Face recognition based on dictionary learning and subspace learning

Liao, Mengmeng; Gu, Xiaodong

doi:10.1016/j.dsp.2019.04.006

Cited by 25 publications

(8 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(3) the computational overhead is relatively small; (4) the clustering accuracy can be theoretically guaranteed under certain conditions. And the method has been widely used in real-world applications, such as the Internet-of-Things (IoT) [19], human motion segmentation [20,21], and face recognition [22,23,24]. In particular, Sparse Subspace Clustering (SSC) [25] and Low-Rank Representation (LRR) are foundational studies, which provide a standard learning paradigm:…”

Section: Related Workmentioning

confidence: 99%

Partially latent factors based multi-view subspace learning

Lu¹,

Liu²,

Liu³

et al. 2022

Preprint

View full text Add to dashboard Cite

Multi-view subspace clustering always performs well in high-dimensional data analysis, but is sensitive to the quality of data representation. To this end, a two stage fusion strategy is proposed to embed representation learning into the process of multi-view subspace clustering. This paper first propose a novel matrix factorization method that can separate the coupling consistent and complementary information from observations of multiple views. Based on the obtained latent representations, we further propose two subspace clustering strategies: feature-level fusion and subspace-level hierarchical strategy. Feature-level method concatenates all kinds of latent representations from multiple views, and the original problem therefore degenerates to a single-view subspace clustering process. Subspace-level hierarchical method performs different self-expressive reconstruction processes on the corresponding complementary and consistent latent representations coming from each view, i.e. the prior constraints imposed on different types of subspace representations are related to the appropriate input factors. Finally, extensive experimental results on real-world datasets demonstrate the superiority of our proposed methods by comparing against some state-of-the-art subspace clustering algorithms.

show abstract

Section: Related Workmentioning

confidence: 99%

Partially latent factors based multi-view subspace learning

Lu¹,

Liu²,

Liu³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Another method for face recognition based on dictionary learning and subspace learning (DLSL) is introduced in [47]. This new approach efficiently handles corrupted data, including noise or face variations (e.g., occlusion and significant pose variation).…”

Section: Subspace-based Learningmentioning

confidence: 99%

Grassmannian learning mutual subspace method for image set recognition

Souza¹,

Sogi²,

Gatto³

et al. 2021

Preprint

View full text Add to dashboard Cite

This paper addresses the problem of object recognition given a set of images as input (e.g., multiple camera sources and video frames). Convolutional neural network (CNN)-based frameworks do not exploit these sets effectively, processing a pattern as observed, not capturing the underlying feature distribution as it does not consider the variance of images in the set. To address this issue, we propose the Grassmannian learning mutual subspace method (G-LMSM), a NN layer embedded on top of CNNs as a classifier, that can process image sets more effectively and can be trained in an end-to-end manner. The image set is represented by a low-dimensional input subspace; and this input subspace is matched with reference subspaces by a similarity of their canonical angles, an interpretable and easy to compute metric. The key idea of G-LMSM is that the reference subspaces are learned as points on the Grassmann manifold, optimized with Riemannian stochastic gradient descent. This learning is stable, efficient and theoretically well-grounded. We demonstrate the effectiveness of our proposed method on hand shape recognition, face identification, and facial emotion recognition.

show abstract

“…There is increasing demand for an image classification system that can perform automatic facial recognition tasks [11][12][13]. Several studies have investigated facial recognition and facial perception.…”

Section: Related Workmentioning

confidence: 99%

“…Automatic facial processing [11] is a reliable method and realistic approach for facial recognition. It benefits from the use of deep neural networks [12], dictionary learning [13], and automatic partial learning. These tools can be utilized to create a practical face dataset using inexpensive digital cameras or video recorders.…”

Section: Related Workmentioning

confidence: 99%

Digital Forensics for Skulls Classification in Physical Anthropology Collection Management

Yuadi¹,

Artaria²,

Asyhari³

et al. 2021

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

The size, shape, and physical characteristics of the human skull are distinct when considering individual humans. In physical anthropology, the accurate management of skull collections is crucial for storing and maintaining collections in a cost-effective manner. For example, labeling skulls inaccurately or attaching printed labels to skulls can affect the authenticity of collections. Given the multiple issues associated with the manual identification of skulls, we propose an automatic human skull classification approach that uses a support vector machine and different feature extraction methods such as gray-level co-occurrence matrix features, Gabor features, fractal features, discrete wavelet transforms, and combinations of features. Each underlying facial bone exhibits unique characteristics essential to the face's physical structure that could be exploited for identification. Therefore, we developed an automatic recognition method to classify human skulls for consistent identification compared with traditional classification approaches. Using our proposed approach, we were able to achieve an accuracy of 92.3-99.5% in the classification of human skulls with mandibles and an accuracy of 91.4-99.9% in the classification of human skills without mandibles. Our study represents a step forward in the construction of an effective automatic human skull identification system with a classification process that achieves satisfactory performance for a limited dataset of skull images.

show abstract

Face recognition based on dictionary learning and subspace learning

Cited by 25 publications

References 43 publications

Partially latent factors based multi-view subspace learning

Partially latent factors based multi-view subspace learning

Grassmannian learning mutual subspace method for image set recognition

Digital Forensics for Skulls Classification in Physical Anthropology Collection Management

Contact Info

Product

Resources

About