Face Recognition With Comprehensive Data Processing Method And Application Framework Research

In a service supported society surrounded by multimedia format Social-Info, further development on video source utility and semantic abstraction is always a hot spot of scientific concerning and social significance. But at this stage on such research it is by far neither prevalent for industry using such as in video detection nor in web multimedia information extraction. A series of comprehensive problem make it bearing both troubling and charming style a unique field to testify researches from every stage of time and viewpoints, wherein System Theory, Image Processing, Characteristic Solution, Decision Modeling, Asynchronized Business Logic Ware and Common Data Structure API is asking for their overall ability of scientific operation. A sensible face recognition solution should be ready for regional change to meet most theory's appending demand at large, and it should be appended by most useful historic research products, optimizing them in to enrich the system. In this paper, the author introduce a system based on the personal understanding of modern image processing and face recognition field, giving explanation on every main stage of image pre-processing, characteristic extraction, multi-Assembler, embodying classic image semantic understanding and pattern recognition, and to result accompanied by multi-Assembler as LSM optimization decision. In system designing part, device API, video signal API as encoding adapter ware, generic type interface as BLL indexing data structure, pattern query in Oracle or SQL Server 2005 as DAL, image template transformation, sub-image matching, computing space mapping, and UI design for every different case in practical using. Some apart from issue in face recognition, simplicity for data piping and full depth of data mining are also highlighted, which needs author to appreciate any impossible research product and invoke them to help enhancing system's performance. Consideration fills in every processing state to try to get possible optimized recognition results.Theoretically, Face recognition from a video is a capturing process from complex facial 2D-topography due to the partial or flexible performance measure human faces give to, thus identities probably break as time proceeds. This context systematically evaluates how identities of time-variant probe series be executed in real-time 3D registration model, and how the determined identities chase to background gallery, shrinking record area via one-dimensional cascading indices first and then combining to perform multidimensional restraint focuses, finally which method be the best for final validation. Establishment of first-order performance list with a threshold makes the inter-probe identity source optimally exploited.