Single sample per person face recognition based on deep convolutional neural network

Zeng, Junying; Zhao, Xiaoxiao; Qin, Chuanbo; Lin, Zuoyong

doi:10.1109/compcomm.2017.8322819

Cited by 15 publications

(13 citation statements)

References 12 publications

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“…They can automatically determine complex non-linear data structures [40]. Zeng et al (2017) [41] proposed a method that uses Deep Convolutional Neural Networks (DCNNs). Firstly, they propose using an expanding sample technique to augment the training sample set, and then a trained DCNN model is implemented and fine-tuned by those expanding samples to be used in the classification process.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

“…Compared to related SSFRs, which can be categorized as either generic learning methods (e.g., ESRC [ 31 ], SVDL [ 32 ], and LGR [ 33 ], image partitioning methods (e.g., CRC [ 35 ], PCRC [ 34 ], and DNNC [ 39 ]) or deep learning methods (e.g., DCNN [ 41 ] and BDL [ 44 ]), the capabilities of our method can be explained in terms of its exploitation of different forms of information. This can be summarized as follows: The BSIF descriptor scans the image pixel by pixel, i.e., we consider the benefits of local information.…”

Section: Experimental Analysismentioning

confidence: 99%

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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: Deep Learning Methodsmentioning

confidence: 99%

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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“…Lu et al (2013) [33] suggested a technique called Discriminant Multimanifold Analysis (DMMA) that divides any registered image into multiple non-overlapping blocks and then learns several feature spaces to optimize the various margins of different individuals. [34] developed local histogram-based face image operators. They decomposed each image into different non-overlapping blocks.…”

Section: Image Partitioning Methodsmentioning

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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“…This is an extended version of our conference papers [ 13 , 14 ]. The contributions of this paper are shown as follows: We propose a novel expanding sample method.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Network Used in Single Sample per Person Face Recognition

Zeng

Zhao

Gan

et al. 2018

Computational Intelligence and Neuroscience

Self Cite

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Face recognition (FR) with single sample per person (SSPP) is a challenge in computer vision. Since there is only one sample to be trained, it makes facial variation such as pose, illumination, and disguise difficult to be predicted. To overcome this problem, this paper proposes a scheme combined traditional and deep learning (TDL) method to process the task. First, it proposes an expanding sample method based on traditional approach. Compared with other expanding sample methods, the method can be used easily and conveniently. Besides, it can generate samples such as disguise, expression, and mixed variation. Second, it uses transfer learning and introduces a well-trained deep convolutional neural network (DCNN) model and then selects some expanding samples to fine-tune the DCNN model. Third, the fine-tuned model is used to implement experiment. Experimental results on AR face database, Extend Yale B face database, FERET face database, and LFW database demonstrate that TDL achieves the state-of-the-art performance in SSPP FR.

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Single sample per person face recognition based on deep convolutional neural network

Cited by 15 publications

References 12 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

Deep Convolutional Neural Network Used in Single Sample per Person Face Recognition

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