Human eye iris detection method and device

A detection method, iris technology, applied in character and pattern recognition, instruments, calculations, etc., can solve problems such as excessive dependence on the starting point of iterations, achieve efficient positioning, high accuracy and robustness, and improve detection accuracy

Active Publication Date: 2020-04-28
ZHEJIANG UNIV
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

[0006] In view of this, the embodiment of the present invention provides a human iris detection method an

Method used

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  • Human eye iris detection method and device
  • Human eye iris detection method and device
  • Human eye iris detection method and device

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Embodiment 1

[0024] figure 1 It is a flowchart of a human iris detection method according to Embodiment 1 of the present invention, such as figure 1 As shown, the method includes the following steps:

[0025] Step S101, receiving a face data set, and training a neural network for key point positioning.

[0026] Specifically, in the above steps, the network can be trained using four-tuple data including face images, face key point coordinates, face category labels, and face pose angles. The key point positioning neural network can use Hourglass as the Backbone convolutional neural network.

[0027] Step S102, use the key point positioning neural network to perform rough positioning of the key points of the face, obtain the corresponding key points of the face, and select the key points of the human eye area according to the obtained key points of the face.

[0028] Specifically, before using the key point positioning neural network for rough positioning of face key points, the input face...

Embodiment 2

[0095] The present application also provides a human iris detection device for implementing the human iris detection method in Embodiment 1, Figure 7 It is a structural schematic diagram of a human iris detection device according to an embodiment of the present invention, the device includes:

[0096] The training neural network module 10 is used to receive the face data set and train the key point positioning neural network.

[0097] The key point detection module 11 is used to use the key point positioning neural network to roughly locate the key points of the human face, obtain the corresponding key points of the human face, and select the key points of the human eye area according to the obtained key points of the human face.

[0098] The parameter estimation module 12 is used to calculate the angle size of the iris not blocked by the eyelids and the upper limit of the radius of the outer boundary of the iris of the human eye from the key points of the human eye area.

...

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Abstract

The invention discloses a human eye iris detection method and a device, and the method comprises the steps: receiving a face data set, and training a key point positioning neural network; carrying outcoarse positioning on the face key points by using a key point positioning neural network to obtain corresponding face key points, and selecting human eye region key points according to the obtainedface key points; calculating the angle of the iris not shielded by eyelids and the upper bound of the radius of the outer boundary of the iris of the human eye according to the key points of the humaneye area; and performing iterative optimization on the key points of the human eye area according to the angle of the iris which is not shielded by the eyelids and the upper bound of the radius of the outer boundary of the human eye iris, thereby realizing fine positioning of the key points and fine positioning of the outer boundary of the iris. According to the method, precise positioning of human eye key points and iris outer boundaries can be efficiently positioned, the accuracy and robustness are high, and the real-time processing efficiency can be achieved.

Description

[0001] technology neighborhood [0002] The invention belongs to the field of iris detection, in particular to a human iris detection method and device. Background technique [0003] In the face recognition task, the face key point detection task is a very important branch. The face key point detection task refers to locating the key area of ​​the face for a given face image, usually referring to the eyes, nose, mouth, face contour, etc. Obtaining accurate facial key point positions is a prerequisite for tasks such as face gesture recognition, expression recognition, face beautification, and fatigue recognition. [0004] The traditional iris detection algorithm is represented by the Daugman algorithm, which was proposed by Dr. Daugman in the literature in 1993. The algorithm uses a strategy from coarse to fine when locating the iris, and finally achieves single-pixel accuracy, and estimates the iris and The center and radius of the iris. [0005] Although the Daugman algori...

Claims

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Application Information

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IPC IPC(8): G06K9/00
CPCG06V40/171G06V40/18
Inventor 于慧敏丁洋凯
Owner ZHEJIANG UNIV
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