Face recognition system and method based on locally distributed linear embedding algorithm

Abstract

Disclosed are a face recognition system and method based on a locally distributed linear embedding algorithm. The system comprises an image collecting module, an image preprocessing module, a feature analysis module, a dimensionality mapping module, a recognition database and a recognition module. The method comprises the steps that (1) face images are obtained; (2) the obtained face images are stored; (3) preprocessing is conducted on the obtained face images; (4) feature analysis and extraction are conducted on the preprocessed face images, and face features in a high-dimension space are mapped into a low-dimension space; (5) the unknown face images are recognized according to the spatial relationship between the corresponding face images in the low-dimension space and images in the recognition database. According to the face recognition system and method based on the locally distributed linear embedding algorithm, the features of the obtained face images are analyzed and a dimensionality conversion program is operated according to the obtained face images so that during recognition, points in a similar neighborhood can be found out in the high-dimension space automatically according to the face images, after the face images are mapped into the low-dimension space, the relative order relation cannot be changed, and therefore the face recognition accuracy can be effectively improved.

Description

technical field [0001] The present invention relates to an image analysis system and its method, in particular to a method of extracting features from acquired face images, mapping high-dimensional features to low-dimensional spaces through manifold space transformation, and according to the extracted face features and database A face recognition method for identifying and comparing other face image features and a face recognition system using the method. Background technique [0002] Video surveillance systems have been widely used in many fields, but they have also brought about many problems. For example, in the face of tens of thousands or hundreds of thousands of surveillance cameras in a city, the staff is simply unable to manage and monitor them, and the video system is almost reduced to A video recording tool that provides post-mortem forensics. In addition, with the acceleration of technological updates, there are also some technical problems in the construction of...

AI Technical Summary

Problems solved by technology

When the monitoring system realizes high-definition, the amount of data increases geometrically, limited by factors such as computer performance, and traditional retrieval is time-consuming and inefficient

A large amount of unstructured or semi-structured video image data, it will cost a lot of money to convert them into relational data for future analysis.

Images