arXiv subjects

Fernando Pereira

Publications and source records attributed to Fernando Pereira.

At least 37 records · Page 2Linked to original sources

arXiv2021

CapsField: Light Field-based Face and Expression Recognition in the Wild using Capsule Routing

Light field (LF) cameras provide rich spatio-angular visual representations by sensing the visual scene from multiple perspectives and have recently emerged as a promising technology to boost the performance of human-machine systems such as biometrics and affective computing. Despite the significant success of LF representation for constrained facial image analysis, this technology has never been used for face and expression recognition in the wild. In this context, this paper proposes a new deep face and expression recognition solution, called CapsField, based on a convolutional neural network and an additional capsule network that utilizes dynamic routing to learn hierarchical relations between capsules. CapsField extracts the spatial features from facial images and learns the angular part-whole relations for a selected set of 2D sub-aperture images rendered from each LF image. To analyze the performance of the proposed solution in the wild, the first in the wild LF face dataset, along with a new complementary constrained face dataset captured from the same subjects recorded earlier have been captured and are made available. A subset of the in the wild dataset contains facial images with different expressions, annotated for usage in the context of face expression recognition tests. An extensive performance assessment study using the new datasets has been conducted for the proposed and relevant prior solutions, showing that the CapsField proposed solution achieves superior performance for both face and expression recognition tasks when compared to the state-of-the-art.

cs.CV↗

arXiv2020

Point Cloud Rendering after Coding: Impacts on Subjective and Objective Quality

Recently, point clouds have shown to be a promising way to represent 3D visual data for a wide range of immersive applications, from augmented reality to autonomous cars. Emerging imaging sensors have made easier to perform richer and denser point cloud acquisition, notably with millions of points, thus raising the need for efficient point cloud coding solutions. In such a scenario, it is important to evaluate the impact and performance of several processing steps in a point cloud communication system, notably the quality degradations associated to point cloud coding solutions. Moreover, since point clouds are not directly visualized but rather processed with a rendering algorithm before shown on any display, the perceived quality of point cloud data highly depends on the rendering solution. In this context, the main objective of this paper is to study the impact of several coding and rendering solutions on the perceived user quality and in the performance of available objective quality assessment metrics. Another contribution regards the assessment of recent MPEG point cloud coding solutions for several popular rendering methods which were never presented before. The conclusions regard the visibility of three types of coding artifacts for the three considered rendering approaches as well as the strengths and weakness of objective quality metrics when point clouds are rendered after coding.

eess.IV↗

arXiv2020

Improving PSNR-based Quality Metrics Performance For Point Cloud Geometry

An increased interest in immersive applications has drawn attention to emerging 3D imaging representation formats, notably light fields and point clouds (PCs). Nowadays, PCs are one of the most popular 3D media formats, due to recent developments in PC acquisition, namely with new depth sensors and signal processing algorithms. To obtain high fidelity 3D representations of visual scenes a huge amount of PC data is typically acquired, which demands efficient compression solutions. As in 2D media formats, the final perceived PC quality plays an important role in the overall user experience and, thus, objective metrics capable to measure the PC quality in a reliable way are essential. In this context, this paper proposes and evaluates a set of objective quality metrics for the geometry component of PC data, which plays a very important role in the final perceived quality. Based on the popular PSNR PC geometry quality metric, the novel improved PSNR-based metrics are proposed by exploiting the intrinsic PC characteristics and the rendering process that must occur before visualization. The experimental results show the superiority of the best-proposed metrics over the state-of-the-art, obtaining an improvement of up to 32% in the Pearson correlation coefficient.

eess.IV↗

arXiv2020

Long Short-Term Memory with Gate and State Level Fusion for Light Field-Based Face Recognition

Long Short-Term Memory (LSTM) is a prominent recurrent neural network for extracting dependencies from sequential data such as time-series and multi-view data, having achieved impressive results for different visual recognition tasks. A conventional LSTM network can learn a model to posteriorly extract information from one input sequence. However, if two or more dependent sequences of data are simultaneously acquired, the conventional LSTM networks may only process those sequences consecutively, not taking benefit of the information carried out by their mutual dependencies. In this context, this paper proposes two novel LSTM cell architectures that are able to jointly learn from multiple sequences simultaneously acquired, targeting to create richer and more effective models for recognition tasks. The efficacy of the novel LSTM cell architectures is assessed by integrating them into deep learning-based methods for face recognition with multi-view, light field images. The new cell architectures jointly learn the scene horizontal and vertical parallaxes available in a light field image, to capture richer spatio-angular information from both directions. A comprehensive evaluation, with the IST-EURECOM LFFD dataset using three challenging evaluation protocols, shows the advantage of using the novel LSTM cell architectures for face recognition over the state-of-the-art light field-based methods. These results highlight the added value of the novel cell architectures when learning from correlated input sequences.

