Face Recognition with Image Rotation Detection, Correction and Reinforced Decision using ANN
Rotation or tilt present in an image capture by digital
means can be detected and corrected using Artificial Neural Network
(ANN) for application with a Face Recognition System (FRS). Principal
Component Analysis (PCA) features of faces at different angles
are used to train an ANN which detects the rotation for an input image
and corrected using a set of operations implemented using another
system based on ANN. The work also deals with the recognition
of human faces with features from the foreheads, eyes, nose and
mouths as decision support entities of the system configured using
a Generalized Feed Forward Artificial Neural Network (GFFANN).
These features are combined to provide a reinforced decision for
verification of a person-s identity despite illumination variations. The
complete system performing facial image rotation detection, correction
and recognition using re-enforced decision support provides a
success rate in the higher 90s.
[1] C. B. Owen and F. Makedon: High Quality Alias Free Image Rotation,
Proceedings of 30th Asilomar Conference on Signals, Systems, and
Computers Pacific Grove, California, November 2-6, 1996.
[2] K. Delac, M. Grgic and P. Liatsisand: Appearance-based Statistical
Methods for Face Recognition, Proccedings of 47th International
Symposium ELMAR-2005, 08-10 June 2005, Zadar, Croatia.
[3] W. Y. Zhao and R. Chellappa: Image base face recognition: Issues
and Methods, Center for Automation Research, University of Maryland,
USA.
[4] S. Tamma: Face Recognition Techniques, Department of Computer
Science, University of New Mexico, Albuquerque, USA, Dec., 2002.
[5] K. Teng and J. Auwaerter: Face Recognition using Wavelet representations
obtained from different pruning strategies, Department of ECE,
Carnegie Mellon University, Pittsburgh, USA, 2005.
[6] H. A. Rowley, S. Baluja, and T. Kanade: Neural Network-Based
Face Detection, PAMI, January, 1998.
[7] A. Rda and B. Aoued: Artificial Neural Network-Based Face
Recognition, Proceedings of ISCCSP, 2006.
[8] V. Bhagavatula: Face Recognition using Correlation Filters, Data
Storage Systems Center (DSSC), Carnegie Mellon University, Pittsburgh,
PA, USA, 2007.
[9] S. Duda, P. E. Hart, D. G. Stork. Pattern Classification, 2nd Ed., John
Wiley, 2002.
[10] S. Haykin: Neural Networks A Comprehensive Foundation, 2nd ed.,
Pearson Education, New Delhi, 2003.
[1] C. B. Owen and F. Makedon: High Quality Alias Free Image Rotation,
Proceedings of 30th Asilomar Conference on Signals, Systems, and
Computers Pacific Grove, California, November 2-6, 1996.
[2] K. Delac, M. Grgic and P. Liatsisand: Appearance-based Statistical
Methods for Face Recognition, Proccedings of 47th International
Symposium ELMAR-2005, 08-10 June 2005, Zadar, Croatia.
[3] W. Y. Zhao and R. Chellappa: Image base face recognition: Issues
and Methods, Center for Automation Research, University of Maryland,
USA.
[4] S. Tamma: Face Recognition Techniques, Department of Computer
Science, University of New Mexico, Albuquerque, USA, Dec., 2002.
[5] K. Teng and J. Auwaerter: Face Recognition using Wavelet representations
obtained from different pruning strategies, Department of ECE,
Carnegie Mellon University, Pittsburgh, USA, 2005.
[6] H. A. Rowley, S. Baluja, and T. Kanade: Neural Network-Based
Face Detection, PAMI, January, 1998.
[7] A. Rda and B. Aoued: Artificial Neural Network-Based Face
Recognition, Proceedings of ISCCSP, 2006.
[8] V. Bhagavatula: Face Recognition using Correlation Filters, Data
Storage Systems Center (DSSC), Carnegie Mellon University, Pittsburgh,
PA, USA, 2007.
[9] S. Duda, P. E. Hart, D. G. Stork. Pattern Classification, 2nd Ed., John
Wiley, 2002.
[10] S. Haykin: Neural Networks A Comprehensive Foundation, 2nd ed.,
Pearson Education, New Delhi, 2003.
@article{"International Journal of Electrical, Electronic and Communication Sciences:63150", author = "Hemashree Bordoloi and Kandarpa Kumar Sarma", title = "Face Recognition with Image Rotation Detection, Correction and Reinforced Decision using ANN", abstract = "Rotation or tilt present in an image capture by digital
means can be detected and corrected using Artificial Neural Network
(ANN) for application with a Face Recognition System (FRS). Principal
Component Analysis (PCA) features of faces at different angles
are used to train an ANN which detects the rotation for an input image
and corrected using a set of operations implemented using another
system based on ANN. The work also deals with the recognition
of human faces with features from the foreheads, eyes, nose and
mouths as decision support entities of the system configured using
a Generalized Feed Forward Artificial Neural Network (GFFANN).
These features are combined to provide a reinforced decision for
verification of a person-s identity despite illumination variations. The
complete system performing facial image rotation detection, correction
and recognition using re-enforced decision support provides a
success rate in the higher 90s.", keywords = "Rotation, Face, Recognition, ANN.", volume = "3", number = "3", pages = "548-8", }