Audio-visual biometric recognition via joint sparse representations

In this paper we present a novel audio-visual (AV) person identification system based on joint sparse representation. Video features used were vectorized raw pixel values, while i-vectors were used as the audio features. Classification is performed by solving the joint sparsity optimization problem,...

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Bibliographic Details
Published in2016 23rd International Conference on Pattern Recognition (ICPR) pp. 3031 - 3035
Main Authors Primorac, Rudi, Togneri, Roberto, Bennamoun, Mohammed, Sohel, Ferdous
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2016
Subjects
audio-visual biometric recognition
Australia
Biometrics (access control)
Dictionaries
Face
Feature extraction
joint sparse representations
Training
Visualization
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Summary:In this paper we present a novel audio-visual (AV) person identification system based on joint sparse representation. Video features used were vectorized raw pixel values, while i-vectors were used as the audio features. Classification is performed by solving the joint sparsity optimization problem, and fusion is carried out by using the quality (confidence) assigned to each matcher. Our experimental results on the challenging MOBIO database using 100 subjects show that the system based on joint sparse representation outperforms the system based on separate sparse representations for each modality. Furthermore, we show that our newly introduced quality measure improves the system's performance, when compared to conventionally used quality measures for sparse representation - based systems.
DOI:10.1109/ICPR.2016.7900099