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Fast face angle recognition method based on deep learning

A deep learning and recognition method technology, applied in the field of face recognition, can solve the problems of uncertain face angle and low robustness, achieve fast and accurate face angle recognition, and improve the effect of face recognition accuracy

Inactive Publication Date: 2017-10-27
HARBIN MAX TELEGENT SCI & TECH DEV CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] Face angle recognition technology is for each collected image containing a face, which can quickly and accurately obtain the three-dimensional angle information of the face in the image, and can use this information for further face recognition. Face angle recognition is a computer vision It is an important research direction in the field and face recognition field. It is a crucial step in the face recognition system. It has wide application value and good market prospects. The existing face angle recognition technologies include template-based methods, The method of face collection features, etc., but these methods have certain limitations. They can only recognize face angle changes in a small range of single directions, and are not robust against factors such as illumination changes, partial occlusions, and expression changes. , it is impossible to determine the complex face angles that change simultaneously in multiple directions

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  • Fast face angle recognition method based on deep learning
  • Fast face angle recognition method based on deep learning

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Experimental program
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Embodiment 1

[0016] A fast face angle recognition method based on deep learning, the method includes the following steps: (1) mark the face images when the rotation is 0°, plus or minus 15°, plus or minus 30° and plus or minus 45° to establish Database; (2) The angle label is extracted through a five-layer convolution module, and converted into a 2048-dimensional feature vector after two layers of full connection, which is used as the input of the softmax classification layer for classification to establish a convolutional neural network structure; ( 3) Based on the ubuntu 16.04 operating system, under GPU1080, the convolutional neural network is trained under the deep learning framework CAFFE, and the face angle recognition model is obtained.

Embodiment 2

[0018] According to the fast face angle recognition method based on deep learning described in embodiment 1, the specific process of establishing the database is: the database is 300 different people's face images, a total of 7000, the images in the database Including face images when the head rotates 0°, plus or minus 15°, plus or minus 30° and plus or minus 45° around each axis in the three-dimensional vertical coordinate system, and the rotation angle is normalized to a value between 0-1 The value is used as the label value to mark the angle of the face in each image, and the marked image is used for the training of the neural network.

Embodiment 3

[0020] According to the fast face angle recognition method based on deep learning described in embodiment 1 or 2, the specific process of establishing the convolutional neural network structure is as follows: the input layer accepts input data, obtains image data and corresponding label values ​​thereof, and establishes The data set contains three label values, which correspond to the angle labels of each axis rotation of the three-dimensional coordinate system, and then undergoes feature extraction through five layers of convolution modules. Each convolution module includes a convolution layer and a pooling layer, which will be extracted to The feature vector of the input to the full connection layer, after two layers of full connection, the feature map is converted into a 2048-dimensional feature vector, which is used as the input of the softmax classification layer for classification. The three labels correspond to three parallel classification layers. Each classification Th...

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Abstract

The invention discloses a fast face angle recognition method based on deep learning. Face angle recognition relates to rotation angles of a head around three axes in a three-dimensional vertical coordinate system, and a face angle change can cause partial absence of facial feature information. The method comprises the following steps: (1) labeling face images at times of rotation of 0 degree, plus and minus 15 degrees, plus and minus 30 degrees and plus and minus 45 degrees to establish a database; (2) using angle labels to carry out feature extraction through five layers of a convolution module, transforming features into 2048-dimensional feature vectors through two layers of full connection, using the feature vectors as inputs of a softmax classification layer, carrying out classification, and thus establishing a convolutional neural network structure; and (3) based on an ubuntu 16.04 operating system, training the convolutional neural network under a GPU of 1080 and a deep learning framework of a CAFFE to obtain a face angle recognition model. The method is used for deep learning-based real-time recognition of three-dimensional face angles.

Description

Technical field: [0001] The invention relates to the field of face recognition, in particular to a fast face angle recognition method based on deep learning. Background technique: [0002] Face angle recognition technology is for each collected image containing a face, which can quickly and accurately obtain the three-dimensional angle information of the face in the image, and can use this information for further face recognition. Face angle recognition is a computer vision It is an important research direction in the field and face recognition field. It is a crucial step in the face recognition system. It has wide application value and good market prospects. The existing face angle recognition technologies include template-based methods, The method of face collection features, etc., but these methods have certain limitations. They can only recognize face angle changes in a small range of single directions, and are not robust against factors such as illumination changes, par...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/161G06V40/172G06V40/168G06F18/214
Inventor 姚一鸣
Owner HARBIN MAX TELEGENT SCI & TECH DEV CO LTD
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