Three-dimensional face recognition method based on expression invariant regions

Abstract

The invention discloses a three-dimensional face recognition method based on expression invariant regions. Firstly a two-dimensional face region is obtained from two-dimensional face images corresponding to three-dimensional face data through detection; an initial three-dimensional face region is extracted in the three-dimensional face data according to the two-dimensional face region, transverse slicing is performed on the initial three-dimensional face region, and a nose tip point is detected; a relatively accurate three-dimensional face region is extracted according to the nose tip point, and then statistical characteristic vectors and the expression invariant regions are extracted in the three-dimensional face region; the statistical characteristic vectors of a check sample act as the check samples of a reject classifier; and the candidate check samples are obtained according to the statistical characteristic vectors of samples to be recognized, then the set of points of the expression invariant regions of the samples to be recognized are matched with the set of points of the expression invariant regions of the candidate check samples, and a recognition result is obtained according to the matching error. Accuracy of the three-dimensional face region can be enhanced, and accuracy of three-dimensional face recognition is enhanced via combination of the statistical characteristic vectors and the expression invariant regions.

Description

technical field [0001] The invention belongs to the technical field of three-dimensional face recognition, and more specifically relates to a three-dimensional face recognition method based on an expression-invariant region. Background technique [0002] Since the 1990s, face recognition has been developed for more than 20 years. The initial focus of face recognition research focused on face recognition on two-dimensional images. After the advancement of related research, two-dimensional face recognition has been able to achieve a good recognition rate under constraints such as limited lighting angles, postures, and expressions. The application scenarios under simple conditions are satisfied. With the advancement of research, face recognition under non-ideal conditions has become a relevant research hotspot, but the recognition rate under complex conditions has not made great progress. With the development of 3D scanning technology, 3D data acquisition has become easier an...

AI Technical Summary

Problems solved by technology

However, the current multi-modal recognition method has low accuracy when extracting expression-invariant regions, so there are still deficiencies in eliminating the influence of expression changes.

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