Biometric recognition offers a reliable solution to the problem of user authentication in identity management systems. With the widespread deployment of biometric systems in various applications, there are increasing concerns about the security and privacy of biometric technology. Public acceptance of biometrics technology will depend on the ability of system designers to demonstrate that these systems are robust, have low error rates, and are tamper proof. We present a high-level categorization of the various vulnerabilities of a biometric system and discuss countermeasures that have been proposed to address these vulnerabilities. In particular, we focus on biometric template security which is an important issue because, unlike passwords and tokens, compromised biometric templates cannot be revoked and reissued. Protecting the template is a challenging task due to intrauser variability in the acquired biometric traits. We present an overview of various biometric template protection schemes and discuss their advantages and limitations in terms of security, revocability, and impact on matching accuracy. A template protection scheme with provable security and acceptable recognition performance has thus far remained elusive. Development of such a scheme is crucial as biometric systems are beginning to proliferate into the core physical and information infrastructure of our society.

/pdf/biometric-template-security-3nxd1k841h.pdf

Biometric template security

Top end mobile phones include a number of specialized (e.g., accelerometer, compass, GPS) and general purpose sensors (e.g., microphone, camera) that enable new people-centric sensing applications. Perhaps the most ubiquitous and unexploited sensor on mobile phones is the microphone - a powerful sensor that is capable of making sophisticated inferences about human activity, location, and social events from sound. In this paper, we exploit this untapped sensor not in the context of human communications but as an enabler of new sensing applications. We propose SoundSense, a scalable framework for modeling sound events on mobile phones. SoundSense is implemented on the Apple iPhone and represents the first general purpose sound sensing system specifically designed to work on resource limited phones. The architecture and algorithms are designed for scalability and Soundsense uses a combination of supervised and unsupervised learning techniques to classify both general sound types (e.g., music, voice) and discover novel sound events specific to individual users. The system runs solely on the mobile phone with no back-end interactions. Through implementation and evaluation of two proof of concept people-centric sensing applications, we demostrate that SoundSense is capable of recognizing meaningful sound events that occur in users' everyday lives.

/pdf/soundsense-scalable-sound-sensing-for-people-centric-25e8zqtup4.pdf

SoundSense: scalable sound sensing for people-centric applications on mobile phones

Form a privacy perspective most concerns against the common use of biometrics arise from the storage and misuse of biometric data. Biometric cryptosystems and cancelable biometrics represent emerging technologies of biometric template protection addressing these concerns and improving public confidence and acceptance of biometrics. In addition, biometric cryptosystems provide mechanisms for biometric-dependent key-release. In the last years a significant amount of approaches to both technologies have been published. A comprehensive survey of biometric cryptosystems and cancelable biometrics is presented. State-of-the-art approaches are reviewed based on which an in-depth discussion and an outlook to future prospects are given.

/pdf/a-survey-on-biometric-cryptosystems-and-cancelable-1rzqwaeseq.pdf

A survey on biometric cryptosystems and cancelable biometrics

Supporting continuous sensing applications on mobile phones is challenging because of the resource demands of long-term sensing, inference and communication algorithms. We present the design, implementation and evaluation of the Jigsaw continuous sensing engine, which balances the performance needs of the application and the resource demands of continuous sensing on the phone. Jigsaw comprises a set of sensing pipelines for the accelerometer, microphone and GPS sensors, which are built in a plug and play manner to support: i) resilient accelerometer data processing, which allows inferences to be robust to different phone hardware, orientation and body positions; ii) smart admission control and on-demand processing for the microphone and accelerometer data, which adaptively throttles the depth and sophistication of sensing pipelines when the input data is low quality or uninformative; and iii) adaptive pipeline processing, which judiciously triggers power hungry pipeline stages (e.g., sampling the GPS) taking into account the mobility and behavioral patterns of the user to drive down energy costs. We implement and evaluate Jigsaw on the Nokia N95 and the Apple iPhone, two popular smartphone platforms, to demonstrate its capability to recognize user activities and perform long term GPS tracking in an energy-efficient manner.

