Near infrared face recognition by combining Zernike moments and undecimated discrete wavelet transform

0428536 - ÚTIA 2015 RIV NL eng J - Journal Article
Farokhi, Sajad - Shamsuddin, S.M. - Sheikh, U.U. - Flusser, Jan - Khansari, M. - Jafari-Khouzani, K.
Near infrared face recognition by combining Zernike moments and undecimated discrete wavelet transform.
Digital Signal Processing. Roč. 31, č. 1 (2014), s. 13-27. ISSN 1051-2004. E-ISSN 1095-4333
R&D Projects: GA ČR GAP103/11/1552
Institutional support: RVO:67985556
Keywords : Zernike moments * Undecimated discrete wavelet transform * Decision fusion * Near infrared * Face recognition
Subject RIV: JD - Computer Applications, Robotics
Impact factor: 1.256, year: 2014
http://library.utia.cas.cz/separaty/2014/ZOI/flusser-0428536.pdf

This study proposes a novel near infrared face recognition algorithm based on a combination of both local and global features. In this method local features are extracted from partitioned images by means of undecimated discrete wavelet transform (UDWT) and global features are extracted from the whole face image by means of Zernike moments (ZMs). Spectral regression discriminant analysis (SRDA) is then used to reduce the dimension of features. In order to make full use of global and local features and further improve the performance, a decision fusion technique is employed by using weighted sum rule. Experiments conducted on CASIA NIR database and PolyU-NIRFD database indicate that the proposed method has superior overall performance compared to some other methods in the presence of facial expressions, eyeglasses, head rotation, image noise and misalignments. Moreover its computational time is acceptable for on-line face recognition systems.
Permanent Link: http://hdl.handle.net/11104/0235481