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

Nguyen, Kien; Fookes, Clinton; Ross, Arun; Sridharan, Sridha

doi:10.1109/access.2017.2784352

Cited by 278 publications

(199 citation statements)

References 37 publications

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“…One reason of such limited research is the lack of big amounts of training data, as required by deep learning methods. Inspired by the work of Nguyen et al in iris [9], this paper leverages the power of existing pre-trained architectures which have proven to be successful in very large recognition tasks. This eliminates the necessity of designing and training new CNNs for the Authors thank the Swedish Research Council (VR), the Sweden's innovation agency (VINNOVA), and the Swedish Knowledge Foundation (CAISR program and SIDUS-AIR project).…”

Section: Introductionmentioning

confidence: 99%

Periocular Recognition Using CNN Features Off-the-Shelf

Hernandez‐Diaz

Alonso‐Fernandez

Bigün

2018

2018 International Conference of the Biometrics Special Interest Group (BIOSIG)

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Periocular refers to the region around the eye, including sclera, eyelids, lashes, brows and skin. With a surprisingly high discrimination ability, it is the ocular modality requiring the least constrained acquisition. Here, we apply existing pre-trained architectures, proposed in the context of the ImageNet Large Scale Visual Recognition Challenge, to the task of periocular recognition. These have proven to be very successful for many other computer vision tasks apart from the detection and classification tasks for which they were designed. Experiments are done with a database of periocular images captured with a digital camera. We demonstrate that these offthe-shelf CNN features can effectively recognize individuals based on periocular images, despite being trained to classify generic objects. Compared against reference periocular features, they show an EER reduction of up to ∼40%, with the fusion of CNN and traditional features providing additional improvements.

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Section: Introductionmentioning

confidence: 99%

Periocular Recognition Using CNN Features Off-the-Shelf

Hernandez‐Diaz

Alonso‐Fernandez

Bigün

2018

2018 International Conference of the Biometrics Special Interest Group (BIOSIG)

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“…Authors argue that scores from both algorithms are complementary, which maximises the benefits of fusion with respect to the best standalone classifier. Nguyen et al [20] used the responses of the CNN's fully connected layers as feature descriptors. Five well known models (AlexNet, VGG, Inception, ResNet and DenseNet) were fine-tuned and fed a SVM used for multi-class discrimination (one-against-all mode), having authors reported state-of-theart performance.…”

Section: Related Workmentioning

confidence: 99%

A Reminiscence of “<italic>Mastermind</italic>”: Iris/Periocular Biometrics by “<italic>In-Set</italic>” CNN Iterative Analysis

Proença

Neves

2019

IEEE Trans.Inform.Forensic Secur.

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Convolutional neural networks (CNNs) have emerged as the most popular classification models in biometrics research. Under the discriminative paradigm of pattern recognition, CNNs are used typically in one of two ways: 1) verification mode ("are samples from the same person?"), where pairs of images are provided to the network to distinguish between genuine and impostor instances; and 2) identification mode ("whom is this sample from?"), where appropriate feature representations that map images to identities are found. This paper postulates a novel mode for using CNNs in biometric identification, by learning models that answer to the question "is the query's identity among this set?". The insight is a reminiscence of the classical Mastermind game: by iteratively analysing the network responses when multiple random samples of k gallery elements are compared to the query, we obtain weakly correlated matching scores that-altogether-provide solid cues to infer the most likely identity. In this setting, identification is regarded as a variable selection and regularization problem, with sparse linear regression techniques being used to infer the matching probability with respect to each gallery identity. As main strength, this strategy is highly robust to outlier matching scores, which are known to be a primary error source in biometric recognition. Our experiments were carried out in full versions of two well known irises near-infrared (CASIA-IrisV4-Thousand) and periocular visible wavelength (UBIRIS.v2) datasets, and confirm that recognition performance can be solidly boosted-up by the proposed algorithm, when compared to the traditional working modes of CNNs in biometrics.

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“…Nguyen et al [20] further showed that the CNN features originally trained for classifying generic objects are also extremely good for the iris recognition.…”

Section: Related Workmentioning

confidence: 99%

Classification of Genetically Identical Left and Right Irises Using a Convolutional Neural Network

Fang

Zhou

et al. 2019

Electronics

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As one of the most reliable biometric identification techniques, iris recognition has focused on the differences in iris textures without considering the similarities. In this work, we investigate the correlation between the left and right irises of an individual using a VGG16 convolutional neural network. Experimental results with two independent iris datasets show that a remarkably high classification accuracy of larger than 94% can be achieved when identifying if two irises (left and right) are from the same or different individuals. This exciting finding suggests that the similarities between genetically identical irises that are indistinguishable using traditional Daugman’s approaches can be detected by deep learning. We expect this work will shed light on further studies on the correlation between irises and/or other biometric identifiers of genetically identical or related individuals, which would find potential applications in criminal investigations.

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

Cited by 278 publications

References 37 publications

Periocular Recognition Using CNN Features Off-the-Shelf

Periocular Recognition Using CNN Features Off-the-Shelf

A Reminiscence of “<italic>Mastermind</italic>”: Iris/Periocular Biometrics by “<italic>In-Set</italic>” CNN Iterative Analysis

Classification of Genetically Identical Left and Right Irises Using a Convolutional Neural Network

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