Multispectral palmprint recognition methodology based on multiscale representation

Abstract

The increasing demand to develop a palmprint biometric system with a low-error rate has prompted scientists to use multispectral imaging to overcome the limits of the techniques that act in visible light. In order to improve the accuracy of multispectral palmprint recognition, we explore two level fusions: pixel and the feature level fusion approaches. The former is based on a maximum selection rule, which combines discriminating information from different spectral bands of discrete wavelet transform of multispectral images. The latter operates the fusion of features extracted from subimages. We propose to use both approaches for statistical and energy distribution analysis of the finite ridgelet transform coefficients, for the sake of their simplicity and low-computational complexity. Once the feature vectors are obtained, we perform a robust classification to identify/verify individuals with both approaches. The effectiveness of the proposed methods is evaluated on several classifiers for binary and multiclass cases. The experimental results conducted on Chinese Academy of Sciences Institute of Automation and Hong Kong Polytechnic University databases show that the proposed approaches ensure, respectively, an accuracy rate of 100% and 99.79%. A comparative study has revealed that our approach outperforms or at least equals the performances of the state-of-the-art multispectral palmprint recognition methods. Bouchemha et al.: Multispectral palmprint recognition methodology based on multiscale representation Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 08/03/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Journal of Electronic Imaging 043005-3 Jul∕Aug 2015 • Vol. 24(4) Bouchemha et al.: Multispectral palmprint recognition methodology based on multiscale representation Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 08/03/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Amel Bouchemha received her engineer degree in electrical engineering from University of Constantine in 1992, then her DEA degree in signal processing. In 1999, she received her magister degree from the University of Constantine. Since 2001, she has been working as a researcher and associate professor at the University of Tebessa, Algeria. Her principal research interests include biometric, signal processing, image processing, modeling, and fault detection. Bouchemha et al.: Multispectral palmprint recognition methodology based on multiscale representation Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 08/03/2015 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

Multispectral image fusion for illumination-invariant palmprint recognition

“…Image fusion aims to combine the complementary information of multisource images and make the fused image more understandable and purposeful. For multispectral palmprint recognition [ 9 – 12 ], the task of image fusion is to reserve the useful features and remove the confusing identity information in each fusion component so that the images can be separated perfectly in the fusion space. For this purpose, an improved weighted Fisher criterion is applied to the BIMFs extracted from multispectral images.…”

“…In addition, traditional methods obtain features from a single spectral band and consequently cannot achieve enough discriminative information of identities. In recent researches, there is a growing trend to use multispectral images instead of exploiting a single spectral image to improve the accuracy of a palmprint recognition system [ 9 – 12 ]. Images are captured at Blue, Green, Red and Near-infrared (NIR) spectral bands respectively, each of which commonly highlights different specific and complementary palm features.…”

“…Qualitative analysis demonstrated that the CT based image fusion could achieve a higher recognition accuracy. Besides, some other innovative methods, such as nonsubsampled contourlet transform (NSCT) [ 14 , 15 ], discrete wavelet transform (DWT) [ 11 , 12 , 16 ], shift-invariant digital wavelet transform (SIDWT) [ 17 , 18 ] and digital shearlet transform (DST) [ 19 , 20 ], were widely and successfully used in multispectral palmprint image fusion. Alternatively, in the case of fusion at matching score level, palmprint features are extracted from different spectral bands separately, followed by a comparator to obtain a matching score.…”

Region of Interest Extraction using K-Means and Edge Detection for DEXA Images

“…A crucial tool for decision-making and treatment procedures in healthcare is now digital medical images [1]- [3]. Determining the area of interest in medical images is one of the most important basic operations in diagnostic systems [4], [5], and the reason is due to the fact that most images contain useless areas [6], [7]. For example, the size of the background and the object differ from one image to another, as the image that is close to the scanner occupies the largest part.…”

Learning Hand-Crafted Palm-Features for a High-Performance Biometric Systems

“…With this in mind, Hassner et al [5] propose a new variety of LBP, called Three-Patch LBP (TP-LBP) and Four-Patch LBP (FP-LBP), to labeled faces in the Wild image set. Also, in their work, Bouchemha et al [6] attempted to extract critical information from multispectral palmprint images using dynamic and statistic features based on ridgelet transform and the parameters of the Gray-Level Co-occurrence Matrix (GLCM). Finally, it should be noted that several methods combine these features to increase the performance of multispectral/hyperspectral palmprint recognition systems.…”