 2022

ijhs

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People have become more interested in biometrics recognition as technology has advanced. Biometrics is the study of automatic systems for distinguishing people related to physical or behavioural features. Methods for finding favourable biometric traits have acquired more attention in recent years. The ear is among the most consistent biometric traits because it does not alter with aging or emotion. The human ear is an excellent source of data for passive personal authentication. The ear looks to be an excellent potential solution because it is accessible, images are easily obtained, and the ear structure doesn't change over the years. The ear meets a biometric criterion (universality, distinctiveness, durability, and collectability). The biometric field is working hard to develop automated individual identification using ear images. Several face recognition approaches fail in this present COVID-19 outbreak due to the mask-wearing situation. The human ear is attractive data for passive personal authentication because it does not need the individual's involvement to recognize the ear. In this research, we suggested an automatic ear identification system using Scale Invariant Feature Transform (SIFT) and an Artificial Neural Network (ANN).

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Human ear recognition using SIFT features

Cited by 13 publications

References 18 publications

Human Recognition Using Ear Features: A Review

Human Recognition Using Ear Features: A Review

Corresponding keypoint constrained sparse representation three‐dimensional ear recognition via one sample per person

Human ear identification system using shape and structural features based on SIFT and ANN classifier

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