DeepIrisNet: Deep iris representation with applications in iris recognition and cross-sensor iris recognition

Gangwar, Abhishek; Joshi, Akanksha

doi:10.1109/icip.2016.7532769

Cited by 190 publications

(114 citation statements)

References 18 publications

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“…The first deep iris recognition approach was presented in 2016 [9]. Here, the authors, Gangwar and Joshi, presented two architectures, collectively called DeepIrisNet.…”

Section: Related Workmentioning

confidence: 99%

“…With the recent success of deep learning in different areas of computer vision, research in iris recognition is also starting to look at deep learning methodologies. A number of solution has been presented recently in the literature for iris segmentation, e.g., [2]- [8], and recognition [9]- [12]. However, these typically do not approach iris recognition in an endto-end manner, but either segment the iris using supervised deep learning models and then represent the iris texture using established iris encoding techniques, e.g., [13], or first segment the iris from the input image using standard iris-segmentation approaches and then process the unwrapped iris texture using deep learning models, e.g., [9].…”

Section: Introductionmentioning

confidence: 99%

“…A number of solution has been presented recently in the literature for iris segmentation, e.g., [2]- [8], and recognition [9]- [12]. However, these typically do not approach iris recognition in an endto-end manner, but either segment the iris using supervised deep learning models and then represent the iris texture using established iris encoding techniques, e.g., [13], or first segment the iris from the input image using standard iris-segmentation approaches and then process the unwrapped iris texture using deep learning models, e.g., [9]. While the interaction of deep segmentation models and standard iris representation techniques has been studied recently [13], [14], the influence of segmentation techniques on the performance of deep learning models has, to the best of our knowledge, not been investigated so far for the task of iris recognition.…”

Section: Introductionmentioning

confidence: 99%

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Influence of segmentation on deep iris recognition performance

Štepec¹,

Peer²

2019

2019 7th International Workshop on Biometrics and Forensics (IWBF)

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Despite the rise of deep learning in numerous areas of computer vision and image processing, iris recognition has not benefited considerably from these trends so far. Most of the existing research on deep iris recognition is focused on new models for generating discriminative and robust iris representations and relies on methodologies akin to traditional iris recognition pipelines. Hence, the proposed models do not approach iris recognition in an end-to-end manner, but rather use standard heuristic iris segmentation (and unwrapping) techniques to produce normalized inputs for the deep learning models. However, because deep learning is able to model very complex data distributions and nonlinear data changes, an obvious question arises. How important is the use of traditional segmentation methods in a deep learning setting? To answer this question, we present in this paper an empirical analysis of the impact of iris segmentation on the performance of deep learning models using a simple two stage pipeline consisting of a segmentation and a recognition step. We evaluate how the accuracy of segmentation influences recognition performance but also examine if segmentation is needed at all. We use the CASIA Thousand and SBVPI datasets for the experiments and report several interesting findings.

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“…The first deep iris recognition approach was presented in 2016 [9]. Here, the authors, Gangwar and Joshi, presented two architectures, collectively called DeepIrisNet.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Influence of segmentation on deep iris recognition performance

Štepec¹,

Peer²

2019

2019 7th International Workshop on Biometrics and Forensics (IWBF)

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show abstract

“…Convolutional neural networks have been used as classifiers, but they are also efficient tools to extract and represent discriminative features from the raw data at different levels of abstraction. Compared to hand-crafted features, employing CNN as domain feature extractor demonstrated to be more promising when facing different modalities such as face [26], [27], [28], [29], iris [30] and fingerprint [31], [32]. However, the effects of the fusion at different levels of feature resolution and abstraction and joint optimization of the architecture are not investigated for multimodal biometric identification.…”

Section: Introductionmentioning

confidence: 99%

Multi-Level Feature Abstraction from Convolutional Neural Networks for Multimodal Biometric Identification

Soleymani

Dabouei

Kazemi

et al. 2018

2018 24th International Conference on Pattern Recognition (ICPR)

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In this paper, we propose a deep multimodal fusion network to fuse multiple modalities (face, iris, and fingerprint) for person identification. The proposed deep multimodal fusion algorithm consists of multiple streams of modality-specific Convolutional Neural Networks (CNNs), which are jointly optimized at multiple feature abstraction levels. Multiple features are extracted at several different convolutional layers from each modality-specific CNN for joint feature fusion, optimization, and classification. Features extracted at different convolutional layers of a modality-specific CNN represent the input at several different levels of abstract representations. We demonstrate that an efficient multimodal classification can be accomplished with a significant reduction in the number of network parameters by exploiting these multi-level abstract representations extracted from all the modality-specific CNNs. We demonstrate an increase in multimodal person identification performance by utilizing the proposed multi-level feature abstract representations in our multimodal fusion, rather than using only the features from the last layer of each modality-specific CNNs. We show that our deep multi-modal CNNs with multimodal fusion at several different feature level abstraction can significantly outperform the unimodal representation accuracy. We also demonstrate that the joint optimization of all the modality-specific CNNs excels the score and decision level fusions of independently optimized CNNs.

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“…Gangwar & Joshi [10] also developed a deep learning application for iris recognition in images obtained from different sensors, called DeepIrisNet. In their study, a CNN was used to extract features and representations of iris images.…”

Section: B Deep Learning In Iris Recognitionmentioning

confidence: 99%

The Impact of Preprocessing on Deep Representations for Iris Recognition on Unconstrained Environments

Zanlorensi

Luz

Laroca

et al. 2018

2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)

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The use of iris as a biometric trait is widely used because of its high level of distinction and uniqueness. Nowadays, one of the major research challenges relies on the recognition of iris images obtained in visible spectrum under unconstrained environments. In this scenario, the acquired iris are affected by capture distance, rotation, blur, motion blur, low contrast and specular reflection, creating noises that disturb the iris recognition systems. Besides delineating the iris region, usually preprocessing techniques such as normalization and segmentation of noisy iris images are employed to minimize these problems. But these techniques inevitably run into some errors. In this context, we propose the use of deep representations, more specifically, architectures based on VGG and ResNet-50 networks, for dealing with the images using (and not) iris segmentation and normalization. We use transfer learning from the face domain and also propose a specific data augmentation technique for iris images. Our results show that the approach using non-normalized and only circle-delimited iris images reaches a new state of the art in the official protocol of the NICE.II competition, a subset of the UBIRIS database, one of the most challenging databases on unconstrained environments, reporting an average Equal Error Rate (EER) of 13.98% which represents an absolute reduction of about 5%.

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DeepIrisNet: Deep iris representation with applications in iris recognition and cross-sensor iris recognition

Cited by 190 publications

References 18 publications

Influence of segmentation on deep iris recognition performance

Influence of segmentation on deep iris recognition performance

Multi-Level Feature Abstraction from Convolutional Neural Networks for Multimodal Biometric Identification

The Impact of Preprocessing on Deep Representations for Iris Recognition on Unconstrained Environments

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