Fully Associative Patch-Based 1-to-N Matcher for Face Recognition

Zhang, Lingfeng; Kakadiaris, Ioannis A.

doi:10.1109/icb2018.2018.00032

Cited by 2 publications

(2 citation statements)

References 50 publications

(61 reference statements)

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“…The evaluation of the proposed HML matcher on the UR2D system is evaluated with two types of face recognition scenarios: constrained environment and unconstrained environment. The datasets used for testing are the UHDB31 dataset [49,54] and the IJB-A dataset [55,56]. The UHDB31 dataset contains 29,106 color face images of 77 subjects with 21 poses and 18 illuminations.…”

Section: Signature S T L Evaluationmentioning

confidence: 99%

Patch-based face recognition using a hierarchical multi-label matcher

Zhang¹,

Dou²,

Kakadiaris³

2018

Image and Vision Computing

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This paper proposes a hierarchical multi-label matcher for patch-based face recognition. In signature generation, a face image is iteratively divided into multi-level patches. Two different types of patch divisions and signatures are introduced for 2D facial image and texture-lifted image, respectively. The matcher training consists of three steps. First, local classifiers are built to learn the local matching of each patch. Second, the hierarchical relationships defined between local patches are used to learn the global matching of each patch. Three ways are introduced to learn the global matching: majority voting, 1 -regularized weighting, and decision rule. Last, the global matchings of different levels are combined as the final matching. Experimental results on different face recognition tasks demonstrate the effectiveness of the proposed matcher at the cost of gallery generalization. Compared with the UR2D system, the proposed matcher improves the Rank-1 accuracy significantly by 3% and 0.18% on the UHDB31 dataset and IJB-A dataset, respectively.

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Section: Signature S T L Evaluationmentioning

confidence: 99%

Patch-based face recognition using a hierarchical multi-label matcher

Zhang¹,

Dou²,

Kakadiaris³

2018

Image and Vision Computing

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“…For patch-based CNN model in the UR2D system, the global model feature contains two part: feature matrix and occlusion encoding. The feature size is 8 × 512 + 8 based on DPRFS signature [48,21,49]. Cosine similarity is applied to compute the global matching vector.…”

Section: Global Model Component: S Gmentioning

confidence: 99%

A Hierarchical Matcher using Local Classifier Chains

Zhang,

Kakadiaris

2018

Preprint

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This paper focuses on improving the performance of current convolutional neural networks in visual recognition without changing the network architecture. A hierarchical matcher is proposed that builds chains of local binary neural networks after one global neural network over all the class labels, named as Local Classifier Chains based Convolutional Neural Network (LCC-CNN). The signature of each sample as two components: global component based on the global network; local component based on local binary networks. The local networks are built based on label pairs created by a similarity matrix and confusion matrix. During matching, each sample travels through one global network and a chain of local networks to obtain its final matching to avoid error propagation. The proposed matcher has been evaluated with image recognition, character recognition and face recognition datasets. The experimental results indicate that the proposed matcher achieves better performance when compared with methods using only a global deep network. Compared with the UR2D system, the accuracy is improved significantly by 1% and 0.17% on the UHDB31 dataset and the IJB-A dataset, respectively.

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Fully Associative Patch-Based 1-to-N Matcher for Face Recognition

Cited by 2 publications

References 50 publications

Patch-based face recognition using a hierarchical multi-label matcher

Patch-based face recognition using a hierarchical multi-label matcher

A Hierarchical Matcher using Local Classifier Chains

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