SpPCANet: a simple deep learning-based feature extraction approach for 3D face recognition

Dutta, Koushik; Bhattacharjee, Debotosh; Nasipuri, Mita

doi:10.1007/s11042-020-09554-6

Cited by 14 publications

(20 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section we evaluate our proposed GS-PCANet image classification algorithm with other open-source histopathology image classification methods: SpPCANet method for image classification [46], multiple clustered instance learning (MCIL) for histopathology image classification [50], saliency-based dictionary learning (SDL) [34], analysis-synthesis learning with shared features (ASLF) [35], patch-based convolutional neural network (PCNN) [36], encoded local projections (ELP) for histopathology image classification [20], and weakly supervised deep learning (WSDL) for whole slide tissue classification [40]. We evaluate these seven methods using commonly used detection/classification measures: precision (P), recall (R), detection accuracy, F β -score, Tanimoto coefficient (T), and the receiver operating characteristic (ROC) curves along with the area under the curve (AUC).…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…When the training data is limited and/or noisy, as is often the case in medical imaging, these methods tend to show a performance degradation [43]. Another class of learning-based approaches involve orthogonal transformation of the data such as principal component analysis (PCA) transform to extract relevant features for image classification [30], [44]- [46]. These learning-based approaches using orthogonal transformation explore the data distribution to preserve global structures in the data.…”

Section: Introductionmentioning

confidence: 99%

“…Unlike other histopathology image classification methods, in this work we propose a baseline neural network method called GS-PCANet, which is different from prior methods [30], [44]- [46] in two aspects. 1) We include an additional sparsity promoting term in the PCA transformation so as to select more interpretable features from the images.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network

Ram¹,

Tang²,

Bell³

et al. 2021

Preprint

View full text Add to dashboard Cite

Early detection of lung cancer is critical for improvement of patient survival. To address the clinical need for efficacious treatments, genetically engineered mouse models (GEMM) have become integral in identifying and evaluating the molecular underpinnings of this complex disease that may be exploited as therapeutic targets. Assessment of GEMM tumor burden on histopathological sections performed by manual inspection is both time consuming and prone to subjective bias. Therefore, an interplay of needs and challenges exists for computer-aided diagnostic tools, for accurate and efficient analysis of these histopathology images. In this paper, we propose a simple machine learning approach called the graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E). Our method comprises four steps: 1) cascaded graph-based sparse PCA, 2) PCA binary hashing, 3) block-wise histograms, and 4) support vector machine (SVM) classification. In our proposed architecture, graph-based sparse PCA is employed to learn the filter banks of the multiple stages of a convolutional network. This is followed by PCA hashing and block histograms for indexing and pooling. The meaningful features extracted from this GS-PCA are then fed to an SVM classifier. We evaluate the performance of the proposed algorithm on H&E slides obtained from an inducible K-ras G12D lung cancer mouse model using precision/recall rates, F β -score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC) and show that our algorithm is efficient and provides improved detection accuracy compared to existing algorithms.

show abstract

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network

Ram¹,

Tang²,

Bell³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Unlike Jing Luo et al [9] and others who directly fused two-dimensional face features with depth map, Xu Kangming et al [10] fused depth features extracted based on a convolutional neural network. Koushik Dutta et al [11] proposed a lightweight deep learning network sppcanet for feature extraction and used a linear support vector machine (SVM) to classify the extracted features.…”

Section: Related Work a 3d Face Recognitionmentioning

confidence: 99%

RP-Net: A PointNet++ 3D Face Recognition Algorithm Integrating RoPS Local Descriptor

2022

View full text Add to dashboard Cite

as a biometric identification method in the post-epidemic era, face recognition owing more and more attention in practical applications to its non-contact and interaction-friendly advantages. Researchers more favor 3D faces because they have richer spatial information than 2D faces and are not easily affected by the environment. However 3D faces are not all collected in normal environments. To enhance the facial features of 3D faces and improve the recognition degree of 3D faces in weak-light or dark environments, a 3D face recognition algorithm based on point cloud depth learning is proposed. First, 3D faces are automatically detected from 3D raw data and preprocessed, including nose-tip detection and face cropping, spike removal and hole filling, and surface normals. Then, rotated projection statistical local feature descriptors (RoPS) are integrated into the PointNet++ network to describe and classify local features. Finally, feature matching is performed using the nearest neighbor distance ratio. The algorithm was tested on the Bosphorus and CASIA-3D datasets, and good results were obtained in a simulated weak-light environment.

show abstract

“…Dutta at al. [138] proposed a lightweight sparse principal component analysis network (SpPCANet). It includes three parts: convolutional layer, nonlinear processing layer and feature merging layer.…”

Section: B 3d Face Recognitionmentioning

confidence: 99%

3D Face Recognition: A Survey

Jing,

Lu,

Gao

2021

Preprint

View full text Add to dashboard Cite

Face recognition is one of the most studied research topics in the community. In recent years, the research on face recognition has shifted to using 3D facial surfaces, as more discriminating features can be represented by the 3D geometric information. This survey focuses on reviewing the 3D face recognition techniques developed in the past ten years which are generally categorized into conventional methods and deep learning methods. The categorized techniques are evaluated using detailed descriptions of the representative works. The advantages and disadvantages of the techniques are summarized in terms of accuracy, complexity and robustness to face variation (expression, pose and occlusions, etc). The main contribution of this survey is that it comprehensively covers both conventional methods and deep learning methods on 3D face recognition. In addition, a review of available 3D face databases is provided, along with the discussion of future research challenges and directions.

show abstract

SpPCANet: a simple deep learning-based feature extraction approach for 3D face recognition

Cited by 14 publications

References 35 publications

Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network

Lung Cancer Lesion Detection in Histopathology Images Using Graph-Based Sparse PCA Network

RP-Net: A PointNet++ 3D Face Recognition Algorithm Integrating RoPS Local Descriptor

3D Face Recognition: A Survey

Contact Info

Product

Resources

About