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The first International Workshop on Biometrics in the Wild (B-Wild 2015) was held on May 8th, 2015 in conjunction with the 11th IEEE International Conference on Automatic Face and Gesture Recognition (IEEE FG-2015) in Ljubljana, Slovenia. The goal of the workshop was to present the most advanced work related to biometric recognition in the wild and to bring recent advances from this field to the attention...
The paper addresses the problem of face recognition in the wild. It introduces a novel approach to unconstrained face recognition that exploits Gabor magnitude features and a simplified version of the probabilistic linear discriminant analysis (PLDA). The novel approach, named Probabilistic Gabor-Fisher Classifier (PGFC), first extracts a vector of Gabor magnitude features from the given input image...
Recently, there is an extensive research efforts devoted to the challenging problem of face verification in unconstrained settings and weakly labeled data, where the task is to determine whether pairs of images are from the same person or not. In this paper, we propose a novel discriminative dimensionality reduction technique called Side-Information Exponential Discriminant Analysis (SIEDA) which...
The automatic verification of kinship is a challenging problem that recently attracted much interest from the research community. It consists in telling whether two individuals are related or not, based on the analysis of their facial images. This is a challenging task since it has to deal with differences in race, gender and age between subjects. In addition, the unpredictable amount of genetic information...
Typically, an automatic face authentication (FA) procedure begins with data (facial images) acquisition, procedure that can be carried out with or without human monitoring (in unconstrained settings), the subsequent steps being automatically processed. When the human monitoring is absent for the access procedure (i.e., the system is operating in the “wild”), the current FA systems can be easily cheated...
The term “in the wild” has become wildly popular in face recognition research. The term refers generally to use of datasets that are somehow less controlled or more realistic. In this work, we consider how face recognition accuracy varies according to the composition of the dataset on which the decision threshold is learned and the dataset on which performance is then measured. We identify different...
While face recognition technology has made significant progress in recent years, practical pose invariant face recognition remains a challenge. This paper describes a reference-based framework for solving this problem. The similarity between a face image and a set of reference individuals defines the reference-based descriptor for a face image. Recognition is performed using the reference-based descriptors...
While the problem of facial landmark detection is getting big attention in the computer vision community recently, most of the methods deal only with near-frontal views and there is only a few really multi-view detectors available, that are capable of detection in a wide range of yaw angle (e.g. Φ ϵ (−90°, 90°)). We describe a multi-view facial landmark detector based on the Deformable Part Models,...
Face and fingerprint were used in de-duplication of the voter registration list for the 2010 Somaliland presidential election. Iris recognition was evaluated as a possible more powerful means of de-duplication of the voting register for the planned 2015 elections. On a trial dataset of 1,062 registration records, all instances of duplicate registration were detected and zero non-duplicates were falsely...
Affect communication and recognition is influenced by a number of contextual, social, and psychological factors. Personality and natural expressiveness of the individual, previous experiences, the ongoing task, and pre-existing relationships with the people involved helps individuals interpret and respond to social interactions. An important related issue to address in automatic affect recognition...
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