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
View PDF5 Cites 27 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • 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

Examples

Experimental program
Comparison scheme
Effect test

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 , ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

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

technical field [0001] The invention relates to the fields of digital image processing and computer vision, in particular to a video face recognition algorithm. Background technique [0002] Video face recognition has become a research hotspot and difficult problem in the field of computer vision in recent years. With the development of the Internet of Things and network security, it has broad application prospects. Compared with static images, the feature information that can be selected in dynamic videos is more abundant and diverse. For example, the temporal dynamic information of videos helps to improve the recognition rate; images with relatively high resolution can be selected from video sequences to improve recognition performance. ; The 3D model of the target can also be reconstructed through video learning, and the target recognition can be realized efficiently by using these models. In summary, temporal and motion information play a crucial role in video-based obj...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/00
Inventor 陈海鹏申铉京王玉吕颖达王子瑜徐浩然
Owner JILIN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products