Live face identification method and system

Abstract

The invention discloses a live face identification method. The method comprises steps that video frame images shot by a camera are acquired; whether the video frame images are live face images can be identified through a trained convolutional neural network. The invention further discloses a live face identification system, the system comprise a camera module, an acquisition module and a live face detection module, wherein the acquisition module is connected with the camera module and the live face detection module, the acquisition module is used for sampling videos shot by the camera module to acquire the video frame images, and the live face detection module is used for detecting and identifying whether the video frame images are live face images through the trained convolutional neural network. The method and the system are advantaged in that brains of human beings are simulated through employing a depth learning neural network, whether the video frame images are live face images is identified through the trained convolutional neural network, whether the images are live faces or pictures can be identified through intuition of a computer to a great degree but not a designed algorithm, so algorithmic defects can be avoided, and identification accuracy is substantially improved.

Description

technical field [0001] The invention belongs to the technical field of face recognition, and in particular relates to a living face recognition method and system. Background technique [0002] The use security of biometric identification system is a common concern of people. People's confidence and acceptance of biometric identification system largely depends on the robustness, low error rate and anti-spoofing ability of the system. Therefore, liveness detection is an important function of detecting and rejecting counterfeit identity features in a biometric system. Of all the possible deceptions that face recognition systems may face, photos or videos are undoubtedly the most common. If there is no guarantee of live face detection, a photo or a video can be fraudulently passed, so what is the practical application value of such face recognition? Therefore, there are still serious security risks in face recognition technology. For example, in smart home security products, ...

AI Technical Summary

Problems solved by technology

However, this traditional algorithm cannot be flawless. This method is easy to cause misjudgment in real use, especially in complex environments, such as poor lighting conditions, the accuracy rate is not high

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