Imagae Feature Based Annotation for Data Verification System

Authors

  • Priyanka Mankar  Department of Computer Sci. & Engineering, Engineering, Anjuman College of Engineering & Technology Technology Nagpur, Maharashtra, India
  • Shweta Meshram  Department of Computer Sci. & Engineering, Engineering, Anjuman College of Engineering & Technology Technology Nagpur, Maharashtra, India
  • Prof. Imteyaz Shahzad  Department of Computer Sci. & Engineering, Engineering, Anjuman College of Engineering & Technology Technology Nagpur, Maharashtra, India

Keywords:

Face Annotation, Content Based, face data, association.

Abstract

A face annotation has many applications the main part of based face annotation is to management of most same facial images and their weak data labels. This problem, different method are adopted. The efficiency of annotating systems are improved by using these methods. This paper proposes a review on various techniques used for detection and analysis of each technique. Combine techniques are used in retrieving facial images based on query. So it is effective to label the images with their exact names. The detected face recognition techniques can annotate the faces with exact data labels which will help to improve the detection more efficiently. For a set of semantically similar images Annotations from them. Then content-based search is performed on this set to retrieve visually similar images, annotations are mined from the data descriptions. The method is to find the face data association in images with data label. Specifically, the task of face-name association should obey the constraint face can be a data appearing in its associated a name can be given to at most one face and a face can be assigned to one name.

References

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Published

2018-01-30

Issue

Section

Research Articles

How to Cite

[1]
Priyanka Mankar, Shweta Meshram, Prof. Imteyaz Shahzad, " Imagae Feature Based Annotation for Data Verification System, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 4, Issue 3, pp.61-65, January-February-2018.