Journal ArticleDOI
A Two-Phase Weighted Collaborative Representation for 3D partial face recognition with single sample
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TLDR
Experimental results on six challenging 3D facial datasets show that the proposed KMTS-TPWCRC framework achieves promising results for human face recognition with missing parts, occlusions, data corruptions, expressions and pose variations.About:
This article is published in Pattern Recognition.The article was published on 2016-04-01. It has received 92 citations till now. The article focuses on the topics: Facial recognition system & Face (geometry).read more
Citations
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Journal ArticleDOI
A survey of local feature methods for 3D face recognition
TL;DR: This survey presents a state-of-the-art for 3D face recognition using local features, with the main focus being the extraction of these features.
Proceedings ArticleDOI
Learning from Millions of 3D Scans for Large-Scale 3D Face Recognition
TL;DR: Wang et al. as mentioned in this paper proposed a method for generating a large corpus of labeled 3D face identities and their multiple instances for training and a protocol for merging the most challenging existing 3D datasets for testing.
Journal ArticleDOI
TOLDI: An effective and robust approach for 3D local shape description
TL;DR: Experimental results and comparisons with the state-of-the-arts validate the effectiveness, robustness, high efficiency, and overall superiority of the TOLDI method for local shape description.
Proceedings ArticleDOI
Deep 3D face identification
TL;DR: In this paper, a 3D face recognition algorithm using a deep convolutional neural network (DCNN) and a three-dimensional face expression augmentation technique was proposed. But, the authors only used a small number of 3D facial scans.
Journal ArticleDOI
Joint and collaborative representation with local adaptive convolution feature for face recognition with single sample per person
TL;DR: By densely sampling and sparsely detecting facial points, JCR-ACF is extracted and robust local regions are learned and convolution features adaptive to the local regions and discriminative to the face identity are learned by using convolutional neural networks (CNN).
References
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Journal ArticleDOI
Robust Face Recognition via Sparse Representation
TL;DR: This work considers the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise, and proposes a general classification algorithm for (image-based) object recognition based on a sparse representation computed by C1-minimization.
Journal ArticleDOI
Face recognition: A literature survey
TL;DR: In this paper, the authors provide an up-to-date critical survey of still-and video-based face recognition research, and provide some insights into the studies of machine recognition of faces.
Proceedings ArticleDOI
Locality-constrained Linear Coding for image classification
TL;DR: This paper presents a simple but effective coding scheme called Locality-constrained Linear Coding (LLC) in place of the VQ coding in traditional SPM, using the locality constraints to project each descriptor into its local-coordinate system, and the projected coordinates are integrated by max pooling to generate the final representation.
Proceedings ArticleDOI
Overview of the face recognition grand challenge
P.J. Phillips,Patrick J. Flynn,T. Scruggs,Kevin W. Bowyer,Jin Chang,K. Hoffman,J. Marques,Jaesik Min,William J. Worek +8 more
TL;DR: The face recognition grand challenge (FRGC) is designed to achieve this performance goal by presenting to researchers a six-experiment challenge problem along with data corpus of 50,000 images.
Proceedings ArticleDOI
Sparse representation or collaborative representation: Which helps face recognition?
TL;DR: This paper indicates that it is the CR but not the l1-norm sparsity that makes SRC powerful for face classification, and proposes a very simple yet much more efficient face classification scheme, namely CR based classification with regularized least square (CRC_RLS).