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  Published Paper Details:

  Paper Title

ATTENDANCE SYSTEM USING CASCADE CLASSIFIER

  Authors

  Abdul Azeem,  Ankit Verma,  Akansha Bhatnagar,  Harsh Choudhary,  Kanchan Singh

  Keywords

Image processing, Expressions, HAAR features, HOG features, Face Detection Method, Face Recognition Method, Features Extraction, Features Matching, Query Image

  Abstract


Various organisations like schools, colleges, companies, etc., require to keep the daily record of their students/employees. Manual attendance maintaining is difficult process, especially for large group of students. Traditional face recognition systems employ methods to identify a face from the given input but the results are not usually accurate and precise as desired. Some automated systems developed to overcome these difficulties but they have drawbacks like cost, fake attendance, accuracy, intrusiveness, etc. To overcome these drawbacks, there is need of smart and automated attendance system. In this paper, we present Attendance System by using cascade classifier with real time video as an input. The working method of this system is entirely divided into six main modules. System will recognize one or more people captured in that video, this is taken as an output of this system. After recognition the attendance will marked and saved in database.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2005097

  Paper ID - 194367

  Page Number(s) - 714-717

  Pubished in - Volume 8 | Issue 5 | May 2020

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Abdul Azeem,  Ankit Verma,  Akansha Bhatnagar,  Harsh Choudhary,  Kanchan Singh,   "ATTENDANCE SYSTEM USING CASCADE CLASSIFIER", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.8, Issue 5, pp.714-717, May 2020, Available at :http://www.ijcrt.org/papers/IJCRT2005097.pdf

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ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


ISSN
ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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