DeepIris: Learning pairwise filter bank for heterogeneous iris verification

Liu, Nianfeng; Zhang, Man; Li, Haiqing; Sun, Zhenan; Tan, Tieniu

doi:10.1016/j.patrec.2015.09.016

Cited by 139 publications

(93 citation statements)

References 8 publications

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“…To the best of our knowledge, there is only one work [8] in which a convolutional neural network (CNN) extracts the features and performs the iris verification at the same time. However, the method is designed to verify a match only between a pair of heterogeneous irises, i.e.…”

Section: Related Workmentioning

confidence: 99%

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Iris Verification with Convolutional Neural Network and Unit-Circle Layer

Špetlík

Razumenić²

2019

Lecture Notes in Computer Science

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We propose a novel convolutional neural network to verify a match between two images of the human iris. The network is trained end-to-end and validated on three publicly available datasets yielding state-of-the-art results against four baseline methods. The network performs better by a 10% margin to the state-of-the-art method on the CASIA.v4 dataset. In the network, we use a novel layer whose output is interpreted as a normalized response in the complex plane. We show that the layer improves the performance of the model up to 15% on previously-unseen data. IntroductionIris verification is a biometric technique used for human identification. Given a pair of images of human irises, the task is to decide whether the irises match. Iris verification is applied widely, e.g., in border control, citizen authentication, or in forensics [21].Common iris verification pipeline has three steps -iris detection, feature extraction, and matching (see Fig. 2, interested reader is referred, e.g., to [5]). First, an iris is found and normalized. Second, the normalized iris is typically convolved with Gabor filters and converted into a "bitcode", i.e. a matrix of binary numbers. Third, two bitcodes are compared. The bitcodes match if their Hamming distance is smaller than a given threshold.Feature extraction and matching are highly data-dependent in a common iris verification pipeline and therefore require parameter-tuning. Since the task is not convex, an exhaustive search for parameters is performed. In this paper, we propose a method which replaces the feature extraction and matching part of the iris verification pipeline with a single fully convolutional neural network and a single learning rule -the backward propagation of errors or backpropagation. The network is trained end-to-end using the binary cross-entropy loss function. The input of the network is a pair of normalized irises, the output is a single number which is interpreted as a posterior probability of a match (see Fig. 1).So far, convolutional neural networks were used in iris verification for better feature encoding. To encode the features, standard blocks of convolutions, max-pooling, and batch normalization layers were used. We introduce a novel "Complex-response layer" that replaces the feature extraction step in a common iris verification pipeline and is learned optimally by backpropagation.The contributions of this paper are the following: (i) we propose a novel method of iris verification that replaces feature extraction and matching steps of a commonly used iris verification pipeline. We replace it with a single convolutional neural network (IrisMatch-CNN) trained end-to-end that is robust to changes in the iris image acquisition setup, (ii) we introduce a novel "Complex-response layer" whose output is interpreted as a normalized filter response in the complex plane, (iii) we evaluate the method on three public datasets against four methods achieving state-of-the-art results. * Work performed during an internship at Microsoft Development Center Serbia d.o.o.

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Section: Related Workmentioning

confidence: 99%

“…Authors of [8] design a CNN to verify the relationship between two heterogeneous iris images. A "pairwise filter" layer is introduced to extract features from a pair of normalized irises from different sources.…”

Section: Related Workmentioning

confidence: 99%

Iris Verification with Convolutional Neural Network and Unit-Circle Layer

Špetlík

Razumenić²

2019

Lecture Notes in Computer Science

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“…Machine learning techniques have also been applied to iris recognition [Marsico et al 2016]. Very recently, deep learning was used to tackle the problem of heterogeneous iris verification that typically arises in large-scale identification applications, where different sensors are likely to be used [Liu et al 2015].…”

Section: A:15mentioning

confidence: 99%

Biometric Recognition in Automated Border Control

et al. 2016

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The increasing demand for traveler clearance at international border crossing points (BCPs) has motivated research for finding more efficient solutions. Automated border control (ABC) is emerging as a solution to enhance the convenience of travelers, the throughput of BCPs, and national security. This is the first comprehensive survey on the biometric techniques and systems that enable automatic identity verification in ABC. We survey the biometric literature relevant to identity verification and summarize the best practices and biometric techniques applicable to ABC, relying on a real experience collected in the field. Furthermore, we select some of the major biometric issues raised and highlight the open research areas.

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“…Current large scale deployments of iris recognition systems in UAE and India are considered successful. Also, significant research on less constrained iris recognition have been conducted and the results are encouraging [9,10,15,16,18,19,13].…”

Section: Introductionmentioning

confidence: 99%