A noise robust face recognition method based on a cascade deep convolutional neural network

A deep convolution and neural network technology, applied in the field of computer vision, can solve problems such as the inability to obtain satisfactory face recognition results and loss of face detail features, and achieve the goal of reducing gradient disappearance, accelerating training speed, and enhancing denoising effects Effect

Active Publication Date: 2019-06-28
XIAMEN UNIV
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Problems solved by technology

However, due to its simple network architecture and the use of a large number of pooling operations, some facial detai

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  • A noise robust face recognition method based on a cascade deep convolutional neural network
  • A noise robust face recognition method based on a cascade deep convolutional neural network
  • A noise robust face recognition method based on a cascade deep convolutional neural network

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Embodiment Construction

[0038] The method of the present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0039] see figure 1 , the embodiment of the present invention includes the following steps:

[0040] 1. Prepare the training sample set.

[0041] 1.1. Perform face detection and face key point detection on each image in the training sample set one by one, using MTCNN (K. Zhang, Z. Zhang, Z. Li, Y. Qiao, "Joint face detection and alignment using multi-task cascaded convolutional networks”, IEEE Signal Processing Letters, vol.23, no.10, pp.1499-1503, 2016.) method, get the position of the key points of the face in each image, and align the face to the standard face on the image.

[0042] 1.2 Cut each face image to obtain a face image with a size of 64×64 pixels.

[0043] 1.3. Randomly add Gaussian white noise (AWGN) to each face image. The intensity of Gaussian white noise is expressed as σ, and its range is set to σ∈[0,50] to obtain a...

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Abstract

The invention discloses a noise robust face recognition method based on a cascade deep convolutional neural network, and relates to a computer vision technology. The method comprises the following steps: firstly, designing a denoising sub-network and a face recognition sub-network, and in the denoising sub-network, connecting feature maps generated by all layers in front six layers of the networklayer by layer from front to back by using a dense connection method so as to fully utilize face features generated by a shallow-layer network. A residual network structure is adopted in the face recognition sub-network, and an identity mapping method is used for performing shortcut connection between different layers of the network, so that a gradient vanishing phenomenon in a deep network structure can be effectively reduced; and then, joint training is carried out on the de-noising sub-network and the face recognition sub-network by adopting a cascade method to obtain noise robust face representation, and a joint loss function is designed for weight updating of the two sub-networks. And finally, a final noise face recognition result is obtained according to the trained network model.

Description

technical field [0001] The invention relates to computer vision technology, in particular to a noise-robust face recognition method based on a cascaded deep convolutional neural network. Background technique [0002] In the past few decades, with the widespread application of face recognition technology, it has received more and more attention in computer vision and pattern recognition. In recent years, due to the rapid development of deep learning, the accuracy of face recognition methods has been greatly improved, and in some specific scenarios it has surpassed the recognition limit of the human eye. However, due to the influence of external interference factors such as illumination, occlusion, and noise, face recognition still faces great challenges in these interference environments. [0003] With the development of deep learning, convolutional neural network (CNN) has been widely used in various computer vision tasks and achieved excellent results. At present, the mai...

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Application Information

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IPC IPC(8): G06K9/00G06K9/40G06K9/46G06K9/62
Inventor 严严孟祥邦王菡子
Owner XIAMEN UNIV
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