Face recognition detection method based on hybrid attention mechanism
A face recognition and detection method technology, applied in the field of image processing, can solve problems such as difficulty in extracting effective feature information, positioning errors, target missed detection, etc., to achieve the effect of improving accuracy, enhancing characterization ability, and improving detection accuracy
Active Publication Date: 2021-01-08
北京电信易通信息技术股份有限公司
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[0004] In the complex detection task of face recognition and detection, the huge difference in individual feature information and the changing visual detection area make it difficult for the existing deep neural network of face recognition and detection to extract effective feature information, resulting in missed detection and positioning of targets. Therefore, it is extremely important to effectively capture the key recognition features of human face targets
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[0052] Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatuses and methods consistent with aspects of the present disclosure as recited in the appended claims.
[0053] The terminology used in the present disclosure is for the purpose of describing particular embodiments only, and is not intended to limit the present disclosure. As used in this disclosure and the appended claims, the singular forms "a", "the", and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise. It should also be understood...
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The invention provides a face recognition detection method based on a hybrid attention mechanism. The face recognition detection method comprises the following steps: constructing a face target imagedata set; training a designed deep neural network model by adopting the training set and the verification set of the face target image data set; and using a test set in the face target image data setin the trained deep neural network model to detect a human face target in the image. According to the invention, the mixed attention module is adopted to extract key detection features, and the key features are transmitted to a subsequent layer, so that the detection accuracy is improved; a Faster RCNN network based on FPN is established, and rich detail information is extracted by adopting a multi-scale feature fusion technology, so that the representation capability of the network on face features is enhanced; and the SENet attention module is constructed and embedded into the FPN, so that redundant feature information can be filtered by the network, key features are transmitted to the RPN network, and the face recognition detection precision is improved.
Description
technical field [0001] The invention relates to the technical field of image processing, in particular to a face recognition detection method based on a mixed attention mechanism. Background technique [0002] With the rapid development of computer technology, biometric recognition has been widely studied as an independent research topic in the field of computer vision, and face recognition detection, as a branch of biometric recognition, has become a popular research direction. Face recognition detection refers to any given image, using a certain strategy to search it to determine whether it contains one or more faces, and if so, return the detection task of the location of the face (Wang Feilong. A review of face recognition technology and its security [J]. Information Recording Materials, 2018, 19(12): 229-230.); This technology has been widely used in many fields such as image search, identity authentication and security protection Application (Yan Chen. Research and ve...
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Login to View More IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V40/161G06V40/168G06N3/045G06F18/213G06F18/214G06F18/24
Inventor 刘晨杨涛
Owner 北京电信易通信息技术股份有限公司