Bamboo: A fast descriptor based on AsymMetric pairwise BOOsting

A robust hash, or content-based fingerprint, is a succinct representation of the perceptually most relevant parts of a multimedia object. A key requirement of fingerprinting is that elements with perceptually similar content should map to the same fingerprint, even if their bit-level representations are different. In this work we propose BAMBOO (Binary descriptor based on AsymMetric pairwise BOOsting), a binary local descriptor that exploits a combination of content-based fingerprinting techniques and computationally efficient filters (box filters, Haar-like features, etc.) applied to image patches. In particular, we define a possibly large set of filters and iteratively select the most discriminative ones resorting to an asymmetric pair-wise boosting technique. The output values of the filtering process are quantized to one bit, leading to a very compact binary descriptor. Results show that such descriptor leads to compelling results, significantly outperforming binary descriptors having comparable complexity (e.g., BRISK), and approaching the discriminative power of state-of-the-art descriptors which are significantly more complex (e.g., SIFT and BinBoost)

REAL ADABOOST FOR CONTENT IDENTIFICATION

ABSTRACT This paper proposes a machine learning method based on Real Adaboost that jointly optimizes the content ID codes and the decoding metric. Significant performance gains over prior art are demonstrated for audio fingerprinting

TopologyNet: Topology based deep convolutional neural networks for biomolecular property predictions

Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the entangled geometric complexity and biological complexity. We introduce topology, i.e., element specific persistent homology (ESPH), to untangle geometric complexity and biological complexity. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains crucial biological information via a multichannel image representation. It is able to reveal hidden structure-function relationships in biomolecules. We further integrate ESPH and convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the limitations to deep learning arising from small and noisy training sets, we present a multitask topological convolutional neural network (MT-TCNN). We demonstrate that the present TopologyNet architectures outperform other state-of-the-art methods in the predictions of protein-ligand binding affinities, globular protein mutation impacts, and membrane protein mutation impacts.Comment: 20 pages, 8 figures, 5 table

Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results

In this paper, automated user verification techniques for smartphones are investigated. A unique non-commercial dataset, the University of Maryland Active Authentication Dataset 02 (UMDAA-02) for multi-modal user authentication research is introduced. This paper focuses on three sensors - front camera, touch sensor and location service while providing a general description for other modalities. Benchmark results for face detection, face verification, touch-based user identification and location-based next-place prediction are presented, which indicate that more robust methods fine-tuned to the mobile platform are needed to achieve satisfactory verification accuracy. The dataset will be made available to the research community for promoting additional research.Comment: 8 pages, 12 figures, 6 tables. Best poster award at BTAS 201

A computationally efficient framework for large-scale distributed fingerprint matching

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science, School of Computer Science and Applied Mathematics. May 2017.Biometric features have been widely implemented to be utilized for forensic and civil applications. Amongst many different kinds of biometric characteristics, the fingerprint is globally accepted and remains the mostly used biometric characteristic by commercial and industrial societies due to its easy acquisition, uniqueness, stability and reliability. There are currently various effective solutions available, however the fingerprint identification is still not considered a fully solved problem mainly due to accuracy and computational time requirements. Although many of the fingerprint recognition systems based on minutiae provide good accuracy, the systems with very large databases require fast and real time comparison of fingerprints, they often either fail to meet the high performance speed requirements or compromise the accuracy. For fingerprint matching that involves databases containing millions of fingerprints, real time identification can only be obtained through the implementation of optimal algorithms that may utilize the given hardware as robustly and efficiently as possible. There are currently no known distributed database and computing framework available that deal with real time solution for fingerprint recognition problem involving databases containing as many as sixty million fingerprints, the size which is close to the size of the South African population. This research proposal intends to serve two main purposes: 1) exploit and scale the best known minutiae matching algorithm for a minimum of sixty million fingerprints; and 2) design a framework for distributed database to deal with large fingerprint databases based on the results obtained in the former item.GR201

FP-Fed: Privacy-Preserving Federated Detection of Browser Fingerprinting

Browser fingerprinting often provides an attractive alternative to third-party cookies for tracking users across the web. In fact, the increasing restrictions on third-party cookies placed by common web browsers and recent regulations like the GDPR may accelerate the transition. To counter browser fingerprinting, previous work proposed several techniques to detect its prevalence and severity. However, these rely on 1) centralized web crawls and/or 2) computationally intensive operations to extract and process signals (e.g., information-flow and static analysis). To address these limitations, we present FP-Fed, the first distributed system for browser fingerprinting detection. Using FP-Fed, users can collaboratively train on-device models based on their real browsing patterns, without sharing their training data with a central entity, by relying on Differentially Private Federated Learning (DP-FL). To demonstrate its feasibility and effectiveness, we evaluate FP-Fed's performance on a set of 18.3k popular websites with different privacy levels, numbers of participants, and features extracted from the scripts. Our experiments show that FP-Fed achieves reasonably high detection performance and can perform both training and inference efficiently, on-device, by only relying on runtime signals extracted from the execution trace, without requiring any resource-intensive operation

