Mass face library retrieval method based on man-machine collaboration

Abstract

The invention discloses a massive face image library retrieval method based on man-machine collaboration. The low-level features extracted by computer vision are different from the high-level semantic features captured by human vision, so that the computer vision retrieval result cannot reach the expectation of people. Face images are small in difference in low-level contour features, and similar faces are difficult to distinguish only depending on computer vision. The method comprises the following steps: 1, establishing an image library; 2, training an EEG classification model by using an EEG signal generated by watching the portrait by a person; and 3, performing online iteration by using an EEG classification model, and retrieving a target image required by the testee from the image data. The face image retrieval method is different from a traditional face retrieval method, fast, accurate and robust massive face image retrieval is achieved by combining the powerful cognitive ability of the human brain with the fast computing ability and the massive storage ability of a computer, and the face image retrieval method has remarkable application value.

Description

technical field [0001] The invention belongs to the intersecting technical field of rapid sequence visual presentation and image retrieval, and in particular relates to a method for retrieving a massive face library based on human-machine collaboration. Background technique [0002] The existing video surveillance system only realizes the simple video storage function, and the efficient analysis and utilization of surveillance video information is still a problem. Therefore, in the video surveillance capture and recording of high-definition images, it provides identification, comparison, query to Designating human faces is the goal pursued by the new generation of video surveillance, and it is also an urgently needed function in practical applications. The rapid increase in the number of face images that can be obtained in the field of public security makes the existing face image retrieval technology face severe challenges in the process of processing massive image data. ...

AI Technical Summary

Problems solved by technology

However, there is a gap between the low-level features extracted by computer vision and the high-level semantic features captured by human vision, so the retrieval results of computer vision are far from meeting human expectations.

Face images have little difference in low-level contour features, and it is difficult to distinguish similar faces only by computer vision

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