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...

A novel real-time local visual feature, namely FAST+LBP, is proposed in this paper for omnidirectional vision. It combines the advantages of two computationally simple operators by using Features from Accelerated Segment Test (FAST) as the feature detector and Local Binary Patterns (LBP) operator as the feature descriptor. The matching experiments of the panoramic images from the COLD database ...

We present a method using facial attributes for continuous authentication of smartphone users. We train a bunch of binary attribute classifiers which provide compact visual descriptions of faces. The learned classifiers are applied to the image of the current user of a mobile device to extract the attributes and then authentication is done by simply comparing the calculated attributes with the ...

A new method for action recognition is proposed by revisiting LBP-based dynamic texture operators. It captures the similarity of motion around keypoints tracked by a realtime semi-dense point tracking method. The use of self-similarity operator allows to highlight the geometric shape of rigid parts of foreground object in a video sequence. Inheriting from the efficient representation of LBP-bas...