Detecting and Recognizing the Face and Iris Features from a Video Sequence using DBPNN and Adaptive Hamming Distance

1
S. Revathy
S. Revathy

Send Message

To: Author

GJRE Volume 16 Issue F5

Article Fingerprint

ReserarchID

CV18Z

Detecting and Recognizing the Face and Iris Features from a Video Sequence using DBPNN and Adaptive Hamming Distance Banner
  • English
  • Afrikaans
  • Albanian
  • Amharic
  • Arabic
  • Armenian
  • Azerbaijani
  • Basque
  • Belarusian
  • Bengali
  • Bosnian
  • Bulgarian
  • Catalan
  • Cebuano
  • Chichewa
  • Chinese (Simplified)
  • Chinese (Traditional)
  • Corsican
  • Croatian
  • Czech
  • Danish
  • Dutch
  • Esperanto
  • Estonian
  • Filipino
  • Finnish
  • French
  • Frisian
  • Galician
  • Georgian
  • German
  • Greek
  • Gujarati
  • Haitian Creole
  • Hausa
  • Hawaiian
  • Hebrew
  • Hindi
  • Hmong
  • Hungarian
  • Icelandic
  • Igbo
  • Indonesian
  • Irish
  • Italian
  • Japanese
  • Javanese
  • Kannada
  • Kazakh
  • Khmer
  • Korean
  • Kurdish (Kurmanji)
  • Kyrgyz
  • Lao
  • Latin
  • Latvian
  • Lithuanian
  • Luxembourgish
  • Macedonian
  • Malagasy
  • Malay
  • Malayalam
  • Maltese
  • Maori
  • Marathi
  • Mongolian
  • Myanmar (Burmese)
  • Nepali
  • Norwegian
  • Pashto
  • Persian
  • Polish
  • Portuguese
  • Punjabi
  • Romanian
  • Russian
  • Samoan
  • Scots Gaelic
  • Serbian
  • Sesotho
  • Shona
  • Sindhi
  • Sinhala
  • Slovak
  • Slovenian
  • Somali
  • Spanish
  • Sundanese
  • Swahili
  • Swedish
  • Tajik
  • Tamil
  • Telugu
  • Thai
  • Turkish
  • Ukrainian
  • Urdu
  • Uzbek
  • Vietnamese
  • Welsh
  • Xhosa
  • Yiddish
  • Yoruba
  • Zulu

Dense feature extraction is becoming increasingly popular in face recognition. Face recognition is a vital component for authorization and security. In earlier days, CCA (Canonical Correlation Analysis) and SIFT (Scale Invariant Feature Transforms) was used for face recognition. Since multi scale extraction is not possible with these existing methods, a new approach to dense feature extraction is developed in this project. The proposed method combines dense feature extraction and decision based propagation neural network (DBPNN). Neural network algorithm is presented to recognize the face at different angle, and it is used for training and learning and leading to efficient and robust face recognition. Finally Iris matching is done by using Iterative randomized Hough transform for detecting the pupil region with number of iteration counts. Experimental results show that the proposed method is providing effective recognition rate with accuracy in comparing with existing methods.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

S. Revathy. 2016. \u201cDetecting and Recognizing the Face and Iris Features from a Video Sequence using DBPNN and Adaptive Hamming Distance\u201d. Global Journal of Research in Engineering - F: Electrical & Electronic GJRE-F Volume 16 (GJRE Volume 16 Issue F5): .

Download Citation

Journal Specifications

Crossref Journal DOI 10.17406/gjre

Print ISSN 0975-5861

e-ISSN 2249-4596

Keywords
Classification
GJRE-F Classification: FOR Code: 290903
Version of record

v1.2

Issue date

September 24, 2016

Language

English

Experiance in AR

The methods for personal identification and authentication are no exception.

Read in 3D

The methods for personal identification and authentication are no exception.

Article Matrices
Total Views: 3731
Total Downloads: 1768
2026 Trends
Research Identity (RIN)
Related Research

Published Article

Dense feature extraction is becoming increasingly popular in face recognition. Face recognition is a vital component for authorization and security. In earlier days, CCA (Canonical Correlation Analysis) and SIFT (Scale Invariant Feature Transforms) was used for face recognition. Since multi scale extraction is not possible with these existing methods, a new approach to dense feature extraction is developed in this project. The proposed method combines dense feature extraction and decision based propagation neural network (DBPNN). Neural network algorithm is presented to recognize the face at different angle, and it is used for training and learning and leading to efficient and robust face recognition. Finally Iris matching is done by using Iterative randomized Hough transform for detecting the pupil region with number of iteration counts. Experimental results show that the proposed method is providing effective recognition rate with accuracy in comparing with existing methods.

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]
×

This Page is Under Development

We are currently updating this article page for a better experience.

Request Access

Please fill out the form below to request access to this research paper. Your request will be reviewed by the editorial or author team.
X

Quote and Order Details

Contact Person

Invoice Address

Notes or Comments

This is the heading

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

High-quality academic research articles on global topics and journals.

Detecting and Recognizing the Face and Iris Features from a Video Sequence using DBPNN and Adaptive Hamming Distance

S. Revathy
S. Revathy

Research Journals