Information Retrieval: Recent Advances and Beyond

In this paper, we provide a detailed overview of the models used for information retrieval in the first and second stages of the typical processing chain. We discuss the current state-of-the-art models, including methods based on terms, semantic retrieval, and neural. Additionally, we delve into the key topics related to the learning process of these models. This way, this survey offers a comprehensive understanding of the field and is of interest for for researchers and practitioners entering/working in the information retrieval domain

Joint head pose/soft label estimation for human recognition in-the-wild

Soft biometrics have been emerging to complement other traits and are particularly useful for poor quality data. In this paper, we propose an efficient algorithm to estimate human head poses and to infer soft biometric labels based on the 3D morphology of the human head. Starting by considering a set of pose hypotheses, we use a learning set of head shapes synthesized from anthropometric surveys to derive a set of 3D head centroids that constitutes a metric space. Next, representing queries by sets of 2D head landmarks, we use projective geometry techniques to rank efficiently the joint 3D head centroids/pose hypotheses according to their likelihood of matching each query. The rationale is that the most likely hypotheses are sufficiently close to the query, so a good solution can be found by convex energy minimization techniques. Once a solution has been found, the 3D head centroid and the query are assumed to have similar morphology, yielding the soft label. Our experiments point toward the usefulness of the proposed solution, which can improve the effectiveness of face recognizers and can also be used as a privacy-preserving solution for biometric recognition in public environments

Mobile Iris CHallenge Evaluation II: Results from the ICPR competition

The growing interest for mobile biometrics stems from the increasing need to secure personal data and services, which are often stored or accessed from there. Modern user mobile devices, with acquisition and computation resources to support related operations, are nowadays widely available. This makes this research topic very attracting and promising. Iris recognition plays a major role in this scenario. However, mobile biometrics still suffer from some hindering factors. The resolution of captured images and the computational power are not comparable to desktop systems yet. Furthermore, the acquisition setting is generally uncontrolled, with users who are not that expert to autonomously generate biometric samples of sufficient quality. Mobile Iris CHallenge Evaluation aims at providing a testbed to assess the progress of mobile iris recognition, and to evaluate the extent of its present limitations. This paper presents the results of the competition launched at the 2016 edition of the International Conference on Pattern Recognition (ICPR)

Optimizing probabilistic fuzzy systems for classification using metaheuristics

Two new methods for the optimization of probabilistic fuzzy classifiers are proposed. Probabilistic fuzzy systems are specially attractive due to their explicit and simultaneous modelling of two kinds of uncertainty, namely vagueness in linguistic terms (fuzziness) and probabilistic uncertainty. The current method uses the maximization of the likelihood with the stochastic gradient descent, which not only converges to local minima but also does not guarantee the minimization of the misclassification error. The proposed methods address this specific problem by incorporating global search techniques. The first algorithm proposed is a genetic algorithm with simple crossover and mutation operations. The other is a first generation memetic algorithm which combines the genetic algorithm with the stochastic gradient descent. A total of five benchmarks were used to compare the three algorithms. The results show that the proposed methods have an average relative improvement of 2% and 6% for the accuracy with the genetic and memetic algorithms, respectively