Timeliness and face quality considered filtering selection method and system

Abstract

The invention provides a timeliness and face quality considered filtering selection method and system. The filtering selection method comprises a receiving step, a feature extracting step, a quality scoring step, a quality rating step, a judging step, a storing step and a returning step. The timeliness and face quality considered filtering selection method provided by the invention has the beneficial effects that the filtering selection method adopts a supervised machine learning method based on real mass data to obtain a fuzzy evaluation standard of face image quality which is convenient for a machine to effectively retrieve, and in a certain period of time, a method to quickly decide and select high quality images is provided, which can reduce the invalid storage and search volume under the mass data; and a cascade configuration method is provided for rapid filter selection, which considers both the timeliness and image quality, and also improves the flexibility.

Description

technical field [0001] The invention relates to the technical field of face recognition, in particular to a filtering and selection method and system that take into account both real-time performance and face quality. Background technique [0002] Real-time acquisition and selection of high-quality faces from video streams has important practical value. The application of face recognition in real-time streams has great application value in the aspects of case control, arrest, and case warning. However, in real-time video with a large flow of people, the number of faces to be analyzed will increase exponentially, forming a massive analysis source. For a specific face, how to quickly select a higher-quality frame within a certain video time to form a higher-quality effective face data, and how to analyze and store it in a near-real-time manner in a massive face database An urgent problem to be solved in video surveillance system based on face recognition. [0003] The proble...

AI Technical Summary

Problems solved by technology

However, in practical applications, due to the complexity of calculating and comparing each frame of each face in a large-scale, thousands of channels of video one by one, the computing resources are limited, and the time interval in the video is usually selected. This usually results in a large number of collected face pictures, and the pictures collected for the same face will appear repeatedly within a period of time, and the collected pictures may not be correct due to reasons such as angle, size, light, and clarity. Provide high-quality face feature values ​​for search systems based on massive face data, and repeated pictures will also increase additional storage overhead

[0004] The existing technology is based on building a time-series face screenshot linked list for each face in the real-time video stream, which is usually returned to the main application in the form of a callback, but it is not flexible enough in terms of how to filter and make quick decisions

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