Robust watch-list screening using dynamic ensembles of SVMs based on multiple face representations

Bashbaghi, Saman; Granger, Éric; Sabourin, Robert; Bilodeau, Guillaume-Alexandre

doi:10.1007/s00138-016-0820-4

Cited by 15 publications

(11 citation statements)

References 55 publications

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“…5 for each ri do 6 Generate q synthetic images Si ∈ R d×q per each individual based on q representative pose angle. 7 Merge Si with ri to form D i ∈ R d×(1+q) .…”

Section: Synthetic Plus Variational Encodingmentioning

confidence: 99%

A paired sparse representation model for robust face recognition from a single sample

Mokhayeri

Granger

2020

Pattern Recognition

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Sparse representation-based classification (SRC) has been shown to achieve a high level of accuracy in face recognition (FR). However, matching faces captured in unconstrained video against a gallery with a single reference facial still per individual typically yields low accuracy. For improved robustness to intra-class variations, SRC techniques for FR have recently been extended to incorporate variational information from an external generic set into an auxiliary dictionary. Despite their success in handling linear variations, non-linear variations (e.g., pose and expressions) between probe and reference facial images cannot be accurately reconstructed with a linear combination of images in the gallery and auxiliary dictionaries because they do not share the same type of variations. In order to account for non-linear variations due to pose, a paired sparse representation model is introduced allowing for joint use of variational information and synthetic face images. The proposed model, called synthetic plus variational model, reconstructs a probe image by jointly using (1) a variational dictionary and (2) a gallery dictionary augmented with a set of synthetic images generated over a wide diversity of pose angles. The augmented gallery dictionary is then encouraged to pair the same sparsity pattern with the variational dictionary for similar pose angles by solving a newly formulated simultaneous sparsity-based optimization problem. Experimental results obtained on Chokepoint and COX-S2V datasets, using different face representations, indicate that the proposed approach can outperform state-of-the-art SRC-based methods for still-to-video FR with a single sample per person.

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“…5 for each ri do 6 Generate q synthetic images Si ∈ R d×q per each individual based on q representative pose angle. 7 Merge Si with ri to form D i ∈ R d×(1+q) .…”

Section: Synthetic Plus Variational Encodingmentioning

confidence: 99%

A paired sparse representation model for robust face recognition from a single sample

Mokhayeri

Granger

2020

Pattern Recognition

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“…The ensemble is selected in a dynamic fashion according to each new test sample. This property makes dynamic ensemble selection techniques a robust approach to deal with many pattern recognition applications, such as, handwritten recognition [7], face recognition [33], remote sensing image classification [34], offline signature verification [35] and the recognition of EMG signals in a bio-prosthetic hand [36].…”

Section: Dynamic Selectionmentioning

confidence: 99%

META-DES.Oracle: Meta-learning and feature selection for dynamic ensemble selection

2017

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Dynamic ensemble selection (DES) techniques work by estimating the competence level of each classifier from a pool of classifiers, and selecting only the most competent ones for the classification of a specific test sample. The key issue in DES is defining a suitable criterion for calculating the classifiers' competence. There are several criteria available to measure the level of competence of base classifiers, such as local accuracy estimates and ranking. However, using only one criterion may lead to a poor estimation of the classifier's competence. In order to deal with this issue, we have proposed a novel dynamic ensemble selection framework using meta-learning, called META-DES. A meta-classifier is trained, based on the meta-features extracted from the training data, to estimate the level of competence of a classifier for the classification of a given query sample. An important aspect of the META-DES framework is that multiple criteria can be embedded in the system encoded as different sets of meta-features. However, some DES criteria are not suitable for every classification problem. For instance, local accuracy estimates may produce poor results when there is a high degree of overlap between the classes. Moreover, a higher classification accuracy can be obtained if the performance of the meta-classifier is optimized for the corresponding data. In this paper, we propose a novel version of the META-DES framework based on the formal definition of the Oracle, called META-DES.Oracle. The Oracle is an abstract method that represents an ideal classifier selection scheme. A meta-feature selection scheme using an overfitting cautious Binary Particle Swarm Optimization (BPSO) is proposed for improving the performance of the meta-classifier. The difference between the outputs obtained by the meta-classifier and those presented by the Oracle is minimized. Thus, the meta-classifier is expected to obtain results that are similar to the Oracle. Experiments carried out using 30 classifi-1 arXiv:1811.00217v1 [cs.LG] 1 Nov 2018 cation problems demonstrate that the optimization procedure based on the Oracle definition leads to a significant improvement in classification accuracy when compared to previous versions of the META-DES framework and other state-of-the-art DES techniques.

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“…airport baggage claim areas, and subway stations). Two common types of applications in VS are: (1) still-to-video FR (e.g., watch-list screening), and (2) video-to-video FR (e.g., face re-identification or search and retrieval) [4,11,23]. In the former application, reference face images or stills of target individuals of interest are used to design facial models, while in the latter, facial models are designed using faces captured in reference videos.…”

Section: Introductionmentioning

confidence: 99%

“…In spite of improvements achieved using the conventional methods, yet they are less robust to real-world still-to-video FR scenario. On the other hand, there exists no feature extraction technique that can overcome all the challenges encountered in VS individually [4,15,34].…”

Section: Introductionmentioning

confidence: 99%

“…These techniques seek to enhance the robustness of face models to intra-class variations. In multiple representations, different patches and face descriptors are employed [2,4], while 2D morphing or 3D reconstructions are used to synthesize artificial face images [16,22]. A generic auxiliary dataset containing faces of other persons can be exploited to perform domain adaptation [20], and sparse representation classification through dictionary learning [36].…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Architectures for Face Recognition in Video Surveillance

Bashbaghi

Granger

Sabourin

et al. 2019

Deep Learning in Object Detection and Recognition

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Face recognition (FR) systems for video surveillance (VS) applications attempt to accurately detect the presence of target individuals over a distributed network of cameras. In video-based FR systems, facial models of target individuals are designed a priori during enrollment using a limited number of reference still images or video data. These facial models are not typically representative of faces being observed during operations due to large variations in illumination, pose, scale, occlusion, blur, and to camera inter-operability. Specifically, in still-to-video FR application, a single high-quality reference still image captured with still camera under controlled conditions is employed to generate a facial model to be matched later against lower-quality faces captured with video cameras under uncontrolled conditions. Current video-based FR systems can perform well on controlled scenarios, while their performance is not satisfactory in uncontrolled scenarios mainly because of the differences between the source (enrollment) and the target (operational) domains. Most of the efforts in this area have been toward the design of robust video-based FR systems in unconstrained surveillance environments. This chapter presents an overview of recent advances in still-to-video FR scenario through deep convolutional neural networks (CNNs). In particular, deep learning architectures proposed in the literature based on triplet-loss function (e.g., cross-correlation matching CNN, trunk-branch ensemble CNN and HaarNet) and supervised autoencoders (e.g., canonical face representation CNN) are reviewed and compared in terms of accuracy and computational complexity.

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Robust watch-list screening using dynamic ensembles of SVMs based on multiple face representations

Cited by 15 publications

References 55 publications

A paired sparse representation model for robust face recognition from a single sample

A paired sparse representation model for robust face recognition from a single sample

META-DES.Oracle: Meta-learning and feature selection for dynamic ensemble selection

Deep Learning Architectures for Face Recognition in Video Surveillance

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