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Open AccessArticle

Multi-Block Color-Binarized Statistical Images for Single-Sample Face Recognition

1
Department of Computer Science, LIMPAF, University of Bouira, Bouira 10000, Algeria
2
Polytech Tours, Imaging and Brain, INSERM U930, University of Tours, 37200 Tours, France
3
Department of Electrical Engineering, University of Bouira, Bouira 10000, Algeria
4
GREMAN UMR 7347, University of Tours, CNRS, INSA Centre Val-de-Loire, 37200 Tours, France
*
Author to whom correspondence should be addressed.
Academic Editor: Kang Ryoung Park
Sensors 2021, 21(3), 728; https://doi.org/10.3390/s21030728
Received: 8 December 2020 / Revised: 14 January 2021 / Accepted: 19 January 2021 / Published: 21 January 2021
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. First, the MB-C-BSIF method decomposes a facial image into three channels (e.g., red, green, and blue), then it divides each channel into equal non-overlapping blocks to select the local facial characteristics that are consequently employed in the classification phase. Finally, the identity is determined by calculating the similarities among the characteristic vectors adopting a distance measurement of the K-nearest neighbors (K-NN) classifier. Extensive experiments on several subsets of the unconstrained Alex and Robert (AR) and Labeled Faces in the Wild (LFW) databases show that the MB-C-BSIF achieves superior and competitive results in unconstrained situations when compared to current state-of-the-art methods, especially when dealing with changes in facial expression, lighting, and occlusion. 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. View Full-Text
Keywords: biometrics; face recognition; single-sample face recognition; binarized statistical image features; K-nearest neighbors biometrics; face recognition; single-sample face recognition; binarized statistical image features; K-nearest neighbors
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MDPI and ACS Style

Adjabi, I.; Ouahabi, A.; Benzaoui, A.; Jacques, S. Multi-Block Color-Binarized Statistical Images for Single-Sample Face Recognition. Sensors 2021, 21, 728. https://doi.org/10.3390/s21030728

AMA Style

Adjabi I, Ouahabi A, Benzaoui A, Jacques S. Multi-Block Color-Binarized Statistical Images for Single-Sample Face Recognition. Sensors. 2021; 21(3):728. https://doi.org/10.3390/s21030728

Chicago/Turabian Style

Adjabi, Insaf; Ouahabi, Abdeldjalil; Benzaoui, Amir; Jacques, Sébastien. 2021. "Multi-Block Color-Binarized Statistical Images for Single-Sample Face Recognition" Sensors 21, no. 3: 728. https://doi.org/10.3390/s21030728

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