Traditionally, iris recognition systems capture iris images in the 700 to 900nm range. It is within these ranges that researchers have found the most viable iris textures for iris recognition. Recently, there has been an interest for exploration of spectrum ranges that falls outside of these traditional ranges. In this work, we will explore the performance of feature extraction techniques on a ...

One of the most challenging issues in iris recognition is the design of techniques able to ensure high accuracy even in adverse conditions. This paper deals with an approach to iris matching based on the combination of local features: Linear Binary Patterns (LBP) and discriminable textons (BLOBs) are presently exploited. The techniques have been refined ad hoc, to allow the extraction of signif...

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...

In face recognition Local appearance based methods has achieved greater performance. In this paper, we have proposed single sample per class using Center Symmetric Local Binary Pattern and Gabor Filter. Gabor Filter extracts the textual feature and generates a binary face templat and the binary face template acts like a mask to extract local texture information using Center Symmetric Local Bina...

In this paper, we propose a new representation, called Local Min-Max Binary Pattern (LMin-MaxBP), and apply it to face recognition with single sample per class. The local appearance based methods have been successfully applied to face recognition and achieved state-of-the-art performance. The Local Binary Pattern (LBP) has been proved to be effective for image representation. The motivation for...

Face recognition is one of the important and popular visual recognition problem due to its challenging nature and its diverse set of applications. That‘s why face recognition is attracted by many researchers. Methods based on Local Binary Pattern (LBP) are widely used for face recognition in the literature, and it is sensitive to noise. To address this present paper utilized the powerful local ...

Background: Emotion Recognition is the main component of social cognition and has various patterns in different cultures and nationalities. The present study aimed to investigate emotion recognition patterns among Iranians using the Cambridge Neuro-Psychological Test Automated Battery (CANTAB) as a valid neuropsychological test. Methods: In this descriptive-analytical study, 117 males and fema...

In this work it is described a framework for classifying face images using Adaboost and domain-partitioning based classifiers. The most interesting aspect of this framework is the capability of building classification systems with high accuracy in dynamical environments, which achieve, at the same time, high processing and training speed. We apply this framework to the specific problem of gende...

Gender categorization, based on the analysis of facial appearance, can be useful in a large set of applications. In this paper we investigate the gender classification problem from a non-conventional perspective. In particular, the analysis will aim to determine the factors critically affecting the accuracy of available technologies, better explaining differences between face-based identificati...

Face Recognition is a term that includes several sub-problems. Though face recognition have been a grown up research area, however, there still remain many problems that must be overcome to develop a robust face recognition system that works well under various circumstances such as illumination, pose and expressions variations. Such variations have proven to be one of the biggest problems of fa...