Open AccessSparse representation algorithm for fusion error
Author(s) -
Zijun Li
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1871/1/012120
Subject(s) - facial recognition system , sparse approximation , computer science , artificial intelligence , pattern recognition (psychology) , field (mathematics) , face (sociological concept) , representation (politics) , algorithm , machine learning , mathematics , law , social science , sociology , politics , political science , pure mathematics
In recent years, face recognition technology has been widely used in security, finance, media data retrieval and other fields due to its convenience, concealment, and stability, and has become a popular research field in various scientific research institutions. Among them, face recognition under complex conditions under different lighting, different expressions, and partial occlusion has become a research hotspot. Sparse representation algorithm, as the mainstream algorithm to solve the problem of face recognition under complex conditions, although it improves the recognition accuracy compared with other algorithms, there is still room for improvement. Aiming at the partial occlusion problem in face recognition, this paper conducted many experiments under different training samples through the sparse representation algorithm of fusion error. The experimental results show that the algorithm improves the recognition rate and provides a new solution for the problem of face recognition under partial occlusion.