Asynchronous processing for latent fingerprint identification on heterogeneous CPU-GPU systems

Latent fingerprint identification is one of the most essential identification procedures in criminal investigations. Addressing this task is challenging as (i) it requires analyzing massive databases in reasonable periods and (ii) it is commonly solved by combining different methods with very complex data-dependencies, which make fully exploiting heterogeneous CPU-GPU systems very complex. Most efforts in this context focus on improving the accuracy of the approaches and neglect reducing the processing time. Indeed, the most accurate approach was designed for one single thread. This work introduces the fastest methodology for latent fingerprint identification maintaining high accuracy called Asynchronous processing for Latent Fingerprint Identification (ALFI). ALFI fully exploits all the resources of CPU-GPU systems using asynchronous processing and fine-coarse parallelism for analyzing massive databases. Our approach reduces idle times in processing and exploits the inherent parallelism of comparing latent fingerprints to fingerprint impressions. We analyzed the performance of ALFI on Linux and Windows operating systems using the well-known NIST/FVC databases. Experimental results reveal that ALFI is in average 22x faster than the state-of-the-art algorithm, reaching a value of 44.7x for the best-studied case

Adapting fuzzy rough sets for classification with missing values

We propose an adaptation of fuzzy rough sets to model concepts in datasets with missing values. Upper and lower approximations are replaced by interval-valued fuzzy sets that express the uncertainty caused by incomplete information. Each of these interval-valued fuzzy sets is delineated by a pair of optimistic and pessimistic approximations. We show how this can be used to adapt Fuzzy Rough Nearest Neighbour (FRNN) classification to datasets with missing values. In a small experiment with real-world data, our proposal outperforms simple imputation with the mean and mode on datasets with a low missing value rate

A study on the calibration of fingerprint classifiers

Fingerprint classification is a frequent approach to deal with very large scale databases in fingerprint recognition. In the last few years, several proposals based on Convolutional Neural Networks have pushed state of the art results even further. However, it has also been proven that such networks are prone to be overconfident in the predictions of the classes, which may have an impact on their performance. This paper aims to study the problem from a systematic point of view. First, it is determined that the most common network to classify fingerprints does suffer from badly calibrated predictions. Second, two calibration methods (temperature scaling and Dirichlet calibration) are applied to correct for this tendency. Third, a modified search strategy is proposed, which makes use of the calibrated class probabilities predicted by the classifier to further reduce the penetration rate and avoid the negative impact of impostor input fingerprints. Fourth, all the proposals are evaluated on five datasets, which combine synthetic and real fingerprints of different qualities. Dirichlet calibration led to improved predicted class probabilities, which in turn allowed for further reduction of the penetration, while maintaining a good trade-off with respect to the false rejection rate

SCMFTS : scalable and distributed complexity measures and features for univariate and multivariate time series in Big Data environments

Time series data are becoming increasingly important due to the interconnectedness of the world. Classical problems, which are getting bigger and bigger, require more and more resources for their processing, and Big Data technologies offer many solutions. Although the principal algorithms for traditional vector-based problems are available in Big Data environments, the lack of tools for time series processing in these environments needs to be addressed. In this work, we propose a scalable and distributed time series transformation for Big Data environments based on well-known time series features (SCMFTS), which allows practitioners to apply traditional vector-based algorithms to time series problems. The proposed transformation, along with the algorithms available in Spark, improved the best results in the state-of-the-art on the Wearable Stress and Affect Detection dataset, which is the biggest publicly available multivariate time series dataset in the University of California Irvine (UCI) Machine Learning Repository. In addition, SCMFTS showed a linear relationship between its runtime and the number of processed time series, demonstrating a linear scalable behavior, which is mandatory in Big Data environments. SCMFTS has been implemented in the Scala programming language for the Apache Spark framework, and the code is publicly available.(1

Multiclass heartbeat classification using ECG signals and convolutional neural networks

Given a large enough time series signal from an ECG signal, it is possible to identify and classify heartbeats not only into normal and abnormal classes but into multiple classes including but not limited to Normal beat, Paced beat, Atrial Premature beat and Ventricular flutter as originally suggested by benchmark electrocardiogram (ECG) datasets like the MIT-BIH Arrhythmia Dataset. There are multiple approaches that target ECG classifications using Machine and Deep Learning like One Class SVM, ELM, Anogan etc. These approaches require either very high computational resources, fail to classify classes apart from normal/abnormal classes or fail to classify all classes with an equivalent or near-equivalent accuracy. With these limitations in mind, this paper proposes a deep learning approach using Convolutional Neural Networks (CNNs) to classify multiple classes of heartbeats in an efficient, effective, and generalized manner. By using the MIT-BIH Arrhythmia dataset to filter and segment individual correctly structured heartbeats, we have designed a network which can be trained on different classes of heartbeats and present robust, accurate and efficient results. The class imbalance prevalent in the MIT-BIH dataset has been dealt with using Synthetic Minority Over-sampling Technique (SMOTE). The robustness of the model is increased by adding techniques of loss minimization such as dropout and early stop-ping. The approach gives an accuracy of approximately 96% and an extremely short time span for class prediction(classification), i.e., less than 1 second. The results are also illustrated over multiple (10) classes to exemplify the generality of the model. We have illustrated these results over multiple (10) classes to exemplify generality of the model