Local Binary Patterns (LBP) have been well exploited for facial image analysis recently. In the existing work, the LBP histograms are extracted from local facial regions, and used as a whole for the regional description. However, not all bins in the LBP histogram are necessary to be useful for facial representation. In this paper, we propose to learn discriminative LBP-Histogram (LBPH) bins for...

Recently, a nonparametric approach to texture analysis has been developed, in which the distributions of simple texture measures based on local binary patterns (LBP) are used for texture description. The basic LBP encodes 256 simple feature detectors in a single 3x3 operator. This paper shows that a properly selected subset of patterns encoded in LBP forms an efficient and robust texture descri...

In this paper we propose two novel rotation invariant local texture descriptors. They are based on Local Binary Pattern (LBP), which is one of the most effective and frequently used texture descriptor. Although LBP efficiently captures the local structure, it is not rotation invariant. In the proposed methods, a dominant direction is evaluated in a circular neighbourhood and the descriptor is c...

This paper presents a novel method for facial expression recognition that employs the combination of two different feature sets in an ensemble approach. A pool of base support vector machine classifiers is created using Gabor filters and Local Binary Patterns. Then a multi-objective genetic algorithm is used to search for the best ensemble using as objective functions the minimization of both t...

Gender recognition problem has not been extensively studied in situations where the face cannot be accurately detected and it also can be partially occluded. In this contribution, a comparison of several characterisation methods of the face is presented and they are evaluated in four different experiments that simulate the previous scenario. Two of the characterisation techniques are based on h...

Significant progress has been made in automatic facial expression analysis using facial images and videos. The recognition reliability of most current approaches is still poor in naturalistic expressions compared to acted ones. Most of these methods use a static image of each expression that captures the characteristic image at the apex. However, according to psychologists, analyzing a sequence...

This paper presents a new countermeasure for the protection of automatic speaker verification systems from spoofed, converted voice signals. The new countermeasure is based on the analysis of a sequence of acoustic feature vectors using Local Binary Patterns (LBPs). Compared to existing approaches the new countermeasure is less reliant on prior knowledge and affords robust protection from not o...

This paper aims to investigate whether micro-facial movement sequences can be distinguished from neutral face sequences. As a micro-facial movement tends to be very quick and subtle, classifying when a movement occurs compared to the face without movement can be a challenging computer vision problem. Using local binary patterns on three orthogonal planes and Gaussian derivatives, local features...

In conventional cropping systems, removal of weed population extensively relies on the application of chemical herbicides. However, this practice should be minimized because of the adverse effects of herbicide applications on environment, human health, and other living organisms. In this context, if the distribution of broadleaf and grass weeds could be sensed locally with a machine vision syst...

Face Recognition has been studied for many decades. As opposed to traditional hand-crafted features such as LBP and HOG, much more sophisticated features can be learned automatically by deep learning methods in a data-driven way. In this paper, we propose a two-stage approach that combines a multi-patch deep CNN and deep metric learning, which extracts low dimensional but very discriminative fe...