Single sample face recognition method based on face sparse descriptors

A sparse description and descriptor technology, applied in the field of image processing and pattern recognition research, can solve problems such as failure to meet the application requirements of face recognition, low accuracy, and limited application range

Inactive Publication Date: 2013-11-27
SUN YAT SEN UNIV
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  • Abstract
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AI Technical Summary

Problems solved by technology

2) In terms of similarity measurement, the original SIFT feature matching strategy has problems such as high time-consuming and low accuracy
The above two problems make the face recognition method based on SIFT operator fail to meet the actual application requirements of face recognition in terms of recognition performance and time efficiency, which limits the scope of application of this method

Method used

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  • Single sample face recognition method based on face sparse descriptors
  • Single sample face recognition method based on face sparse descriptors
  • Single sample face recognition method based on face sparse descriptors

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Embodiment 1

[0056] Such as figure 1 As shown, the single-sample face recognition method based on face sparse descriptor mainly includes the following steps:

[0057] S1: Perform alignment and normalization preprocessing on all face images used in the reference image set.

[0058] Preprocessing is performed first, so that the position in the reference image can be accurately corresponded to the position of the key point on the test image during the test.

[0059] S2: Calculate the FSD operator.

[0060] For each image, the FSD operator is calculated separately, and the specific steps include:

[0061] S21: key point positioning.

[0062] Including the following steps:

[0063] S211: Construct a set of scale spaces S(x,y,σ):

[0064] S(x,y,σ)=G(x,y,σ) * I(x,y).

[0065] where I(x,y) is the input image, G ( x , y , σ ) = 1 ...

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Abstract

The invention discloses a single sample face recognition method based on face sparse descriptors. The method includes the first step of carrying out alignment and normalized preprocessing on all available face images in a reference image set, the second step of calculating a group of scale spaces and differential scale spaces on each face image to carry out key point detection, the third step of selecting the local area using the key point as the center, carrying out statistics on the histogram of the area in the gradient direction, and using the histogram as the local feature description corresponding to the key point, the fourth step of calculating non-similarity measures of the images to be tested and each image in the reference image set on the basis of local match, and the fifth step of using the nearest neighbor classifier to carry out classification and recognition on the input images to be tested according to the non-similarity measures. The single sample face recognition method can quite effectively solve the problem of single sample face recognition on the conditions of sheltering, changing of expression and postures and the like.

Description

technical field [0001] The invention relates to the research fields of image processing and pattern recognition, in particular to a single-sample face recognition method based on face sparse descriptors. Background technique [0002] Face recognition is one of the most important tasks in biometric recognition. Its purpose is to automatically identify or confirm the identity of the person in the input static image or dynamic video through the registered face database. At present, face recognition technology has been widely used in public security, access control systems, border security, entertainment and social networking. [0003] However, most of the current research is based on statistical features to improve the recognition rate, which requires the establishment of a training and learning mechanism, and the establishment of this learning mechanism requires more training samples, but in many practical applications, such as In the identification of driver's license or pas...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
Inventor 赖剑煌刘娜郑伟诗
Owner SUN YAT SEN UNIV
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