What is it about?
This document presents the implementation of an original method to solve the problem of facial recognition from a single picture of the person, i.e. a single image; the latter can be difficult to exploit, in particular because of changes in facial expression, posture, lighting or even occultation (example of the mask in the difficult health context that we have been experiencing for many months).
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Why is it important?
Single-Sample Face Recognition (SSFR) is a computer vision challenge. In this scenario, there is only one example from each individual on which to train the system, making it difficult to identify persons in unconstrained environments, mainly when dealing with changes in facial expression, posture, lighting, and occlusion. This paper discusses the relevance of an original method for SSFR, called Multi-Block Color-Binarized Statistical Image Features (MB-C-BSIF), which exploits several kinds of features, namely, local, regional, global, and textured-color characteristics. The average classification accuracies are 96.17% and 99% for the AR database with two specific protocols (i.e., Protocols I and II, respectively), and 38.01% for the challenging LFW database. These performances are clearly superior to those obtained by state-of-the-art methods. Furthermore, the proposed method uses algorithms based only on simple and elementary image processing operations that do not imply higher computational costs as in holistic, sparse or deep learning methods, making it ideal for real-time identification.
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Read the Original
This page is a summary of: Multi-Block Color-Binarized Statistical Images for Single-Sample Face Recognition, Sensors, January 2021, MDPI AG,
DOI: 10.3390/s21030728.
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