Human face recognition method based on multi-camera model

Abstract

The invention discloses a human face recognition method based on a multi-camera model. The method comprises the steps of 1 first multi-camera information acquisition, 2 human face detection and training, 3 feature storage, 4 human face (structural data mode), 5 secondary multi-camera information acquisition, 6 database-related information acquisition and 7 double-layer heterogeneous depth neural network algorithm comparison. According to the invention, a secondary multi-camera information acquisition technology is used; a double-layer heterogeneous depth neural network algorithm comparison technology is used; the problem that an identity card photo stored in an identity card chip has a small pixel in a human face area, is blurred, is generally a photo of years before, and greatly differs from a photo collected in the field is solved; the problem that a human face has many shelters, such as beards, glasses, hats and the like, is solved; the problem that the environmental light intensity and the observation angle of the human face are changed is solved; the problem that the human face is difficultly recognized is solved; and the provided human face recognition method is economic, real-time and accurate.

Description

technical field [0001] The invention relates to a face recognition technology, more specifically to a face recognition method based on a multi-camera model. Background technique [0002] Face recognition technology is an interdisciplinary and challenging frontier topic. It has developed rapidly and achieved fruitful research results. Non-linear modeling methods, statistical theory, learning technology based on Boosting, and face recognition based on 3D models Modeling and identification methods have gradually become the development trend of technology. Existing face recognition systems can achieve satisfactory results with the cooperation of users and ideal image acquisition conditions. However, when the user does not cooperate and the image acquisition is not ideal (such as poor lighting, occlusion, and low image resolution), the recognition rate of the existing system suddenly drops. Therefore, there are still many challenging problems to be solved in order to apply face...

AI Technical Summary

Problems solved by technology

However, when the user does not cooperate and the image acquisition is not ideal (such as poor lighting, occlusion, and low image resolution), the recognition rate of the existing system suddenly drops

Images