A face recognition method based on deep separable convolution model

A convolution model and face recognition technology, applied in the field of face recognition, can solve the problems of low calculation speed and large memory usage, and achieve the effect of optimizing the network structure, simplifying the network layer, and accurate and fast face recognition function

Active Publication Date: 2018-12-11
NANJING KIWI NETWORK TECH CO LTD
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Problems solved by technology

Existing deep neural convolutional network models such as ResNet-50 have a prediction accuracy of over 97%, but there is still a lot of room for improvement before the model is put into actual products.
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Method used

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  • A face recognition method based on deep separable convolution model
  • A face recognition method based on deep separable convolution model
  • A face recognition method based on deep separable convolution model

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Embodiment

[0033] The face recognition method based on the deep separable convolution model of the present embodiment comprises the following steps:

[0034] The first step is to read the face image sample data set, each face image has 3 channels, its height is 112 pixels, and its width is 112 pixels;

[0035] Existing massive databases, such as VGGFace2, some of the data have a very high similarity, and some non-face pollution data exists in it. Therefore, it is a very necessary step to merge and clean up the data in the database. The specific method is:

[0036] Map the existing face data samples in the face data set through the FaceNet method to obtain a series of feature vector sets in the X-dimensional feature space Λ={λ 1 ,λ 2 ,λ 3 ,…}, where each set of eigenvectors λ i Both are X-dimensional, and we judge their similarity by comparing the angle between the two sets of eigenvectors. Assume that the two sets of X-dimensional eigenvectors in Λ are respectively λ i ={v i1 ,v ...

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Abstract

The invention relates to a face recognition method based on a deep separable convolution model, the method comprising the following steps: 1, reading a face image sample data set; 2, establishing a deep separable convolution model, wherein the deep separable convolution model cascades a plurality of residual bottleneck module between two adjacent convolution modules; 3, updating the parameters ofthe deep separable convolution model by using a gradient descent algorithm; 4, carrying out face recognition through the deep separable convolution model after parameter updating. The method can improve the recognition speed on the basis of ensuring the face recognition accuracy. Experiments show that the method can ensure that the recognition accuracy is higher than 99%, so that the recognition speed on an ARMv8 mobile terminal reaches less than 300ms and the mobile terminal can have an accurate and fast face recognition function.

Description

technical field [0001] The invention relates to a face recognition method based on a deep separable convolution model, belonging to the technical field of face recognition. Background technique [0002] In recent years, the demand for face recognition in daily life has been increasing. Some fields, such as face access control, face attendance, face ticket purchase, and face chasing, all have great room for development. These fields have high requirements for the speed and accuracy of face recognition. The convolutional neural network based on deep learning is the cornerstone of face recognition technology. The network continuously reduces the difference between the model output value and the real value through gradient descent feedback to approach the real result. [0003] At present, face recognition technology mainly focuses on the improvement of accuracy. However, in order to make face recognition technology more popular and user-friendly in daily life, the speed of reco...

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Application Information

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IPC IPC(8): G06K9/00G06N3/04
CPCG06V40/172G06N3/045
Inventor 杨通彭若波杜曦
Owner NANJING KIWI NETWORK TECH CO LTD
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