Video face identification algorithm on basis of multi-instance learning

A multi-instance learning and face recognition technology, which is applied in the fields of digital image processing and computer vision, can solve the problems of difficult to accurately locate key frames and low recognition rate, and achieve difficult selection, strong anti-interference ability, and high recognition accuracy. Effect

Active Publication Date: 2015-07-15
JILIN UNIV
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AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to propose a video face recognition method based on multi-example learning in order to solv

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  • Video face identification algorithm on basis of multi-instance learning
  • Video face identification algorithm on basis of multi-instance learning
  • Video face identification algorithm on basis of multi-instance learning

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Embodiment Construction

[0015] Below in conjunction with accompanying drawing, a kind of color image segmentation method based on histogram that the present invention proposes is described in detail:

[0016] like figure 1 Shown, the video face recognition method of the present invention, its steps are as follows:

[0017] Combine below figure 1 The multi-instance learning-based video face recognition algorithm of the present invention is described in detail.

[0018] First, the preprocessing of the face image is carried out. Gabor wavelet transform can extract the multi-scale and multi-directional local frequency information of the image, can enhance some key features, and has good characteristics in extracting the local spatial frequency domain information of the target. In the field of face recognition, Gabor transform has been widely used.

[0019] The two-dimensional Gabor wavelet function is defined as:

[0020] ψ μ , ...

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Abstract

The invention discloses a video face identification algorithm on the basis of multi-instance learning. According to the algorithm, each face video is used as a packet; normalized face frame images in the videos are used as instances in the packets; a blocked local binary pattern cascaded histogram on the basis of weighting is used as an instance feature; in a multi-instance feature space of a training set, a multi-instance learning algorithm is adopted to obtain a classifier so as to realize classification and prediction on a tested sample. By related experiments in a face video database, the algorithm obtains high identification accuracy; meanwhile, the algorithm has excellent robustness for illumination variation, expression variation and the like and effectiveness of the algorithm of verified.

Description

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Claims

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Application Information

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Owner JILIN UNIV
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