An experimental study of deep convolutional features for iris recognition

Minaee, Shervin; Abdolrashidiy, Amirali; Wang, Yao

doi:10.1109/spmb.2016.7846859

Cited by 106 publications

(58 citation statements)

References 35 publications

(35 reference statements)

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“…2. Concept cognition based on deep learning neural network [47], compared with deep learning architecture cognitive model for a small number of samples;…”

Section: Existing Methods Comparison Experimentsmentioning

confidence: 99%

Unsteady State Lightweight Iris Certification Based on Multi-Algorithm Parallel Integration

Liu

Zhu

et al. 2019

Algorithms

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Aimed at the one-to-one certification problem of unsteady state iris at different shooting times, a multi-algorithm parallel integration general model structure is proposed in this paper. The iris in the lightweight constrained state affected by defocusing, deflection, and illumination is taken as the research object, the existing algorithms are combined into the model structure effectively, and a one-to-one certification algorithm for lightweight constrained state unsteady iris was designed based on multi-algorithm integration and maximum trusted decision. In this algorithm, a sufficient number of iris internal feature points from the unstable state texture were extracted as effective iris information through the image processing layer composed of various filtering processing algorithms, thereby eliminating defocused interference. In the feature recognition layer, iris deflection interference was excluded by the improved methods of Gabor and Hamming and Haar and BP for the stable features extracted by the image processing layer, and two certification results were obtained by means of parallel recognition. The correct number of certifications for an algorithm under a certain lighting condition were counted. The method with the most correct number was set as the maximum trusted method under this lighting condition, and the results of the maximum trusted method were taken as the final decision, thereby eliminating the effect of illumination. Experiments using the JLU and CASIA iris libraries under the prerequisites in this paper show that the correct recognition rate of the algorithm can reach a high level of 98% or more, indicating that the algorithm can effectively improve the accuracy of the one-to-one certification of lightweight constrained state unsteady iris. Compared with the latest architecture algorithms, such as CNN and deep learning, the proposed algorithm is more suitable for the prerequisites presented in this paper, which has good environmental inclusiveness and can better improve existing traditional algorithms’ effectiveness through the design of a parallel integration model structure.

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“…2. Concept cognition based on deep learning neural network [47], compared with deep learning architecture cognitive model for a small number of samples;…”

Section: Existing Methods Comparison Experimentsmentioning

confidence: 99%

Unsteady State Lightweight Iris Certification Based on Multi-Algorithm Parallel Integration

Liu

Zhu

et al. 2019

Algorithms

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“…1) NIR Iris recognition Minaee et al [19] studied the effectiveness of features resulting from deep learning architectures, that feed support vector machines (SVMs) working in the multi-class one-against-all mode. Authors observe that even this classical processing chain outperforms the former generation of hand-crafted feature based approaches.…”

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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“…In this paper, we have investigated the application of deep features extracted from VGG-Net for iris recognition. [12] Iris recognition systems are increasingly deployed for large-scale applications such as national ID programs which continue to acquire millions of iris images to establish identity among billions. However with the availability of variety of iris sensors that are deployed for the iris imaging under different illumination/environment, significant performance degradation is expected while matching such iris images acquired under two different domains (either sensorspecific or wavelength-specific).…”

Section: Literature Surveymentioning

confidence: 99%

Gender Classification From The Iris Code Used For Recognition

KMS¹,

Shanmugan²

2017

IJMSR

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Abstract-In this paper, we present a new method for gender classification based on features of the iris texture selected by mutual information to improve gender classification of face images. The performance of the proposed approach has been investigated through extensive experiments performed on public database. We also showed improved results by fusion of texture features with best features selected independently from the left and right irises based on selection of features using rank feature selection method. The classification task has been achieved by using Support Vector Machine (SVM). We compared our method with existing gender classification methods based on texture feature with classifier being the same as SVM.

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An experimental study of deep convolutional features for iris recognition

Cited by 106 publications

References 35 publications

Unsteady State Lightweight Iris Certification Based on Multi-Algorithm Parallel Integration

Unsteady State Lightweight Iris Certification Based on Multi-Algorithm Parallel Integration

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

Gender Classification From The Iris Code Used For Recognition

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