An adaptive multimodal biometric feature recognition method based on visual perception

In order to overcome the recognition accuracy and image information entropy of traditional biometric recognition methods, this paper proposes an adaptive multimodal biometric recognition method based on visual perception. Segmenting images using Weber scores and calculating the just noticeable difference (JND) to obtain human visual perceptual features; using induction bilateral filtering method for processing; using adaptive convolutional neural networks to fuse multimodal biological features; by using the support vector machine Shafer Dempster (SVM-DS) feature recognition function, the reliability allocation of different evidence body recognition is obtained, and the target type obtained by the decision module is obtained to achieve biometric recognition. After testing, when using this method, the image information entropy is greater than 0.9, and the image quality is optimised; in the process of feature recognition, it has the ability to accurately match.