Weighted statistical binary patterns for facial feature representation

Abstract

We present a novel framework for efficient and robust facial feature representation based upon Local Binary Pattern (LBP), called Weighted Statistical Binary Pattern, wherein the descriptors utilize the straight-line topology along with different directions. The input image is initially divided into mean and variance moments. A new variance moment, which contains distinctive facial features, is prepared by extracting root k-th. Then, when Sign and Magnitude components along four different directions using the mean moment are constructed, a weighting approach according to the new variance is applied to each component. Finally, the weighted histograms of Sign and Magnitude components are concatenated to build a novel histogram of Complementary LBP along with different directions. A comprehensive evaluation using six public face datasets suggests that the present framework outperforms the state-of-the-art methods and achieves 98.51% for ORL, 98.72% for YALE, 98.83% for Caltech, 99.52% for AR, 94.78% for FERET, and 99.07% for KDEF in terms of accuracy, respectively. The influence of color spaces and the issue of degraded images are also analyzed with our descriptors. Such a result with theoretical underpinning confirms that our descriptors are robust against noise, illumination variation, diverse facial expressions, and head poses.

Toward Deep-Learning-Based Methods in Image Forgery Detection: A Survey

“…During preprocessing, the input images might be converted to the frequency domain using methods such as discrete cosine transform (DCT) [4], [78], [79] or discrete wavelet transform (DWT) [27], or they may be converted to another color space, such as YC b C r . Various types of image features, such as scale-invariant feature transform (SIFT) [1], [2], [12], [80], speeded-up robust features (SURF) [81], [82], [83], local binary pattern (LBP) [4], [84], and Zenike moments [2], were extracted in block-based or keypoint-based methods. Usually, the final stages are feature matching and filtering to generate detected heatmaps.…”

Radial Orthogonal Median LBP (ROM-LBP): A Discriminant Local Descriptor in Light Variations for Face Recognition

“…Truong et al [11] developed novel descriptor for Face Recognition (FR) so-called Weighted Statistical Binary Patterns (WSBP). In WSBP, there is utilization of straight line topology across distinct directions for developing feature size.…”

“…Table 1 The merits and demerits of the work reported in literature Techniques & References Merits Demerits WSBP [11] Different local and global methods are outclassed by the WSBP.…”

“…Therefore ROM-LBP forms the size of 288. ROM-LBP concept is defined in Equations ( 7)- (11). In Equations ( 7) and (9) D and S are limit (of neighbors) and radius and M c is the center pixel.…”

Face spoofing detection and Authentication Using Linear Binary Patterns, Gabor Features and Support Vector Machine

“…The work submitted by Arashloo et.al [18] presented a problem solving and formulation protocol for a number of evaluation scenariopropositions. The suggested protocol compensates for the realistic condition associated with the new ideas of spoofing.…”