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Journal ArticleDOI

Ocular biometrics

TLDR
A path forward is proposed to advance the research on ocular recognition by improving the sensing technology, heterogeneous recognition for addressing interoperability, utilizing advanced machine learning algorithms for better representation and classification, and developing algorithms for ocular Recognition at a distance.
About
This article is published in Information Fusion.The article was published on 2015-11-01. It has received 138 citations till now. The article focuses on the topics: Iris recognition & Biometrics.

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Citations
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Journal ArticleDOI

Iris Recognition With Off-the-Shelf CNN Features: A Deep Learning Perspective

TL;DR: It is shown that the off-the-shelf CNN features, while originally trained for classifying generic objects, are also extremely good at representing iris images, effectively extracting discriminative visual features and achieving promising recognition results on two iris datasets: ND-CrossSensor-2013 and CASIA-Iris-Thousand.
Journal ArticleDOI

Long range iris recognition

TL;DR: This paper reviews the state-of-the-art design and implementation of iris-recognition-at-a-distance (IAAD) systems and presents a complete solution to the design problem of an IAAD system, from both hardware and algorithmic perspectives.
Journal ArticleDOI

Tumor co-segmentation in PET/CT using multi-modality fully convolutional neural network.

TL;DR: This study proposed a novel multi-modality segmentation method based on a 3D fully convolutional neural network (FCN), which is capable of taking account of both PET and CT information simultaneously for tumor segmentation and achieved significantly performance gain over CNN-based methods and traditional methods.
Journal ArticleDOI

Overview of the combination of biometric matchers

TL;DR: Several systems and architectures related to the combination of biometric systems, both unimodal and multimodal, are overviews, classifying them according to a given taxonomy, and a case study for the experimental evaluation of methods for biometric fusion at score level is presented.
Journal ArticleDOI

A survey on periocular biometrics research

TL;DR: This work is expected to provide an insight of the most relevant issues in periocular biometrics, giving a comprehensive coverage of the existing literature and current state of the art.
References
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Journal ArticleDOI

High confidence visual recognition of persons by a test of statistical independence

TL;DR: A method for rapid visual recognition of personal identity is described, based on the failure of a statistical test of independence, which implies a theoretical "cross-over" error rate of one in 131000 when a decision criterion is adopted that would equalize the false accept and false reject error rates.
Journal ArticleDOI

Multi-PIE

TL;DR: This paper introduces the database, describes the recording procedure, and presents results from baseline experiments using PCA and LDA classifiers to highlight similarities and differences between PIE and Multi-PIE.
Proceedings Article

Saliency Based on Information Maximization

TL;DR: A model of bottom-up overt attention is proposed based on the principle of maximizing information sampled from a scene and is achieved in a neural circuit, which is demonstrated as having close ties with the circuitry existent in die primate visual cortex.
Proceedings Article

Multi-PIE

TL;DR: The CMU Multi-PIE database as mentioned in this paper contains 337 subjects, imaged under 15 view points and 19 illumination conditions in up to four recording sessions, with a limited number of subjects, a single recording session and only few expressions captured.
Book

Handbook of Multibiometrics

TL;DR: Details multi-modal biometrics and its exceptional utility for increasingly reliable human recognition systems and the substantial advantages of multimodal systems over conventional identification methods.
Related Papers (5)
Trending Questions (1)
What are the limitations of ocular biometrics for deception detection?

The provided paper does not specifically mention the limitations of ocular biometrics for deception detection.