Large scale database face recognition based on region constriction

Abstract

To increase face recognition speed and reduce RAM occupancy on a large-scale database,a high-efficient face recognition method was proposed.By introducing the technique of Discrete Cosine Transform(DC) image data compression into the face feature database compression,the database was compressed in multi-level compression ratio to generate few compressed sub-databases.Then,according to the order from high-to-low compression ratio,the rough face recognition was implemented on these sub-databases one by one.In the meantime,the recognition scope was narrowed down progressively according to previous recognition results.Finally,the accurate face recognition was carried out on the original uncompressed database in a very small range.Compared to traditional method,the experimental results show that the recognition time is reduced to 29.2%,RAM occupancy is reduced to 10.2%,and hard disk resource consumption is increased by only 11%,and the recognition rate is not significantly reduced.