Patent: “Two-tiered method and apparatus for fast and accurate face verification on mobile devices”

Yesterday the BioLive team celebrated their patent request approval by Motorola.
This patent, entitled “Two-tiered method and apparatus for fast and accurate face
verification on mobile devices”, is an important result of a collaborative research
project between RECOD and Motorola. It discloses a new face verification method,
which uses state-of-the-art techniques and can be efficiently run in mobile devices.

The BioLive team is formed by Anderson Rocha, Jacques Wainer, Ricardo Torres,
Fernanda Andaló, Rafael Padilha, Waldir Rodrigues, William Dias, Gabriel Bertocco,
and Thiago Resek.

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Workshop about Ethics of Data at IC Unicamp

Looking to present and discuss the new directions about ethics of data, the Institute of Computing (Unicamp) hosted the first edition of the workshop about ethics of data.

Organized by CEPID CCES (Prof. Munir Skaf and Profa. Cláudia Bauzer Medeiros), the goal was to shed some different lights about the topic. This is why there were presentations from computer science, social science and biologic fields.

Prof. Eduardo Valle (RECOD) gave a speech about the relationships between society and artificial intelligence. All presentations (in Portuguese) are available at IC/Unicamp YouTube channel.

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History matching through dynamic decision-making

High-level workflow of the proposed optimization framework, including the two dynamic decision-making optimization components.

History matching is the process of modifying the uncertain attributes of a reservoir model to reproduce the real reservoir performance. It is a classical reservoir engineering problem and plays an important role in reservoir management since the resulting models are used to support decisions in other tasks such as economic analysis and production strategy.

In this paper, the authors present a dynamic decision-making optimization framework for history matching. The term ‘dynamic decision-making’ reflects the fact that, during the framework execution, the decision to generate a particular new solution is always guided and supported by the results of a continuous and dynamic analysis of the data from available solutions.

The proposed framework is different from previous approaches reported in the literature in the following aspects: it is not a stochastic method, since there is no randomness in its execution, nor it requires a large number of simulations to converge; it does not use a proxy model to substitute the flow simulator, so the results obtained with the framework are accurate at any moment of the execution; it is not a geostatistical process neither is primarily concerned with uncertainty reduction of the reservoir attributes. Rather, it is an optimization framework which follows a learning approach where the strategy is to dynamically analyze a set of observations (available solutions) to uncover input patterns (values of reservoir uncertain attributes) that lead to desired responses (good history matching for one or more wells) in the available solutions.


Cavalcante CCB, Maschio C, Santos AA, Schiozer D, Rocha A (2017) History matching through dynamic decision-making. PLOS ONE 12(6): e0178507.https://doi.org/10.1371/journal.pone.0178507

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Data-Driven Feature Characterization Techniques for Laser Printer Attribution

Laser printer attribution is an increasing problem with several applications, such as pointing out the ownership of crime proofs and authentication of printed documents. However, most of the existing methods are limited by modeling assumptions about printing artifacts.

In this paper, the authors explore solutions able to learn discriminant-printing patterns directly from the available data during an investigation, without any further feature engineering, proposing the first approach based on deep learning to laser printer attribution. Experimental results show that the proposed method is robust to noisy data and outperforms existing counterparts in the literature for this problem.

In summary, the main contributions of this paper are:
1) The design and development of an ad-hoc CNN for laser printer attribution based on the analysis of small patches representing text characters;
2) The use of CNNs on multiple representations of the same character to learn complementary features that are fused together for an increased recognition accuracy;
3) The use of a late-fusion paradigm to merge results coming from the analysis of different characters within the same document. In this way, each character is classified separately, and individual results contribute to the final document label.


Ferreira, Anselmo, et al. “Data-Driven Feature Characterization Techniques for Laser Printer Attribution.” IEEE Transactions on Information Forensics and Security (2017). doi: 10.1109/TIFS.2017.2692722

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New dissimilarity measures for image phylogeny reconstruction

Image phylogeny is the problem of reconstructing the structure that represents the history of generation of semantically similar images (e.g., near-duplicate images). Typical image phylogeny approaches break the problem into two steps: (1) estimating the dissimilarity between each pair of images and (2) reconstructing the phylogeny structure.

In this article, the authors propose new approaches to the standard formulation of the dissimilarity measure employed in image phylogeny, aiming at improving the reconstruction of the tree structure that represents the generational relationships between semantically similar images. These new formulations exploit a different method of color adjustment, local gradients to estimate pixel differences and mutual information as a similarity measure.

The results obtained with the proposed formulation remarkably outperform the existing counterparts in the literature, allowing a much better analysis of the kinship relationships in a set of images, allowing for more accurate deployment of phylogeny solutions to tackle traitor tracing, copyright enforcement and digital forensics problems.


