Identity identification method based on self-established sample library and composite characters in video monitoring

A technology of identity recognition and video surveillance, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problems of low recognition accuracy, avoid false recognition or unrecognizable, and reduce the possibility of low recognition rate Effect

Active Publication Date: 2014-07-23
SHANGHAI FUKONG HUALONG MICROSYST TECH
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Problems solved by technology

In addition, as mentioned above, the recognition accuracy rate of using gait features for identity recognition is significantly lower than that of face feature recognition. The reason is that gait recognition belongs to the category of dynamic feature recognition, and at least one continuous gait cycle image is required for feature description. In the process of extracting the gait profile, it is necessary to bridge the different influences of the background difference of the gait sample background and the background difference in the gait image sequence to be recognized on the gait feature description

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  • Identity identification method based on self-established sample library and composite characters in video monitoring
  • Identity identification method based on self-established sample library and composite characters in video monitoring
  • Identity identification method based on self-established sample library and composite characters in video monitoring

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[0046] In order to make the technical means, creative features, goals and effects achieved by the present invention easy to understand, the present invention will be further described below in conjunction with specific illustrations.

[0047] For video surveillance, the present invention adopts an identity recognition method based on a self-established sample library and mixed features. This method detects the input image sequence through a pre-trained face and pedestrian classifier, and uses human-computer interaction to detect the detected The frontal face samples and side gait samples are classified and identified as samples for "training" in the recognition stage, and then these samples are used to extract face or gait features to train the recognizer, and other detected samples are classified and identified. If If there is a face or gait that cannot be classified and identified, the user will be asked to identify it, and the identified face sample or gait sample will be ad...

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Abstract

The invention discloses an identity identification method based on self-established sample libraries and composite characters in video monitoring. The method comprises the steps that firstly, preprocessing is conducted on an acquired video, foreground detection is conducted, so that moving object information is obtained, then face detection is conducted on the basis of the moving object information, the detected face is identified, if the currently detected face can not be identified, a user is inquired so as to identify the detected face, and the identified face is added to the face sample library; if the face can not be detected in foreground information, pedestrian detection is conducted, a detected pedestrian is traced, gait period detection is conducted on a traced pedestrian image sequence, features of detected gait information of a period are extracted and identified, if identification fails, gaits are classified in the same mode of user identification and added to the gait sample library. The identity identification method provides a solution for identity identification on the condition of a lack of training sample diversity or small samples.

Description

technical field [0001] The invention relates to the technical fields of digital image processing, pattern recognition and machine learning, in particular to an identity recognition method based on a self-established sample library and mixed features. Background technique [0002] Biometric identification refers to a technology that uses the inherent physiological or behavioral characteristics of a person to process them by a computer to identify a person's identity. Biological characteristics are divided into physiological characteristics and behavioral characteristics. Physiological characteristics mainly include face, fingerprint, hand shape, palm print, ear shape, DNA, iris, retina, skeleton, etc. Behavioral features include signature movements, keystroke rhythms, speaking sounds, walking postures, etc. Among them, face recognition of physiological characteristics and gait recognition of behavioral characteristics have become the main methods for identification in video...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/66G06K9/54
Inventor 张然然廖小勇杨松绍罗友军徐家君
Owner SHANGHAI FUKONG HUALONG MICROSYST TECH
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