cs.CV↗

arXiv2020

A generalized Hausdorff distance based quality metric for point cloud geometry

Reliable quality assessment of decoded point cloud geometry is essential to evaluate the compression performance of emerging point cloud coding solutions and guarantee some target quality of experience. This paper proposes a novel point cloud geometry quality assessment metric based on a generalization of the Hausdorff distance. To achieve this goal, the so-called generalized Hausdorff distance for multiple rankings is exploited to identify the best performing quality metric in terms of correlation with the MOS scores obtained from a subjective test campaign. The experimental results show that the quality metric derived from the classical Hausdorff distance leads to low objective-subjective correlation and, thus, fails to accurately evaluate the quality of decoded point clouds for emerging codecs. However, the quality metric derived from the generalized Hausdorff distance with an appropriately selected ranking, outperforms the MPEG adopted geometry quality metrics when decoded point clouds with different types of coding distortions are considered.

eess.IV↗

arXiv2020

Lenslet Light Field Image Coding: Classifying, Reviewing and Evaluating

In recent years, visual sensors have been quickly improving, notably targeting richer acquisitions of the light present in a visual scene. In this context, the so-called lenslet light field (LLF) cameras are able to go beyond the conventional 2D visual acquisition models, by enriching the visual representation with directional light measures for each pixel position. LLF imaging is associated to large amounts of data, thus critically demanding efficient coding solutions in order applications involving transmission and storage may be deployed. For this reason, considerable research efforts have been invested in recent years in developing increasingly efficient LLF imaging coding (LLFIC) solutions. In this context, the main objective of this paper is to review and evaluate some of the most relevant LLFIC solutions in the literature, guided by a novel classification taxonomy, which allows better organizing this field. In this way, more solid conclusions can be drawn about the current LLFIC status quo, thus allowing to better drive future research and standardization developments in this technical area.

eess.IV↗

arXiv2019

A Double-Deep Spatio-Angular Learning Framework for Light Field based Face Recognition

Face recognition has attracted increasing attention due to its wide range of applications, but it is still challenging when facing large variations in the biometric data characteristics. Lenslet light field cameras have recently come into prominence to capture rich spatio-angular information, thus offering new possibilities for advanced biometric recognition systems. This paper proposes a double-deep spatio-angular learning framework for light field based face recognition, which is able to learn both texture and angular dynamics in sequence using convolutional representations; this is a novel recognition framework that has never been proposed before for either face recognition or any other visual recognition task. The proposed double-deep learning framework includes a long short-term memory (LSTM) recurrent network whose inputs are VGG-Face descriptions that are computed using a VGG-Very-Deep-16 convolutional neural network (CNN). The VGG-16 network uses different face viewpoints rendered from a full light field image, which are organised as a pseudo-video sequence. A comprehensive set of experiments has been conducted with the IST-EURECOM light field face database, for varied and challenging recognition tasks. Results show that the proposed framework achieves superior face recognition performance when compared to the state-of-the-art.

cs.CV↗

arXiv2019

Face Recognition: A Novel Multi-Level Taxonomy based Survey

In a world where security issues have been gaining growing importance, face recognition systems have attracted increasing attention in multiple application areas, ranging from forensics and surveillance to commerce and entertainment. To help understanding the landscape and abstraction levels relevant for face recognition systems, face recognition taxonomies allow a deeper dissection and comparison of the existing solutions. This paper proposes a new, more encompassing and richer multi-level face recognition taxonomy, facilitating the organization and categorization of available and emerging face recognition solutions; this taxonomy may also guide researchers in the development of more efficient face recognition solutions. The proposed multi-level taxonomy considers levels related to the face structure, feature support and feature extraction approach. Following the proposed taxonomy, a comprehensive survey of representative face recognition solutions is presented. The paper concludes with a discussion on current algorithmic and application related challenges which may define future research directions for face recognition.

cs.CV↗

arXiv2016

Optimal Rendezvous Trajectory for Unmanned Aerial-Ground Vehicles

Fixed-wind unmanned aerial vehicles (UAVs) are essential for low cost aerial surveillance and mapping applications in remote regions. One of the main limitations of UAVs is limited fuel capacity and hence requires periodic refueling to accomplish a mission. The usual mechanism of commanding the UAV to return to a stationary base station for refueling can result in fuel wastage and inefficient mission operation time. Alternatively, unmanned gound vehicle (UGV) can be used as a mobile refueling unit where the UAV will rendezvous with the UGV for refueling. In order to accurately perform this task in the presence of wind disturbances, we need to determine an optimal trajectory in 3D taking UAV and UGV dynamics and kinematics into account. In this paper, we propose an optimal control formulation to generate a tunable UAV trajectory for rendezvous on a moving UGV taking wind disturbances into account. By a suitable choice of the value of an aggressiveness index in our problem setting, we are able to control the UAV rendezvous behavior. Several numerical results are presented to show the reliability and effectiveness of our approach.

