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Three-dimensional ear recognition based on block statistic features and dictionary learning sparse representation classification

A technology of dictionary learning and statistical features, applied in the field of human ear recognition

Active Publication Date: 2015-11-18
TONGJI UNIV
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Problems solved by technology

Aiming at this problem, the present invention adopts the algorithm of dictionary learning and sparse representation classification to solve the one-to-many recognition problem

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  • Three-dimensional ear recognition based on block statistic features and dictionary learning sparse representation classification
  • Three-dimensional ear recognition based on block statistic features and dictionary learning sparse representation classification
  • Three-dimensional ear recognition based on block statistic features and dictionary learning sparse representation classification

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Embodiment Construction

[0051] The present invention will be further described below in conjunction with the embodiments shown in the accompanying drawings.

[0052] Aiming at the problem that in the previous 3D human ear matching process, the human ears to be verified and the sample data sets in the database need to be registered and matched one by one, and the efficiency is greatly reduced as the capacity of the sample data sets increases. The present invention adopts dictionary learning and sparse representation The classification algorithm performs one-to-many recognition of the three-dimensional human ears to be measured; for the small alignment errors that still exist after the registration and matching of the three-dimensional data of the human ears, the present invention adopts a description operator based on block statistical features to establish an accurate and fast 3D human ear recognition method, its specific workflow is as follows figure 1 Shown:

[0053] (1) Determine the three-dimens...

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Abstract

The invention discloses a three-dimensional ear recognition method based on block statistic features and dictionary learning sparse representation classification. A three-dimensional ear sampling image is equally divided into a plurality of sub regions; as for each sub region, an ear surface type is firstly calculated, a histogram is then used for counting the ear surface type in each sub region, surface type histograms of all sub regions are finally spliced to serve as a feature description operator for the ear depth image, a dictionary learning sparse representation framework is used for classification, and thus the recognition efficiency and the accuracy are improved. The method of the invention can be used in situations with strict requirements on identity recognition, and can effectively solve the alignment deviation problem among multiple three-dimensional ear sampling.

Description

technical field [0001] The invention belongs to the field of pattern recognition, and relates to a method for verifying identity information, in particular to a method for human ear recognition. Background technique [0002] With the continuous development of information technology, scholars at home and abroad and many technology companies are keen to improve the verification effect of identity information to meet the needs of identification in many different occasions such as access control, customs clearance, and stadium security in real life. The exacting demands of human identity. Methods based on biometric identification are receiving more and more attention. Among them, the human ear, as an emerging biological feature in the field of biometric identification, has been extensively studied in recent years, and experiments have proved that the human ear is an excellent biological feature. The human ear includes rich structures and special shapes, and has many characteri...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00
CPCG06V20/653G06V40/10
Inventor 张林李力达沈莹李宏宇
Owner TONGJI UNIV
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