Voice and face composite identity authentication method based on end-to-end deep neural network

Abstract

The invention discloses a voice and face composite identity authentication method based on an end-to-end deep neural network. The method comprises the steps that a text-related voice signal and a facial video signal are collected, voice vocal print features and multi-frame face features are extracted, the voice vocal print features and the multi-frame face features are connected to obtain an identity feature vector, dimension reduction processing is carried out on the identity feature vector, and personal identity verification is carried out by adopting a Triplets Loss method. The voice and face composite identity authentication method based on the end-to-end deep neural network has the advantages that identity authentication is carried out by combining the voice and face features, the deficiency caused by a single feature is made up for, the voice and face composite identity authentication method can be applied to occasions such as entrance guard and check on work attendance, man-machine interaction and the like, and the identification accuracy is effectively improved.

Description

technical field [0001] The invention belongs to the technical field of identity recognition, and in particular relates to an end-to-end deep neural network-based voice and face composite identity authentication method. Background technique [0002] With the development of artificial intelligence technology, identity verification methods such as face recognition and voiceprint recognition have been widely used in the field of intelligent security. Among them, FaceNet, a face recognition model based on convolutional neural network developed by Google, directly learns an encoding method from image to Euclidean space end-to-end, and then performs face recognition, face verification and face clustering based on this encoding. Wait. FaceNet has an accuracy rate of 0.9963 on the LFW dataset and an accuracy rate of 0.9512 on the YouTube Faces DB dataset. The Deep Speaker developed by Baidu is composed of deep neural network layers. It uses MFCC, time pooling based on cosine simila...

AI Technical Summary

Problems solved by technology

However, in actual engineering applications, factors such as lighting changes in the application environment and face posture have a greater impact on face recognition, and the state of the speaker's vocal tract and environmental noise also have a greater impact on voiceprint recognition.

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