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Journal ArticleDOI

A survey on ear biometrics

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TLDR
An up-to-date review of the existing literature revealing the current state-of-art in ear detection and recognition is provided, offering insights into some unsolved ear recognition problems as well as ear databases available for researchers.
Abstract
Recognizing people by their ear has recently received significant attention in the literature. Several reasons account for this trend: first, ear recognition does not suffer from some problems associated with other non-contact biometrics, such as face recognition; second, it is the most promising candidate for combination with the face in the context of multi-pose face recognition; and third, the ear can be used for human recognition in surveillance videos where the face may be occluded completely or in part. Further, the ear appears to degrade little with age. Even though current ear detection and recognition systems have reached a certain level of maturity, their success is limited to controlled indoor conditions. In addition to variation in illumination, other open research problems include hair occlusion, earprint forensics, ear symmetry, ear classification, and ear individuality.This article provides a detailed survey of research conducted in ear detection and recognition. It provides an up-to-date review of the existing literature revealing the current state-of-art for not only those who are working in this area but also for those who might exploit this new approach. Furthermore, it offers insights into some unsolved ear recognition problems as well as ear databases available for researchers.

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Citations
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50 years of biometric research

TL;DR: Unlocking the full potential of biometrics through inter-disciplinary research in the above areas will not only lead to widespread adoption of this promising technology, but will also result in wider user acceptance and societal impact.
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Discriminant Correlation Analysis: Real-Time Feature Level Fusion for Multimodal Biometric Recognition

TL;DR: In this paper, a discriminant correlation analysis (DCA) is proposed for feature fusion by maximizing the pairwise correlations across the two feature sets and eliminating the between-class correlations and restricting the correlations to be within the classes.
Journal ArticleDOI

Ear biometrics: a survey of detection, feature extraction and recognition methods

TL;DR: This survey categorise and summarise approaches to ear detection and recognition in 2D and 3D images, and provides an outlook over possible future research in the field of ear recognition, in the context of smart surveillance and forensic image analysis, which the authors consider to be the most important application ofEar recognition characteristic in the near future.
Journal ArticleDOI

Ear recognition: More than a survey

TL;DR: An overview of the field of automatic ear recognition (from 2D images) and focuses specifically on the most recent, descriptor-based methods proposed in this area, as well as introducing a new, fully unconstrained dataset of ear images gathered from the web and a toolbox implementing several state-of-the-art techniques.
References
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Journal ArticleDOI

Robust Real-Time Face Detection

TL;DR: In this paper, a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates is described. But the detection performance is limited to 15 frames per second.
Proceedings ArticleDOI

Robust real-time face detection

TL;DR: A new image representation called the “Integral Image” is introduced which allows the features used by the detector to be computed very quickly and a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions.
Journal ArticleDOI

Robust Face Recognition via Sparse Representation

TL;DR: This work considers the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise, and proposes a general classification algorithm for (image-based) object recognition based on a sparse representation computed by C1-minimization.
Journal ArticleDOI

The FERET evaluation methodology for face-recognition algorithms

TL;DR: Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems.
Journal ArticleDOI

An introduction to biometric recognition

TL;DR: A brief overview of the field of biometrics is given and some of its advantages, disadvantages, strengths, limitations, and related privacy concerns are summarized.
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