math.OC↗

arXiv2016

Optimal Lagrange Multipliers for Dependent Rate Allocation in Video Coding

In a typical video rate allocation problem, the objective is to optimally distribute a source rate budget among a set of (in)dependently coded data units to minimize the total distortion of all units. Conventional Lagrangian approaches convert the lone rate constraint to a linear rate penalty scaled by a multiplier in the objective, resulting in a simpler unconstrained formulation. However, the search for the "optimal" multiplier, one that results in a distortion-minimizing solution among all Lagrangian solutions that satisfy the original rate constraint, remains an elusive open problem in the general setting. To address this problem, we propose a computation-efficient search strategy to identify this optimal multiplier numerically. Specifically, we first formulate a general rate allocation problem where each data unit can be dependently coded at different quantization parameters (QP) using a previous unit as predictor, or left uncoded at the encoder and subsequently interpolated at the decoder using neighboring coded units. After converting the original rate constrained problem to the unconstrained Lagrangian counterpart, we design an efficient dynamic programming (DP) algorithm that finds the optimal Lagrangian solution for a fixed multiplier. Finally, within the DP framework, we iteratively compute neighboring singular multiplier values, each resulting in multiple simultaneously optimal Lagrangian solutions, to drive the rates of the computed Lagrangian solutions towards the bit budget. We terminate when a singular multiplier value results in two Lagrangian solutions with rates below and above the bit budget. In extensive monoview and multiview video coding experiments, we show that our DP algorithm and selection of optimal multipliers on average outperform comparable rate control solutions used in video compression standards such as HEVC that do not skip frames in Y-PSNR.

cs.MM↗

arXiv2016

Multinomial Loss on Held-out Data for the Sparse Non-negative Matrix Language Model

We describe Sparse Non-negative Matrix (SNM) language model estimation using multinomial loss on held-out data. Being able to train on held-out data is important in practical situations where the training data is usually mismatched from the held-out/test data. It is also less constrained than the previous training algorithm using leave-one-out on training data: it allows the use of richer meta-features in the adjustment model, e.g. the diversity counts used by Kneser-Ney smoothing which would be difficult to deal with correctly in leave-one-out training. In experiments on the one billion words language modeling benchmark, we are able to slightly improve on our previous results which use a different loss function, and employ leave-one-out training on a subset of the main training set. Surprisingly, an adjustment model with meta-features that discard all lexical information can perform as well as lexicalized meta-features. We find that fairly small amounts of held-out data (on the order of 30-70 thousand words) are sufficient for training the adjustment model. In a real-life scenario where the training data is a mix of data sources that are imbalanced in size, and of different degrees of relevance to the held-out and test data, taking into account the data source for a given skip-/n-gram feature and combining them for best performance on held-out/test data improves over skip-/n-gram SNM models trained on pooled data by about 8% in the SMT setup, or as much as 15% in the ASR/IME setup. The ability to mix various data sources based on how relevant they are to a mismatched held-out set is probably the most attractive feature of the new estimation method for SNM LM.

cs.CL↗

arXiv2015

Optimal Layered Representation for Adaptive Interactive Multiview Video Streaming

We consider an interactive multiview video streaming (IMVS) system where clients select their preferred viewpoint in a given navigation window. To provide high quality IMVS, many high quality views should be transmitted to the clients. However, this is not always possible due to the limited and heterogeneous capabilities of the clients. In this paper, we propose a novel adaptive IMVS solution based on a layered multiview representation where camera views are organized into layered subsets to match the different clients constraints. We formulate an optimization problem for the joint selection of the views subsets and their encoding rates. Then, we propose an optimal and a reduced computational complexity greedy algorithms, both based on dynamic-programming. Simulation results show the good performance of our novel algorithms compared to a baseline algorithm, proving that an effective IMVS adaptive solution should consider the scene content and the client capabilities and their preferences in navigation.

cs.MM↗

arXiv2014

Parameterized Construction of Program Representations for Sparse Dataflow Analyses