Human metrology for person classification and recognition

Human metrological features generally refers to geometric measurements extracted from humans, such as height, chest circumference or foot length. Human metrology provides an important soft biometric that can be used in challenging situations, such as person classification and recognition at a distance, where hard biometric traits such as fingerprints and iris information cannot easily be acquired. In this work, we first study the question of predictability and correlation in human metrology. We show that partial or available measurements can be used to predict other missing measurements. We then investigate the use of human metrology for the prediction of other soft biometrics, viz. gender and weight. The experimental results based on our proposed copula-based model suggest that human body metrology contains enough information for reliable prediction of gender and weight. Also, the proposed copula-based technique is observed to reduce the impact of noise on prediction performance. We then study the question of whether face metrology can be exploited for reliable gender prediction. A new method based solely on metrological information from facial landmarks is developed. The performance of the proposed metrology-based method is compared with that of a state-of-the-art appearance-based method for gender classification. Results on several face databases show that the metrology-based approach resulted in comparable accuracy to that of the appearance-based method. Furthermore, we study the question of person recognition (classification and identification) via whole body metrology. Using CAESAR 1D database as baseline, we simulate intra-class variation with various noise models. The experimental results indicate that given enough number of features, our metrology-based recognition system can have promising performance that is comparable to several recent state-of-the-art recognition systems. We propose a non-parametric feature selection methodology, called adapted k-nearest neighbor estimator, which does not rely on intra-class distribution of the query set. This leads to improved results over other nearest neighbor estimators (as feature selection criteria) for moderate number of features. Finally we quantify the discrimination capability of human metrology, from both individuality and capacity perspectives. Generally, a biometric-based recognition technique relies on an assumption that the given biometric is unique to an individual. However, the validity of this assumption is not yet generally confirmed for most soft biometrics, such as human metrology. In this work, we first develop two schemes that can be used to quantify the individuality of a given soft-biometric system. Then, a Poisson channel model is proposed to analyze the recognition capacity of human metrology. Our study suggests that the performance of such a system depends more on the accuracy of the ground truth or training set

Learning compact hashing codes for large-scale similarity search

Retrieval of similar objects is a key component in many applications. As databases grow larger, learning compact representations for efficient storage and fast search becomes increasingly important. Moreover, these representations should preserve similarity, i.e., similar objects should have similar representations. Hashing algorithms, which encode objects into compact binary codes to preserve similarity, have demonstrated promising results in addressing these challenges. This dissertation studies the problem of learning compact hashing codes for large-scale similarity search. Specifically, we investigate two classes of approach: regularized Adaboost and signal-to-noise ratio (SNR) maximization. The regularized Adaboost builds on the classical boosting framework for hashing, while SNR maximization is a novel hashing framework with theoretical guarantee and great flexibility in designing hashing algorithms for various scenarios. The regularized Adaboost algorithm is to learn and extract binary hash codes (fingerprints) of time-varying content by filtering and quantizing perceptually significant features. The proposed algorithm extends the recent symmetric pairwise boosting (SPB) algorithm by taking feature sequence correlation into account. An information-theoretic analysis of the SPB algorithm is given, showing that each iteration of SPB maximizes a lower bound on the mutual information between matching fingerprint pairs. Based on the analysis, two practical regularizers are proposed to penalize those filters generating highly correlated filter responses. A learning-theoretic analysis of the regularized Adaboost algorithm is given. The proposed algorithm demonstrates significant performance gains over SPB for both audio and video content identification (ID) systems. SNR maximization hashing (SRN-MH) uses the SNR metric to select a set of uncorrelated projection directions, and one hash bit is extracted from each projection direction. We first motivate this approach under a Gaussian model for the underlying signals, in which case maximizing SNR is equivalent to minimizing the hashing error probability. This theoretical guarantee differentiates SNR-MH from other hashing algorithms where learning has to be carried out with a continuous relaxation of quantization functions. A globally optimal solution can be obtained by solving a generalized eigenvalue problem. Experiments on both synthetic and real datasets demonstrate the power of SNR-MH to learn compact codes. We extend SNR-MH to two different scenarios in large-scale similarity search. The first extension aims at applications with a larger bit budget. To learn longer hash codes, we propose a multi-bit per projection algorithm, called SNR multi-bit hashing (SNR-MBH), to learn longer hash codes when the number of high-SNR projections is limited. Extensive experiments demonstrate the superior performance of SNR-MBH. The second extension aims at a multi-feature setting, where more than one feature vector is available for each object. We propose two multi-feature hashing methods, SNR joint hashing (SNR-JH) and SNR selection hashing (SNR-SH). SNR-JH jointly considers all feature correlations and learns uncorrelated hash functions that maximize SNR, while SNR-SH separately learns hash functions on each individual feature and selects the final hash functions based on the SNR associated with each hash function. The proposed methods perform favorably compared to other state-of-the-art multi-feature hashing algorithms on several benchmark datasets

Identificación de canciones usando Chromaprint y la base de datos Free Music Archive

Actualmente la tecnología tiene un papel fundamental en la sociedad, donde su desarrollo puede marcar un antes y un después en la vida de la gente. En el caso de la música, las tecnologías relacionadas con el reconocimiento de canciones, nos facilitan la búsqueda de aquella canción que tanto nos gusta cuando la escuchamos en la radio, en la televisión o incluso en la discoteca. Este proyecto consiste precisamente en el reconocimiento de segmentos de canciones a través de una aplicación web proporcionada por acoustid.org y dejavu, que incluyen unos algoritmos capaces de generar fingerprints en base a una señal para compararlos entre sí y poder identificar si se trata o no de la misma señal. El objetivo de la investigación es desarrollar un sistema capaz de reconocer segmentos de canciones pertenecientes a la base de datos "FMA: A Dataset For Music Analysis" y analizar la eficiencia del sistema tratando señales limpias y con diferentes tipos e intensidades de ruid