From one to many: Pose-Aware Metric Learning for single-sample face recognition

Deng, Weihong; Hu, Jiani; Wu, Zhongjun; Guo, Jun

doi:10.1016/j.patcog.2017.10.020

Cited by 17 publications

(10 citation statements)

References 20 publications

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“…Moreover, PCANet is also a reference for reviewing advanced deep learning architectures containing a large number of image classifications. Also, Deng, Hu, Wu, and Guo [14] put forward the creation of a face image to mitigate varying illumination and pose, respectively, using only one frontal face image to develop an extended generic elastic model (GEM) and a multidepth model. Pose-aware metric learning (PAML) was learned by means of linear regression to synthesize each pose in their corresponding metric space, and it yielded an accuracy of 100%.…”

Section: Related Workmentioning

confidence: 99%

Analysis and Implementation of Optimization Techniques for Facial Recognition

Appati

Abu

Owusu

et al. 2021

Applied Computational Intelligence and Soft Computing

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Amidst the wide spectrum of recognition methods proposed, there is still the challenge of these algorithms not yielding optimal accuracy against illumination, pose, and facial expression. In recent years, considerable attention has been on the use of swarm intelligence methods to help resolve some of these persistent issues. In this study, the principal component analysis (PCA) method with the inherent property of dimensionality reduction was adopted for feature selection. The resultant features were optimized using the particle swarm optimization (PSO) algorithm. For the purpose of performance comparison, the resultant features were also optimized with the genetic algorithm (GA) and the artificial bee colony (ABC). The optimized features were used for the recognition using Euclidean distance (EUD), K-nearest neighbor (KNN), and the support vector machine (SVM) as classifiers. Experimental results of these hybrid models on the ORL dataset reveal an accuracy of 99.25% for PSO and KNN, followed by ABC with 93.72% and GA with 87.50%. On the central, an experimentation of the PSO, GA, and ABC on the YaleB dataset results in 100% accuracy demonstrating their efficiencies over the state-of-the art methods.

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Section: Related Workmentioning

confidence: 99%

Analysis and Implementation of Optimization Techniques for Facial Recognition

Appati

Abu

Owusu

et al. 2021

Applied Computational Intelligence and Soft Computing

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“…When the multi-perspective light field is scheduled, [33][34][35][36] the important focus is how to sure maneuvering target just as the same target. This part uses the proposed algorithm to identify feature point matching targets for maneuvering targets entering the monitoring area.…”

Section: Maneuvering Target Recognitionmentioning

confidence: 99%

Maneuvering target recognition method based on multi-perspective light field reconstruction

Cai

Luo

Zhou

et al. 2019

International Journal of Distributed Sensor Networks

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It is difficult to reconstruct the complete light field, and the reconstructed light field can only recognize specific fixed targets. These have limited the applications of the light field in practice. To solve the problems above, this article introduces the multi-perspective distributed information fusion into light field reconstruction to monitor and recognize the maneuvering targets. First, the light field is represented as sub-light fields at different perspectives (i.e. the Multi-sensor distributed network), and sparse representation and reconstruction are then performed. Second, we establish the multi-perspective distributed information fusion under the condition of regional full-coverage constraints. Finally, the light field data from multiple perspectives are fused and the states of the maneuvering targets are estimated. Experimental results show that the light field reconstruction time of the proposed method is less than 583 s, and the reconstruction accuracy exceeds 92.447% compared with the existing spatially variable bidirectional reflectance distribution function, micro-lens array, and others. In the aspect of maneuvering target recognition, the recognition time of the algorithm in this article is no more than 3.5 s. The recognition accuracy of the algorithm in this article is up to 86.739%. Moreover, the more viewing angles used, the higher the accuracy.

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“…In [19], given single training images and exploiting a sufficiently rich bootstrap set, the method produces the corresponding 3D face rendering, being potentially able to synthesize images with any pose or illumination variations. This is achieved coupling a multi-depth 3D generic elastic model with the quotient image technique, aiming at synthesizing virtual faces with a desired illumination and expression, given a frontal image.…”

Section: Related Workmentioning

confidence: 99%

Robust Single-Sample Face Recognition by Sparsity-Driven Sub-Dictionary Learning Using Deep Features

Cuculo

D’Amelio

Grossi

et al. 2019

Sensors

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Face recognition using a single reference image per subject is challenging, above all when referring to a large gallery of subjects. Furthermore, the problem hardness seriously increases when the images are acquired in unconstrained conditions. In this paper we address the challenging Single Sample Per Person (SSPP) problem considering large datasets of images acquired in the wild, thus possibly featuring illumination, pose, face expression, partial occlusions, and low-resolution hurdles. The proposed technique alternates a sparse dictionary learning technique based on the method of optimal direction and the iterative sans-serifℓ0-norm minimization algorithm called k-LiMapS. It works on robust deep-learned features, provided that the image variability is extended by standard augmentation techniques. Experiments show the effectiveness of our method against the hardness introduced above: first, we report extensive experiments on the unconstrained LFW dataset when referring to large galleries up to 1680 subjects; second, we present experiments on very low-resolution test images up to 8×8 pixels; third, tests on the AR dataset are analyzed against specific disguises such as partial occlusions, facial expressions, and illumination problems. In all the three scenarios our method outperforms the state-of-the-art approaches adopting similar configurations.

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From one to many: Pose-Aware Metric Learning for single-sample face recognition

Cited by 17 publications

References 20 publications

Analysis and Implementation of Optimization Techniques for Facial Recognition

Analysis and Implementation of Optimization Techniques for Facial Recognition

Maneuvering target recognition method based on multi-perspective light field reconstruction

Robust Single-Sample Face Recognition by Sparsity-Driven Sub-Dictionary Learning Using Deep Features

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