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Multi-scale multi-block covariance descriptor with feature selection

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Abstract

This paper investigates a compact face texture representation able to cover the most discriminant features of facial images. The compactness is achieved by the proposed Pyramid Multi-Level (PML) covariance texture descriptor and the feature selection process that is applied on the raw extracted features. In fact, we introduce a framework based mainly on two new aspects. Firstly, we consider an extension of the original covariance descriptor that relies on de-noised covariance matrices obtained using texture descriptors such as local binary pattern and quaternionic local ranking binary pattern images. Secondly, we exploit the resulting covariance descriptor using a PML face representation which allows a multi-level multi-scale feature extraction. Experiments conducted on four public face datasets show the efficacy of the proposed face descriptor and the associated selection schemes.

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  1. University of the Basque Country (UPV/EHU), San Sebastián, Spain

    Abdelmalik Moujahid & Fadi Dornaika

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  1. Abdelmalik Moujahid
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  2. Fadi Dornaika
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Correspondence to Abdelmalik Moujahid.

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Moujahid, A., Dornaika, F. Multi-scale multi-block covariance descriptor with feature selection. Neural Comput & Applic 32, 6283–6294 (2020). https://doi.org/10.1007/s00521-019-04135-7

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