Face characteristic extraction modeling method based on neural network, face identification method, face characteristic extraction modeling device and face identification device

Abstract

The invention provides a face characteristic extraction modeling method based on a neural network, and a face characteristic extraction modeling device. The face characteristic extraction modeling method comprises the steps of acquiring a triple face picture from a training set of a preset application scene; acquiring a trained face identification neural network, and determining a triple deep neural network according to the face identification neural network; using the triple face picture as input of the triple deep neural network, and determining a loss cost function value; training the triple deep neural network according to the loss cost function value and a training parameter; performing face identification testing on the triple deep neural network through the training set of the preset application scene, determining the testing precision according to the testing result, and determining an objective face feature extraction model according to the testing precision and the triple deep neural network. The face characteristic extraction modeling method and the face characteristic extraction modeling device can realize a beneficial effect of high face identification precision when a face feature model which is obtained through modeling is applied in a specific face identification application scene. The invention further provides a face identification method and a face identification device.

Description

technical field [0001] The present invention relates to the technical field of face recognition, in particular to a neural network-based face feature extraction and modeling method and device, and a face recognition method and device. Background technique [0002] Face recognition is a biometric technology for identification based on human facial feature information. Generally, a camera or camera is used to collect images or video streams containing faces, and then automatically detect and track faces in the images, and then Face recognition is performed on detected faces, which is also commonly called portrait recognition or face recognition. [0003] The traditional face recognition neural network is widely used in face recognition, but because the face feature extraction model in the traditional face recognition neural network is mostly based on training on a large face data set (more than 500,000) , and then used as a general model in various scenarios. [0004] Since ...

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Problems solved by technology

Therefore, the face recognition accuracy of the general model in specific face recognition application scenarios is not high

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