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A method of generating ID photo trimap using fully convolutional neural network

A technology of convolutional neural network and ID photo, which is applied in the field of using a full convolutional neural network to generate a Trimap map of ID photos, which can solve problems such as inability to achieve results

Active Publication Date: 2020-10-27
SOUTH CHINA UNIV OF TECH
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  • Application Information

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Problems solved by technology

However, this method does not achieve ideal results when the background and foreground contrast is low or the unknown area is large.

Method used

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  • A method of generating ID photo trimap using fully convolutional neural network
  • A method of generating ID photo trimap using fully convolutional neural network
  • A method of generating ID photo trimap using fully convolutional neural network

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Embodiment Construction

[0041] The present invention will be further described below in conjunction with specific examples.

[0042] The method provided in this example using the full convolutional neural network to generate the Trimap image of the certificate is as follows:

[0043] 1) Data set preparation

[0044] Training first needs to establish a dataset of ID photos of human head portraits including the shoulder area, such as figure 1 shown. The source of the data set is a batch of ID photos that need to be collected for laboratory projects. After collection, they are screened to ensure that men, women, adults, children, long hair, short hair, straight hair, curly hair, clothes of various colors, gray Different ID photos with blue background, and remove those photos with uneven exposure and out of focus. All images are cropped to the same size based on the position of the portrait to ensure the usability of the portrait. The dataset contains 2469 initial images.

[0045] In order to improv...

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Abstract

The invention discloses a method for generating a certificate Trimap diagram by using a full convolutional neural network. The method comprises the following steps: 1) carrying out data input; 2) training a full convolutional neural network model; 3) segmenting the input image using the trained model. The method mainly solves the problem that a network model is constructed through a self-established certificate photograph data set including a human head image and a shoulder area, and the network model is trained by using the data set. After the training, the network model will be able to segment the human head image including shoulder region with high accuracy. The method has the advantages of high accuracy, good noise immunity, simple use, high efficiency, high speed and the like. Compared with a conventional method, the method realizes the Trimap image segmentation processing for the certificate photo for the first time, and provides good input for matting. At the same time, the method of the invention achieves better effect in the recognition and segmentation of shoulders and clothes.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a method for generating a Trimap image of a certificate photo using a fully convolutional neural network. Background technique [0002] The ID photo is a photo that proves the identity of the holder in various types of certificates. People need to apply for various certificates in their daily life, and the photos taken for making these certificates also need to be processed before they can be used. A very common way to process ID photos is to replace the background color of photos. The core technologies involved include image segmentation and matting. Most of the matting algorithms with ideal effects depend on the input of Trimap images, and the quality of Trimap images also directly affects the matting results. At present, there are very few studies on the automatic generation of Trimap images. Manually marking images consumes manpower and time. It is not small, so the...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/194G06T7/11
CPCG06T2207/20081G06T2207/20084G06T2207/30196G06T7/11G06T7/194
Inventor 邹歆仪李桂清
Owner SOUTH CHINA UNIV OF TECH
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