Iris recognition using scattering transform and textural features

Minaee, Shervin; Abdolrashidi, AmirAli; Wang, Yao

doi:10.1109/dsp-spe.2015.7369524

Cited by 59 publications

(50 citation statements)

References 27 publications

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“…Table 1 provides a comparison of the performance of the proposed scheme and those of other recent algorithms on IIT database. The scattering transform scheme [10] also uses a multi-layer representation and achieves a very high accuracy rate. By using the deep features, we were able to achieve the highest accuracy rate on this dataset.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…Recognition Rate Haar Wavelet [6] 96.6% Log Gabor Filter by Kumar [6] 97.2% Fusion [6] 97.4% Elastic Graph Matching [7] 98% Texture+Scattering Features [10] 99.2% Proposed scheme 99.4%…”

Section: Methodsmentioning

confidence: 99%

“…Pillai [9] proposed a unified framework based on random projections and sparse representations to achieve robust and accurate iris matching. In a more recent work [10], Minaee proposed an algorithm based on textural and scattering transform features, which achieved a high accuracy rate compared to the previous schemes. This approach also extracts features from a multi-layer representation, but with predefined filters.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An experimental study of deep convolutional features for iris recognition

Minaee

Abdolrashidiy

Wang

2016

2016 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

Self Cite

107

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Iris is one of the popular biometrics that is widely used for identity authentication. Different features have been used to perform iris recognition in the past. Most of them are based on hand-crafted features designed by biometrics experts. Due to tremendous success of deep learning in computer vision problems, there has been a lot of interest in applying features learned by convolutional neural networks on general image recognition to other tasks such as segmentation, face recognition, and object detection. In this paper, we have investigated the application of deep features extracted from VGG-Net for iris recognition. The proposed scheme has been tested on two well-known iris databases, and has shown promising results with the best accuracy rate of 99.4%, which outperforms the previous best result.

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Section: Experimental Results and Analysismentioning

confidence: 99%

“…Recognition Rate Haar Wavelet [6] 96.6% Log Gabor Filter by Kumar [6] 97.2% Fusion [6] 97.4% Elastic Graph Matching [7] 98% Texture+Scattering Features [10] 99.2% Proposed scheme 99.4%…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An experimental study of deep convolutional features for iris recognition

Minaee

Abdolrashidiy

Wang

2016

2016 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

Self Cite

107

View full text Add to dashboard Cite

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“…The method is suited to discriminate texture which display joint translation and rotation invariance (Mallat, 2012;Bruna and Mallat (2013)), and by this way could be relevant to analyze and classify weather radar images. Finsberg (2014), Minaee et al (2015) and Sifre (2014) present different applications of the method. To assess the potential of the scattering to accomplish the task at hand, this section will describe application of the scattering transform to the classification of the set of rainfall radar images presented in Section 2.…”

Section: Application To the Classification Of Rainfall Radar Imagesmentioning

confidence: 98%

Classification of rainfall radar images using the scattering transform

Lagrange

Andrieu

Emmanuel

et al. 2018

Journal of Hydrology

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a b s t r a c tThe classification of rainfall fields has mainly focused on the split between convective and stratiform rainfall fields. In the present case study, the wavelet-based scattering transform is used to classify rainfall events observed by a weather radar. This very recent method has, to the best of the authors' knowledge, not yet been applied for such a purpose. This method considers the spatial properties of rainfall radar images. This case study regroups 34 rainfall periods recorded over the Nantes region (western France) during 23 days in both 2009 and 2012. These periods display different characteristics in terms of duration and type of rainfall field. A reference configuration of the scattering transform has been evaluated and compared to various configurations in order to approximate the application conditions most appropriate to this case study. This evaluation is performed by a leave-one-out cross validation. A global accuracy of 93.5% of well classified images is obtained in the reference conditions which is an encouraging result. The temporal sampling of the rainfall fields is an important aspect of the classification process.

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“…Here, the choice of features for pallor classification is motivated by methods with low time-complexity for feature extraction, which are useful for the development of a fast pallor screening applications. Thus, texture-based features for skin-related segmentation tasks with high computational time complexities such as histogram of oriented gradients (HOG), co-occurrence matrix and Gabor wavelet-based features [18] are not analyzed here.…”

Section: Related Workmentioning

confidence: 99%

Azure-Based Smart Monitoring System for Anemia-Like Pallor

2017

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Increasing costs of diagnostic healthcare have necessitated the development of hardware independent non-invasive Point of Care (POC) systems. Although anemia prevalence rates in global populations vary between 10% and 60% in various demographic groups, smart monitoring systems have not yet been developed for screening and tracking anemia-like pallor. In this work, we present two cloud platform-hosted POC applications that are directed towards smart monitoring of anemia-like pallor through eye and tongue pallor site images. The applications consist of a front-end graphical user interface (GUI) module and two different back-end image processing and machine learning modules. Both applications are hosted on a browser accessible tomcat server ported to an Azure Virtual Machine (VM). We observe that the first application spatially segments regions of interest from pallor site images with higher pallor classification accuracy and relatively longer processing times when compared to the lesser accurate yet faster second application. Also, both applications achieve 65-98% accuracy in separating normal images from images with pallor or abnormalities. The optimized front-end module is significantly light-weight with a run-through time ratio of 10 −5 with respect to the back-end modules. Thus, the proposed applications are portable and hardware independent, allowing for their use in pallor monitoring and screening tasks.

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Iris recognition using scattering transform and textural features

Cited by 59 publications

References 27 publications

An experimental study of deep convolutional features for iris recognition

An experimental study of deep convolutional features for iris recognition

Classification of rainfall radar images using the scattering transform

Azure-Based Smart Monitoring System for Anemia-Like Pallor

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