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Iris image segmentation and positioning method, system and device based on deep learning

An iris image, deep learning technology, applied in neural learning methods, instruments, biological neural network models, etc., can solve the problem of low iris recognition accuracy

Active Publication Date: 2019-05-28
INST OF AUTOMATION CHINESE ACAD OF SCI
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Problems solved by technology

[0006] In order to solve the above-mentioned problems in the prior art, that is, the problem of low iris recognition accuracy in uncontrollable scenes, the present invention provides a method for iris image segmentation based on deep learning, including:

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[0069] The application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain related inventions, not to limit the invention. It should also be noted that, for the convenience of description, only the parts related to the related invention are shown in the drawings.

[0070] It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and embodiments.

[0071] The present invention provides a method for iris image segmentation and positioning based on deep learning. The method includes two parts: the first part is a multi-task network based on a fully convolutional encoding layer and decoding layer, and the encoding layer of the net...

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Abstract

The invention belongs to the field of mode recognition, computer vision and image processing, particularly relates to an iris image segmentation and positioning method, system and device based on deeplearning, and aims to solve the problem of low iris recognition precision in a non-controllable scene. The method comprises the steps that a to-be-processed iris image is acquired; four mapping images are generated by adopting a multi-task neural network model, wherein the four mapping images respectively correspond to a pupil center, an iris inner boundary, an iris outer boundary and an iris segmentation mask; the iris segmentation mask mapping graph is processed by adopting threshold segmentation to complete iris segmentation; the pupil center position is predicted according to the geometrical relationship between the pupil center and the iris mask; the mapping graph is de-noised and calculated by utilizing a geometrical relationship among the pupil, the iris and the sclera to obtain iris inner and outer circle parameters and finish iris positioning. According to the method, the iris image acquired in the non-controllable environment can be effectively segmented and positioned, a good foundation is laid for subsequent normalization and recognition, and the iris recognition precision in the non-controllable environment is improved.

Description

technical field [0001] The invention belongs to the fields of pattern recognition, computer vision and image processing, and specifically relates to a method, system and device for iris image segmentation and positioning based on deep learning. Background technique [0002] With the rise of artificial intelligence, biometric identification technologies such as face recognition, iris recognition, and fingerprint recognition have received great attention. Among them, iris recognition technology is considered to be one of the most stable, accurate and reliable verification methods, so Widely used in intelligent unlocking, border control, banking and finance, access control attendance and other fields. [0003] In the iris recognition system, iris segmentation and positioning are at the beginning of the entire processing flow, so its accuracy will directly affect the accuracy of subsequent processing. Iris segmentation refers to extracting effective iris texture pixels, elimina...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/04G06N3/08
Inventor 孙哲南谭铁牛王财勇
Owner INST OF AUTOMATION CHINESE ACAD OF SCI
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