/pdf/the-jigsaw-continuous-sensing-engine-for-mobile-phone-2qof23h2xd.pdf

The Jigsaw continuous sensing engine for mobile phone applications

We present a computer audition system that can both annotate novel audio tracks with semantically meaningful words and retrieve relevant tracks from a database of unlabeled audio content given a text-based query. We consider the related tasks of content-based audio annotation and retrieval as one supervised multiclass, multilabel problem in which we model the joint probability of acoustic features and words. We collect a data set of 1700 human-generated annotations that describe 500 Western popular music tracks. For each word in a vocabulary, we use this data to train a Gaussian mixture model (GMM) over an audio feature space. We estimate the parameters of the model using the weighted mixture hierarchies expectation maximization algorithm. This algorithm is more scalable to large data sets and produces better density estimates than standard parameter estimation techniques. The quality of the music annotations produced by our system is comparable with the performance of humans on the same task. Our ldquoquery-by-textrdquo system can retrieve appropriate songs for a large number of musically relevant words. We also show that our audition system is general by learning a model that can annotate and retrieve sound effects.

/pdf/semantic-annotation-and-retrieval-of-music-and-sound-effects-14hge5xy7b.pdf

Semantic Annotation and Retrieval of Music and Sound Effects

Four audio feature sets are evaluated in their ability to classify five general audio classes and seven popular music genres. The feature sets include low-level signal properties, mel-frequency spectral coefficients, and two new sets based on perceptual models of hearing. The temporal behavior of the features is analyzed and parameterized and these parameters are included as additional features. Using a standard Gaussian framework for classification, results show that the temporal behavior of features is important for both music and audio classification. In addition, classification is better, on average, if based on features from models of auditory perception rather than on standard features.

/pdf/features-for-audio-and-music-classification-3c3d86vqth.pdf

Features for audio and music classification

Parametric-stereo coding is a technique to efficiently code a stereo audio signal as a monaural signal plus a small amount of parametric overhead to describe the stereo image. The stereo properties are analyzed, encoded, and reinstated in a decoder according to spatial psychoacoustical principles. The monaural signal can be encoded using any (conventional) audio coder. Experiments show that the parameterized description of spatial properties enables a highly efficient, high-quality stereo audio representation.

/pdf/parametric-coding-of-stereo-audio-4fznz7nwhj.pdf

Parametric coding of stereo audio

Reference LCAV-CONF-2005-031 URL: www.aes.org Record created on 2005-10-07, modified on 2016-08-08

/pdf/the-reference-model-architecture-for-mpeg-spatial-audio-44puy4f7zg.pdf

The Reference Model Architecture for MPEG Spatial Audio Coding

Following the recent trend of employing parametric enhancement tools for increasing coding or spatial rendering efficiency, Spatial Audio Object Coding (SAOC) is one of the recent standardization activities in the MPEG audio group. SAOC is a technique for efficient coding and flexible, user-controllable rendering of multiple audio objects based on transmission of a mono or stereo downmix of the object signals. The SAOC system extends the MPEG Surround standard by re-using its spatial rendering capabilities. This paper will describe the chosen reference model architecture, the association between the different operational modes and applications, and the current status of the standardization process.

/pdf/spatial-audio-object-coding-saoc-the-upcoming-mpeg-standard-4brcg3xa5i.pdf

Spatial Audio Object Coding (SAOC) - The Upcoming MPEG Standard on Parametric Object Based Audio Coding

Reference LCAV-CONF-2005-029 URL: www.aes.org Record created on 2005-10-07, modified on 2017-05-12

Jeroen Breebaart

Papers

Features for audio and music classification

Parametric coding of stereo audio

The Reference Model Architecture for MPEG Spatial Audio Coding

Spatial Audio Object Coding (SAOC) - The Upcoming MPEG Standard on Parametric Object Based Audio Coding

MPEG Spatial Audio Coding / MPEG Surround: Overview and Current Status