Ear recognition based on weighted wavelet transform and DCT

Tian, Ying; Zhang, Debin; Baihuan, Zhang

doi:10.1109/ccdc.2014.6852957

Cited by 10 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• Tian et al [11] proposed a novel approach to ear recognition based on converting the pictures of the human ear to a two-dimensional discrete wavelet transform, which is then followed by a block discrete cosine transform on the wavelet transform's low-frequency and weighted high-frequency components. The extractions of the image's DCT coefficients as well as the creation of feature vectors are the outcomes of this operation.…”

Section: Handcrafted Featuresmentioning

confidence: 99%

Human Recognition Using Ear Features: A Review

Rajab,

Hashim

2023

J. Al-Qadisiyah Comp. Sci. Math.

View full text Add to dashboard Cite

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.

show abstract

Section: Handcrafted Featuresmentioning

confidence: 99%

Human Recognition Using Ear Features: A Review

Rajab,

Hashim

2023

J. Al-Qadisiyah Comp. Sci. Math.

View full text Add to dashboard Cite

show abstract

“…Kumar and Chan [70] adopted the sparse representation classification algorithm and applied it to local gray-level orientation features, while Galdamez et al [74] first computed SURF features from the images and then reduced the dimensionality using LDA. A combination of the wavelet transform and the discrete cosine transform (DCT) was presented in [76] and a hybrid method based on LBPs and the Haar transform was introduced in [77]. Various combinations of local descriptors and subspace projection techniques were assessed in [41].…”

Section: Referencementioning

confidence: 99%

Ear recognition: More than a survey

2017

View full text Add to dashboard Cite

Automatic identity recognition from ear images represents an active field of research within the biometric community. The ability to capture ear images from a distance and in a covert manner makes the technology an appealing choice for surveillance and security applications as well as other application domains. Significant contributions have been made in the field over recent years, but open research problems still remain and hinder a wider (commercial) deployment of the technology. This paper presents an overview of the field of automatic ear recognition (from 2D images) and focuses specifically on the most recent, descriptorbased methods proposed in this area. Open challenges are discussed and potential research directions are outlined with the goal of providing the reader with a point of reference for issues worth examining in the future. In addition to a comprehensive review on ear recognition technology, the paper also introduces a new, fully unconstrained dataset of ear images gathered from the web and a toolbox implementing several stateof-the-art techniques for ear recognition. The dataset and toolbox are meant to address some of the open issues in the field and are made publicly available to the research community. Index Terms-biometry, dataset, in-the-wild, unconstrained image, descriptor-based method, open-source toolbox, ear recognition• Survey: We present a comprehensive survey on ear recognition, which is meant to provide researchers in this field with a recent and up-to-date overview of the state-of-technology. We introduce a taxonomy of the existing 2D ear recognition approaches, discuss the characteristics of the technology and review the existing state-of-the-art. Most importantly, we elaborate on the open problems and challenges faced by the technology. • Dataset: We make a new dataset of ear images available to the research community. The dataset, named Annotated Web Ears (AWE), contains images collected from the web and is to the best of our knowledge the first dataset for ear recognition gathered "in the wild". The images of the AWE dataset contain a

show abstract

“…Within the context of global feature extraction, techniques such as principal component analysis as proposed by Querencias-Uceta, Ríos-Sánchez and Sánchez-Ávila [9], and a technique based on a combination of the wavelet and discrete cosine transforms as proposed by Ying, Debin and Baihuan [10] have been investigated.…”

Section: Introductionmentioning

confidence: 99%

“…A number of feature matching protocols for quantifying the difference between two ears have been proposed, which include the utilisation of the Euclidean distance ( [4], [7]), and the Hamming distance [4], as well as a minimum distance classifier [6], a k-nearest neighbour (KNN) classifier [8] and a nearest neighbour classifier that is based on a weighted distance [10].…”

Section: Introductionmentioning

confidence: 99%

Ear-based biometric authentication through the detection of prominent contours

Kohlakala

Coetzer

2021

SAIEE Afr. Res. J.

View full text Add to dashboard Cite

In this paper novel semi-automated and fully automated ear-based biometric authentication systems are proposed. The region of interest (ROI) is manually specified and automatically detected within the context of the semi-automated and fully automated systems, respectively. The automatic detection of the ROI is facilitated by a convolutional neural network (CNN) and morphological postprocessing. The CNN classifies sub-images of the ear in question as either foreground (part of the ear shell) or background (homogeneous skin, hair or jewellery). Prominent contours associated with the folds of the ear shell are detected within the ROI. The discrete Radon transform (DRT) is subsequently applied to the resulting binary contour image for the purpose of feature extraction. Feature matching is achieved by implementing an Euclidean distance measure. A ranking verifier is constructed for the purpose of authentication. In this study experiments are conducted on two independent ear databases, that is (1) the Mathematical Analysis of Images (AMI) ear database and (2) the Indian Institute of Technology (IIT) Delhi ear database. The results are encouraging. Within the context of the proposed semi-automated system, accuracies of 99.20% and 96.06% are reported for the AMI and IIT Delhi ear databases respectively.

show abstract

Ear recognition based on weighted wavelet transform and DCT

Cited by 10 publications

References 9 publications

Human Recognition Using Ear Features: A Review

Human Recognition Using Ear Features: A Review

Ear recognition: More than a survey

Ear-based biometric authentication through the detection of prominent contours

Contact Info

Product

Resources

About