Abstract
Abstract In face recognition tasks, one kind of feature set is not adequate to generate superior results; thus, selection and combination of complementary features are crucial steps. In this paper, the fusion of two useful descriptors, i.e., the Zernike moments (ZMs) and the local binary pattern (LBP)/local ternary pattern (LTP), has been proposed. The ZM descriptor consists of good global image representation capabilities besides being invariant to image rotation and noise, while the LBP/LTP descriptors capture the innate details within some local parts of face image and are insensitive to illumination variations. The fusion of these two is observed to incorporate the traits of both of these individual descriptors. Subsequently, in this work, the performance of diverse feature sets of ZMs (i.e., magnitude features, magnitude plus phase features, and the real plus imaginary component features) combined with the LBP/LTP descriptor is analyzed on FERET, Yale, and ORL face databases. The recognition results achieved by the proposed method are approximately 10 to 30% higher than those obtained with these descriptors separately. Recognition rates of the proposed method are also found to be significantly better (i.e., by 8 to 24%) in case of single example image per person in the training.
Highlights
In recent times, face recognition has become one of the widely used biometric techniques having a number of realworld applications like human-computer interaction, surveillance, authentication, computer vision applications, computer user interfaces, etc
6 Conclusions This paper proposes the fusion of two useful feature sets, i.e., the global Zernike moments (ZMs) and the local local binary pattern (LBP)/local ternary pattern (LTP) descriptor
The ZM and LBP/ LTP descriptors are observed to be very effective in providing good recognition performance on the face images containing certain variations
Summary
Face recognition has become one of the widely used biometric techniques having a number of realworld applications like human-computer interaction, surveillance, authentication, computer vision applications, computer user interfaces, etc. Wong et al have proposed dual optimal multiband feature (DOMF) method for face recognition in which wavelet packet transform (WPT) decomposes the image into frequency subbands and the multiband feature fusion technique is incorporated to select optimal multiband feature sets that are invariant to illumination and facial expression. Liu and Liu [26] have proposed an approach for face recognition that fuses color and local spatial and global frequency information This method is composed of multiple features of face images derived from LBP, DCT, hybrid color space, and the Gabor image representation. In this paper, a fusion of two complementary feature sets is proposed, where the global information of the face images is extracted by the ZM descriptor employing its rotation invariance characteristic, while the LBP/ LTP descriptor captures the significant local information.
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