A Fast Parts-Based Approach to Speaker Verification Using Boosted Slice Classifiers

Magimai-Doss, Mathew; Marcel, Sébastien

doi:10.1109/tifs.2011.2166387

Cited by 23 publications

(14 citation statements)

References 19 publications

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“…Most of papers are devoted to a particular modality. We can mention the references [8] and more recently [9] focused on speaker verification for mobile devices. The first deals with textdependent speaker verification, while the latter proposes a new method to extract features from speech spectra called slice features.…”

Section: Biometric Pattern Based Authenticationmentioning

confidence: 99%

Synchronous One Time Biometrics with Pattern Based Authentication

Lacharme

Rosenberger

2016

2016 11th International Conference on Availability, Reliability and Security (ARES)

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Abstract-One time passwords are commonly used for authentication purposes in electronic transactions. Nevertheless, providing such a one time password is not really a strong authentication proof because the token generating the passwords can be given by an impostor. In order to cope with this problem, biometric recognition is more and more employed. Even if biometric data are strongly linked with the user, their revocability nor diversity is possible, without an adapted postprocessing. Biometric template protection schemes, including the BioHashing algorithm, are used to manage the underlying privacy and security issues. These schemes are used for the protection of several biometric modalities, but are not necessary adapted for all of them. In this paper, we propose a new protocol combining protected biometric data and a classical synchronous one time password to enhance the security of user authentication while preserving usability and privacy. Behavioral biometrics is used to provide a fast and a usable solution for users. We show through experimental results the efficiency of the proposed method.

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Section: Biometric Pattern Based Authenticationmentioning

confidence: 99%

Synchronous One Time Biometrics with Pattern Based Authentication

Lacharme

Rosenberger

2016

2016 11th International Conference on Availability, Reliability and Security (ARES)

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“…The authors of [52] call these features, boosted binary features (BBF). In a more recent paper [53], Roy, et al refined their approach and renamed the method a slice classifier. They show similar results with this classifier, compared to the state of the art, but they explain that the method is less computationally intensive and is more suitable for use in mobile devices with limited resources.…”

Section: Local Binary Features (Slice Classifier)mentioning

confidence: 99%

“…As we mentioned in Section 4, Roy, et al [52,53] used the so-called boosted binary features (slice classifier) for speaker verification. Also, we reviewed several developments regarding the i-vector formulation in Section 5.1.…”

Section: Verificationmentioning

confidence: 99%

Speaker Recognition: Advancements and Challenges

Beigi¹

2012

New Trends and Developments in Biometrics

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Speaker Recognition is a multi-disciplinary branch of biometrics that may be used for identification, verification, and classification of individual speakers, with the capability of tracking, detection, and segmentation by extension. Recently, a comprehensive book on all aspects of speaker recognition was published [1]. Therefore, here we are not concerned with details of the standard modeling which is and has been used for the recognition task. In contrast, we present a review of the most recent literature and briefly visit the latest techniques which are being deployed in the various branches of this technology.Most of the works being reviewed here have been published in the last two years. Some of the topics, such as alternative features and modeling techniques, are general and apply to all branches of speaker recognition. Some of these general techniques, such as whispered speech, are related to the advanced treatment of special forms of audio which have not received ample attention in the past. Finally, we will follow by a look at advancements which apply to specific branches of speaker recognition [1], such as verification, identification, classification, and diarization. This chapter is meant to complement the summary of speaker recognition, presented in [2], which provided an overview of the subject. It is also intended as an update on the methods described in [1]. In the next section, for the sake of completeness, a brief history of speaker recognition is presented, followed by sections on specific progress as stated above, for globally applicable treatment and methods, as well as techniques which are related to specific branches of speaker recognition. A brief historyThe topic of speaker recognition [1] has been under development since the mid-twentieth century. The earliest known papers on the subject, published in the 1950s [3,4], were in search of finding personal traits of the speakers, by analyzing their speech, with some statistical underpinning. With the advent of early communication networks, Pollack, et al. [3] noted the need for speaker identification. Although, they employed human listeners to do the identification of individuals and studied the importance of the duration of speech and other facets that help in the recognition of a speaker. In most of the early

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“…It was shown that a combination of these systems produced an impressive bi-modal authentication system. Since then other researchers have examined methods to perform face [3,4], speaker [5,6] and bi-modal [7,8] authentication in the challenging mobile phone environment.…”

Section: Introductionmentioning

confidence: 99%

Bi-modal biometric authentication on mobile phones in challenging conditions

Khoury

Shafey

McCool

et al. 2014

Image and Vision Computing

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This paper examines the issue of face, speaker and bi-modal authentication in mobile environments when there is significant condition mismatch. We introduce this mismatch by enrolling client models on high quality biometric samples obtained on a laptop computer and authenticating them on lower quality biometric samples acquired with a mobile phone. To perform these experiments we develop three novel authentication protocols for the large publicly available MOBIO database. We evaluate state-of-the-art face, speaker and bi-modal authentication techniques and show that inter-session variability modelling using Gaussian mixture models provides a consistently robust system for face, speaker and bi-modal authentication. It is also shown that multi-algorithm fusion provides a consistent performance improvement for face, speaker and bi-modal authentication. Using this bi-modal multi-algorithm system we derive a state-of-the-art authentication system that obtains a half total error rate of 6.3% and 1.9% for Female and Male trials, respectively.

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A Fast Parts-Based Approach to Speaker Verification Using Boosted Slice Classifiers

Cited by 23 publications

References 19 publications

Synchronous One Time Biometrics with Pattern Based Authentication

Synchronous One Time Biometrics with Pattern Based Authentication

Speaker Recognition: Advancements and Challenges

Bi-modal biometric authentication on mobile phones in challenging conditions

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