Face recognition method capable of extracting multi-level image semantics based on CNN (convolutional neural network)

Abstract

The invention provides a face recognition method capable of extracting multi-level image semantics based on the CNN (convolutional neural network). The method comprises the following steps: implementing further optimization on the basis of VGGNet, providing a new network structure, and implementing cross-level splicing on multi-level features to ensure that the finally-extracted image features have multi-level image semantics; and meanwhile, adding the extracted traditional features in the training of the CNN as additional features to ensure that the CNN feature information is more complete; then optimizing the structure of a shallow convolutional layer to ensure that the redundancy calculation is reduced and the calculation amount of models is greatly reduced; and finally, accelerating the convolutional layer by using an improved matrix decomposition algorithm to ensure that the network can be accelerated within 1 second and the accuracy rate of the models cannot be reduced when the speed-up ratio reaches four times. A face matching algorithm achieved by the invention has the characteristics of high accuracy and high real-time performance, and has higher accuracy and higher calculation efficiency compared with existing algorithms.

Description

technical field [0001] The invention provides a face recognition method based on CNN-based multi-level image semantics, and relates to the technical fields of deep learning and computer vision. Background technique [0002] Biometric-based identification technology has been widely used in scenarios such as access control, video security monitoring, and human-computer interaction. my country's biometric technology market is growing rapidly and is expected to reach a market size of around 30 billion in 2020. Commonly used biometric technologies mainly include: face, retina, fingerprint, gait and other methods. Among many biometric technologies, face recognition technology has the advantages of non-invasiveness, non-contact, and easy operation. Moreover, the collection of face image data is relatively easy, and the collection interaction method is more friendly, and it can be directly captured by a camera, which makes the application scenarios of face recognition more extensi...

AI Technical Summary

Problems solved by technology

[0009] The technical problem to be solved by the present invention is to overcome the problems of insufficient feature extraction, insufficient semantics, insufficient robustness, and poor real-time performance in existing face recognition methods.

Images