Multi-mode face identification method

Abstract

The invention discloses a multi-mode face identification method which comprises the following three steps: firstly, performing global spectrum face matching on a to-be-matched face and faces in a database; secondly, if the matching is failed, judging that two face modes are not matched, and if the matching is successful, performing geometric characteristic matching and judging whether the two face modes are matched or not; finally, transmitting face data without global spectrum face matching and geometric characteristic matching into a new face memory database in the database. According to the method, overall difference of different faces is distinguished by a global spectrum face matching method, and local tiny difference of the faces are distinguished in combination with local geometric characteristic matching, so that the face identification performance is improved; for newly acquired faces, if the newly acquired faces are not matched with the faces in the original database, the newly acquired faces are stored in the new face memory database module of the database, so that original images can be quickly called from the new face memory database module during re-matching.

Description

technical field [0001] The invention relates to a face recognition method, in particular to a multi-mode face recognition method combining global feature matching and local feature matching. Background technique [0002] There are many methods of face recognition, which can be roughly divided into two categories: matching based on global features and matching based on local features. Global feature matching methods include PCA, 2DPCA, LDA, spectral face, etc., and local feature matching methods include geometric features, wavelet features, and LBP features. In fact, face patterns often have non-rigid deformations (such as smiles and frowns), and it is difficult to take into account the impact of overall differences and local variations on recognition accuracy by simply using a global feature matching method or a local feature matching method. New faces that do not match the faces in the database cannot be memorized and stored, which brings trouble to the work of investigati...

AI Technical Summary

Problems solved by technology

In fact, face patterns often have non-rigid deformations (such as smiles and frowns), and it is difficult to take into account the impact of overall differences and local variations on recognition accuracy by simply using a global feature matching method or a local feature matching method. New faces that do not match the faces in the database cannot be memorized and stored, which brings trouble to the work of investigating whether this face has appeared before, or needs to include this face into the recorded face database, and also needs to re-enter the person face related data

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