Data-flow analyses usually associate information with control flow regions. Informally, if these regions are too small, like a point between two consecutive statements, we call the analysis dense. On the other hand, if these regions include many such points, then we call it sparse. This paper presents a systematic method to build program representations that support sparse analyses. To pave the way to this framework we clarify the bibliography about well-known intermediate program representations. We show that our approach, up to parameter choice, subsumes many of these representations, such as the SSA, SSI and e-SSA forms. In particular, our algorithms are faster, simpler and more frugal than the previous techniques used to construct SSI - Static Single Information - form programs. We produce intermediate representations isomorphic to Choi et al.'s Sparse Evaluation Graphs (SEG) for the family of data-flow problems that can be partitioned per variables. However, contrary to SEGs, we can handle - sparsely - problems that are not in this family.

cs.PL↗

arXiv2014

Controlling Complexity in Part-of-Speech Induction

We consider the problem of fully unsupervised learning of grammatical (part-of-speech) categories from unlabeled text. The standard maximum-likelihood hidden Markov model for this task performs poorly, because of its weak inductive bias and large model capacity. We address this problem by refining the model and modifying the learning objective to control its capacity via para- metric and non-parametric constraints. Our approach enforces word-category association sparsity, adds morphological and orthographic features, and eliminates hard-to-estimate parameters for rare words. We develop an efficient learning algorithm that is not much more computationally intensive than standard training. We also provide an open-source implementation of the algorithm. Our experiments on five diverse languages (Bulgarian, Danish, English, Portuguese, Spanish) achieve significant improvements compared with previous methods for the same task.

cs.CL↗

arXiv2013

Large Scale Distributed Acoustic Modeling With Back-off N-grams

The paper revives an older approach to acoustic modeling that borrows from n-gram language modeling in an attempt to scale up both the amount of training data and model size (as measured by the number of parameters in the model), to approximately 100 times larger than current sizes used in automatic speech recognition. In such a data-rich setting, we can expand the phonetic context significantly beyond triphones, as well as increase the number of Gaussian mixture components for the context-dependent states that allow it. We have experimented with contexts that span seven or more context-independent phones, and up to 620 mixture components per state. Dealing with unseen phonetic contexts is accomplished using the familiar back-off technique used in language modeling due to implementation simplicity. The back-off acoustic model is estimated, stored and served using MapReduce distributed computing infrastructure. Speech recognition experiments are carried out in an N-best list rescoring framework for Google Voice Search. Training big models on large amounts of data proves to be an effective way to increase the accuracy of a state-of-the-art automatic speech recognition system. We use 87,000 hours of training data (speech along with transcription) obtained by filtering utterances in Voice Search logs on automatic speech recognition confidence. Models ranging in size between 20--40 million Gaussians are estimated using maximum likelihood training. They achieve relative reductions in word-error-rate of 11% and 6% when combined with first-pass models trained using maximum likelihood, and boosted maximum mutual information, respectively. Increasing the context size beyond five phones (quinphones) does not help.

cs.CL↗

arXiv2012

Case-Factor Diagrams for Structured Probabilistic Modeling

We introduce a probabilistic formalism subsuming Markov random fields of bounded tree width and probabilistic context free grammars. Our models are based on a representation of Boolean formulas that we call case-factor diagrams (CFDs). CFDs are similar to binary decision diagrams (BDDs) but are concise for circuits of bounded tree width (unlike BDDs) and can concisely represent the set of parse trees over a given string undera given context free grammar (also unlike BDDs). A probabilistic model consists of aCFD defining a feasible set of Boolean assignments and a weight (or cost) for each individual Boolean variable. We give an insideoutside algorithm for simultaneously computing the marginal of each Boolean variable, and a Viterbi algorithm for finding the mininum cost variable assignment. Both algorithms run in time proportional to the size of the CFD.

cs.AI↗

arXiv2012

A Conditional Random Field for Discriminatively-trained Finite-state String Edit Distance

The need to measure sequence similarity arises in information extraction, object identity, data mining, biological sequence analysis, and other domains. This paper presents discriminative string-edit CRFs, a finitestate conditional random field model for edit sequences between strings. Conditional random fields have advantages over generative approaches to this problem, such as pair HMMs or the work of Ristad and Yianilos, because as conditionally-trained methods, they enable the use of complex, arbitrary actions and features of the input strings. As in generative models, the training data does not have to specify the edit sequences between the given string pairs. Unlike generative models, however, our model is trained on both positive and negative instances of string pairs. We present positive experimental results on several data sets.

cs.LG↗

arXiv2005

Weighted Automata in Text and Speech Processing

Finite-state automata are a very effective tool in natural language processing. However, in a variety of applications and especially in speech precessing, it is necessary to consider more general machines in which arcs are assigned weights or costs. We briefly describe some of the main theoretical and algorithmic aspects of these machines. In particular, we describe an efficient composition algorithm for weighted transducers, and give examples illustrating the value of determinization and minimization algorithms for weighted automata.

cs.CL↗