A supervised multimanifold method with locality preserving for face recognition using single sample per person

Nabipour, Mehrasa; Aghagolzadeh, Ali; Motameni, Homayun

doi:10.1007/s11771-017-3700-9

Cited by 9 publications

(4 citation statements)

References 19 publications

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“…Table 13 presents the highest accuracies obtained using the same subsets and the same assessment protocol with Subset A as the training set and subsets of facial expression variations B, C, D, N, O, and P constituting the test set. The results presented in Table 13 are taken from several references [ 36 , 39 , 53 , 54 ]. “- -” signifies that the considered method has no experimental results.…”

Section: Experimental Analysismentioning

confidence: 99%

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

Adjabi

Ouahabi

Benzaoui

et al. 2021

Sensors

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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.

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Section: Experimental Analysismentioning

confidence: 99%

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

Adjabi

Ouahabi

Benzaoui

et al. 2021

Sensors

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“…Image regions can be conveniently represented with histograms of the pixels' binary codes. Other descriptors that generate binary codes, such as the local binary pattern (LBP) [46] and the local phase quantization (LPQ) [47], have inspired the BSIF process. However, the BSIF is based on natural image statistics, rather than heuristic or handcrafted code constructions, enhancing its modeling capabilities.…”

Section: Mb-c-bsif-based Feature Extractionmentioning

confidence: 99%

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

Adjabi¹,

Benzaoui²,

Ouahabi³

et al. 2020

Preprint

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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, particularly when dealing with changes in facial expression, posture, lighting, and occlusion. This paper suggests a different method based on a variant of the Binarized Statistical Image Features (BSIF) descriptor called Multi-Block Color-Binarized Statistical Image Features (MB-C-BSIF) to resolve the SSFR Problem. 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 &amp; Robert (AR) and Labeled Faces in the Wild (LFW) databases show that the MB-C-BSIF achieves superior results in unconstrained situations when compared to current state-of-the-art methods, especially when dealing with changes in facial expression, lighting, and occlusion. Furthermore, the suggested method employs algorithms with lower computational cost, making it ideal for real-time applications.

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“…The authors claimed that their framework can achieve superior performance than other state-of-theart feature learning algorithms for OSPP face recognition. In [7] the authors presented a supervised learning method called supervised locality preserving multimanifold (SLPMM). In their approach, two graphs are made to represent the information inside every manifold and among different manifolds, with this it simultaneously maximizes the between-manifold scatter and minimizes the within-manifold scatter.…”

Section: Related Workmentioning

confidence: 99%

“…There are some recent works which attempt to solve the OSPP problem along with issues from image acquisition. Among them, there are methods employing sparse representation classifier [1] [4] [5], two-layer neural local-to-global feature learning framework [6], supervised locality preserving multimanifold (SLPMM) [7], single hidden layer analytic Gabor feedforward neural network (AGFN) [2] and multiple feature subspaces analysis (MFSA) [8]. Despite these, an increasing number of bioinspired FR systems had emerged due to their intelligent problem-solving ability, scalability, flexibility, and adaptive nature [9].…”

Section: Introductionmentioning

confidence: 99%

A face recognition framework using self-adaptive differential evolution

Plichoski¹,

Chidambaram²,

Parpinelli³

2019

Learn. Nonlin. Mod.

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It is well known that the development of face recognition (FR) systems is challenging under uncontrolled conditions often related to the variation of pose, illumination, expression, and occlusion. Also, to collect the necessary amount of images is hard to guarantee in many situations, e.g. ID cards, drivers licenses or visas, leading to the one sample per person (OSPP) problem. This work addresses the OSPP problem along with illumination and pose variation using an FR framework composed of a self-adaptive Differential Evolution, named FRjDE. The main feature of the framework stands on the use of the optimization algorithm for choosing which preprocessing and feature extraction strategies to use, as well as tunning their parameters. Also, by using the jDE algorithm, F and CR control parameters are self-adapted. Experiments are made using two well-known databases, named CMU-PIE and FERET. Results obtained from the FRjDE approach are compared against the FR framework using the standard DE algorithm and against results found in the literature. Results suggest that the proposed approach is highly competitive and well suited for face recognition.

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A supervised multimanifold method with locality preserving for face recognition using single sample per person

Cited by 9 publications

References 19 publications

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

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

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

A face recognition framework using self-adaptive differential evolution

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