An efficient ear recognition system using DWT &amp; BLPOC

Arunachalam, Muthukumar; Alagarsamy, Santham Bharathy

doi:10.1109/icicct.2017.7975188

Cited by 8 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• Muthukumar et al [19] proposed an ear recognition algorithm that employs a band-limited phase-only correlation (BLPOC) based local block matching technique for improved efficiency. The discrete wavelet transform (DWT) is used to extract the phase information over the frequency domain of the ear image, decomposing it into a set of basis functions.…”

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

“…Arunachalam et al introduced an ear recognition system using band limited phase only correlation (BLPOC) algorithm for feature extraction. The proposed BLPOC algorithm deals with a local image distortion in local block matching.…”

Section: Emerging Biometric Technologiesmentioning

confidence: 99%

State of the art and perspectives on traditional and emerging biometrics: A survey

Traoré

Alshahrani

Obaidat

2018

Security and Privacy

View full text Add to dashboard Cite

The last three decades have seen a shift and impressive progress in the biometric technologies landscape. Several major real‐world applications of biometrics have been released and are currently being used around the world. At the same time, several new biometrics modalities have emerged and have started making an impact in securing sensitive data, information, and systems. We present in this paper a survey of traditional and emerging biometrics by emphasizing their connection to human body parts, behavior, and cognition. A review of the state of the art in the key technologies is carried out along with a discussion of major applications currently available in the real world.

show abstract

“…1 shows some well-known biometrics traits used for person authentication. Researchers have reported various approaches based on physiological characteristics such as face [12,23]; fingerprint [14,40]; iris [25,27]; palmprint [18,24]; knuckle print [15,16,43]; ear [3,9]; and behavioral characteristics such as voice [28]; gait [26] and signature [35] for authentication. However, there is still scope of improving the overall performance of the aforementioned authentication methods.…”

Section: Introductionmentioning

confidence: 99%

UESegNet: Context Aware Unconstrained ROI Segmentation Networks for Ear Biometric

Kamboj,

Rani,

Nigam

et al. 2020

Preprint

View full text Add to dashboard Cite

Biometric-based personal authentication systems have seen a strong demand mainly due to the increasing concern in various privacy and security applications. Although the use of each biometric trait is problem dependent, the human ear has been found to have enough discriminating characteristics to allow its use as a strong biometric measure.To locate an ear in a 2D side face image is a challenging task, numerous existing approaches have achieved significant performance, but the majority of studies are based on the constrained environment. However, ear biometrics possess a great level of difficulties in the unconstrained environment, where pose, scale, occlusion, illuminations, background clutter etc. varies to a great extent. To address the problem of ear localization in the wild, we have proposed two high-performance region of interest (ROI) segmentation models UESegNet-1 and UESegNet-2, which are fundamentally based on deep convolutional neural networks and primarily uses contextual information to localize ear in the unconstrained environment. Additionally, we have applied state-of-the-art deep learning models viz; FRCNN (Faster Region Proposal Network) and SSD (Single Shot MultiBox Detecor) for ear localization task. To test the model's generalization, they are evaluated on six different benchmark datasets viz; IITD, IITK, USTB-DB3, UND-E, UND-J2 and

show abstract

An efficient ear recognition system using DWT & BLPOC

Cited by 8 publications

References 10 publications

Human Recognition Using Ear Features: A Review

Human Recognition Using Ear Features: A Review

State of the art and perspectives on traditional and emerging biometrics: A survey

UESegNet: Context Aware Unconstrained ROI Segmentation Networks for Ear Biometric

Contact Info

Product

Resources

About