Filipe Costa, Alberto Oliveira, Pasquale Ferrara, Zanoni Dias, Siome Goldenstein and Anderson Rocha. Pattern Analysis and Applications (2017). doi:10.1007/s10044-017-0616-9

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Temporal robust features for violence detection

Automatically detecting violence in videos is paramount for enforcing the law and providing the society with better policies for safer public places. In addition, it may be essential for protecting minors from accessing inappropriate contents on-line, and for helping parents choose suitable movie titles for their children. However, this is an open problem as the very definition of violence is subjective and may vary from one society to another. Detecting such nuances from video footages with no human supervision is very challenging.

In this paper the authors explores a fast end-to-end Bag-of-VisualWords (BoVW)-based framework for violence classification. They adapt Temporal Robust Features (TRoF), a fast spatio-temporal interest point detector and descriptor, which is custom-tailored for inappropriate content detection, such as violence. The used method holds promise for fast and effective classification of other recognition tasks (e.g., pornography and other inappropriate material). When compared to more complex counterparts for violence detection, the method shows similar classification quality while being several times more efficient in terms of runtime and memory footprint.

The explored three-layered BoVW-based framework for video violence classification


MOREIRA, D. H. ; AVILA, SANDRA ; PEREZ, MAURICIO ; MORAES, Daniel ; TESTONI, Vanessa ; VALLE, Eduardo ; Siome Goldenstein ; ROCHA, ANDERSON. Temporal Robust Features for Violence Detection. In: IEEE Intl. Winter Conference on Applications of Computer Vision (WACV), 2017, Santa Rosa. IEEE Intl. Winter Conference on Applications of Computer Vision (WACV), 2017. p. 1-9.

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Our research about pornography detection on the News

Our research about pornography detection, recently published at Neurocomputing, is getting the attention of many News agencies in Brazil. It’s a good indicator to measure the great impact it has into our modern society. All of them are listed below (in Portuguese):

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Talk: Multimedia Integrity Analytics

Today Prof. Anderson Rocha will give a talk at University of Kentucky about Multimedia Integrity Analytics. The talk is part of the university weekly seminars and presentation.

Abstract: Currently, multimedia objects can be easily created, stored, (re)-transmitted, and edited for good or bad. In this sense, there has been an increasing interest in finding the structure of temporal evolution within a set of documents and how documents are related to one another overtime. This process, also known in the literature as Multimedia Phylogeny, aims at finding the phylogeny tree(s) that best explains the creation process of a set of related documents (e.g., images/videos) and their ancestry relationships. Solutions to this problem have direct applications in forensics, security, copyright enforcement, news tracking services and other areas. In this talk, we will explore solutions for reconstructing the evolutionary tree(s) associated with a set of visual documents, more specifically images and videos. This can be useful for aiding experts to track the source of child pornography image broadcasting or the chain of image and video distribution in time, being extremely useful for complex different media provenance tasks. Finally, we will also discuss how to implement such solutions for large-scale setups considering millions of documents at the same time.

The full set of slides are available here.

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Empirical Evaluation of Resampling Procedures for Optimising SVM Hyperparameters

Tuning the regularisation and kernel hyperparameters is a vital step in optimising the generalisation performance of kernel methods, such as the support vector machine (SVM). This is most often performed by minimising a resampling/cross-validation based model selection criterion, however there seems little practical guidance on the most suitable form of resampling.

In this article, the authors present the results of an extensive empirical evaluation of resampling procedures for SVM hyperparameter selection, designed to address this gap in the machine learning literature. They tested 17 different resampling procedures on 121 binary classification data sets in order to select the best SVM hyperparameters.

The conclusion is that the 2-fold procedure should be used in data sets with more than 1000 points. In these cases, the user may expect a difference of −0.0031 to 0.0031 in the error rate of the classifier if a 5-fold procedure was used, which they believe is the limit of what one should consider an irrelevant change in the classifier error rate. For smaller data sets, they could not detect any significant difference (on average) between 5-fold and computationally more costly procedures such as 10-fold, 5 to 20 times repeated bootstrap, or 2 times repeated 5-fold. Thus, a 3-fold is appropriate for smaller data sets.


WAINER, Jacques; CAWLEY, Gavin. Empirical evaluation of resampling procedures for optimising SVM hyperparameters. Journal of Machine Learning Research, 2016.

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RECOD’s Diabetic Retinopathy research on the news

Our research on Diabetic Retinopathy has gotten the attention of a well known website about health and wellness in Brazil. The article (in Portuguese) highlights the importance of diabetic retinopathy treatment and how the society can take advantage of our proposed solution. As it is an ongoing research, a new article with our recent discoveries is under preparation. Stay tuned!

(credits to: Gustavo Arrais)

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