Automated segmentation of iris images acquired in an unconstrained environment using HOG-SVM and GrowCut

Radman, Abduljalil; Zainal, Nasharuddin; Suandi, Shahrel Azmin

doi:10.1016/j.dsp.2017.02.003

Cited by 73 publications

(46 citation statements)

References 36 publications

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“…Proenca performed coarse iris segmentation using a neural network classifier and refined this segmentation via polynomial fitting [28]. Radman et al use a HoG (Histogram of Gradients) as input features to train a support vector machine (SVM) [30]. This trained SVM is then used on new images to localize the iris.…”

Section: Resultsmentioning

confidence: 99%

Unconstrained Iris Segmentation Using Convolutional Neural Networks

Ahmad

Fuller

2019

Lecture Notes in Computer Science

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The extraction of consistent and identifiable features from an image of the human iris is known as iris recognition. Identifying which pixels belong to the iris, known as segmentation, is the first stage of iris recognition. Errors in segmentation propagate to later stages. Current segmentation approaches are tuned to specific environments. We propose using a convolution neural network for iris segmentation. Our algorithm is accurate when trained in a single environment and tested in multiple environments. Our network builds on the Mask R-CNN framework (He et al., ICCV 2017). Our approach segments faster than previous approaches including the Mask R-CNN network. Our network is accurate when trained on a single environment and tested with a different sensors (either visible light or near-infrared). Its accuracy degrades when trained with a visible light sensor and tested with a near-infrared sensor (and vice versa). A small amount of retraining of the visible light model (using a few samples from a near-infrared dataset) yields a tuned network accurate in both settings. For training and testing, this work uses the Casia v4 Interval, Notre Dame 0405, Ubiris v2, and IITD datasets.

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Section: Resultsmentioning

confidence: 99%

Unconstrained Iris Segmentation Using Convolutional Neural Networks

Ahmad

Fuller

2019

Lecture Notes in Computer Science

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“…[51], three expressions are added to the Hu moment as the feature of gesture recognition to match the gesture template. In [52], when the static gesture feature is selected, the concave and the perimeter area ratio of the gesture contour and the first four Hu moments are combined, and the radial kernel function is used for SVM classification.In [53], HOG features are used to identify multiple gestures using the SVM classifier. Compared with the results in the above literature, the results of the maximum recognition rate, the minimum recognition rate and the average recognition rate are compared.…”

Section: Fig12 Recognition Rate On Different Featuresmentioning

confidence: 99%

Research on gesture recognition of smart data fusion features in the IoT

Tan

Sun

et al. 2019

Neural Comput & Applic

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With the rapid development of Internet of Things (IoT) technology, the interaction between people and things has become increasingly frequent. Use simple gestures instead of complex operations to interact with the machine, the fusion of smart data feature information and so on has gradually become a research hotspot. Considering that the depth image of the Kinect sensor lacks color information and is susceptible to depth thresholds, this paper proposes a gesture segmentation method based on the fusion of color information and depth information, in order to ensure the complete information of the segmentation image, a gesture feature extraction method based on Hu invariant moment and HOG feature fusion is proposed, by determining the optimal weight parameters, the global and local features are effectively fused. Finally, the SVM classifier is used to classify and identify gestures. The experimental results show that the proposed fusion features method has a higher gesture recognition rate and better robustness than the traditional method.

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“…[12] proposed GVF active contour method to find out exact boundary of iris though it was expanded and shrink. In paper [13], Radman et. al.…”

Section: A Literature Surveymentioning

confidence: 99%

“…Performances of accuracy and average segmentation time for proposed entropy based CNN segmentation algorithm is compared with some other existing method [11][12][13][14][15][16][17][18]…”

Section: Fig11 Examples Of Sample Images When Segmentation Failsmentioning

confidence: 99%

Entropy Based CNN for Segmentation of Noisy Color Eye Images using Color, Texture and Brightness Contour Features

Pathak¹,

Bairagi²,

Srinivasu³

2019

IJRTE

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Today’s most of the iris recognition systems are strongly dependent on user’s cooperation during image acquisition such as stop-stair condition, head position and camera distance. Images are taken in NIR spectrum to reduce the noise such as effect of illumination. Challenges faced by existing iris recognition systems are such as they are time consuming due to need of extra hardware setup and unable to achieve better performance for images acquired on-the-move, at-a-distance, etc.. To overcome these challenges, in this paper we proposed novel segmentation algorithm based on content based image retrieval approach. In proposed segmentation method, color, texture and brightness contour features were extracted. Entropy value for these extracted contour features was measured to reduce the dimensionality of features. These set of calculated entropy value was given as input to convolutional neural network to cluster noisy eye image into iris, sclera and pupil region. The proposed segmentation algorithm was experimented on freely available UBIRIS.V2 noisy eye image database using MATLAB. The experimentation results shows that proposed segmentation method is superior as compared to existing method by reducing average segmentation time up to 0.9sec and increasing segmentation accuracy up to 97% for non ideal color eye images.

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Automated segmentation of iris images acquired in an unconstrained environment using HOG-SVM and GrowCut

Cited by 73 publications

References 36 publications

Unconstrained Iris Segmentation Using Convolutional Neural Networks

Unconstrained Iris Segmentation Using Convolutional Neural Networks

Research on gesture recognition of smart data fusion features in the IoT

Entropy Based CNN for Segmentation of Noisy Color Eye Images using Color, Texture and Brightness Contour Features

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