Deep coupling neural network for robust facial landmark detection

Wu, Wenyan; Wu, Xingzhe; Cai, Yici; Zhou, Qiang

doi:10.1016/j.cag.2019.05.031

Cited by 8 publications

(2 citation statements)

References 8 publications

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“…The result shows that ANN-PSOCoG outperformed other kinds of ANN models. Wu et al [33] came up with an innovative deep coupling neural network (DCNN), which consists of a dataset-across network (DA-Net) and candidate-decision network (CD-Net), and the result shows that model robustness can be significantly improved by leveraging rich variations within and between different datasets. In the field of medicine, Kamnitsas et al [34] presented a dual pathway, an 11-layer deep 3D convolutional neural network for the challenging task of brain lesion segmentation, and they analyze the development of a deeper and more discriminative 3D CNN.…”

Section: Preliminaries 21 Related Workmentioning

confidence: 99%

GRU Neural Network Based on CEEMDAN–Wavelet for Stock Price Prediction

Ren

2023

Applied Sciences

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Stock indices are considered to be an important indicator of financial market volatility in various countries. Therefore, the stock market forecast is one of the challenging issues to decrease the uncertainty of the future direction of financial markets. In recent years, many scholars attempted to use different conventional statistical and deep learning methods to predict stock indices. However, the non-linear financial noise data will usually cause stochastic deterioration and time lag in forecast results, resulting in existing neural networks that do not demonstrate good prediction results. For this reason, we propose a novel framework to combine the gated recurrent unit (GRU) neural network with the complete ensemble empirical mode decomposition of adaptive noise (CEEMDAN) to predict the stock indices with better accuracy, in which the wavelet threshold method is especially used to denoise high-frequency noises in the sub-signals to exclude noise interference for future data predictions. Firstly, we choose representative datasets collected from the closing prices of S&P500 and CSI 300 stock indices to evaluate the proposed GRU-CEEMDAN–wavelet model. Additionally, we compare the improved model to the traditional ARIMA and several modified neural network models using different gate structures. The result shows that the mean values of MSE and MAE for GRU based on CEEMDAN–wavelet are the smallest by significance analysis. Overall, we found that our model could improve prediction accuracy and alleviates the time lag problem.

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Section: Preliminaries 21 Related Workmentioning

confidence: 99%

GRU Neural Network Based on CEEMDAN–Wavelet for Stock Price Prediction

Ren

2023

Applied Sciences

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“…There are various techniques to find face landmark points. Some techniques [13] use neural network that train a classifier by feeding numerous images with manual hand drawn face landmarks on numerous faces whereas some techniques find face landmarks by finding a face that minimizes the deviation with its mean shape [14].  Figure 2: Sample image illustrated with 68 face landmarks [15]  Perform Delaunay Triangulation on the face landmark points.…”

Section: Developed Step-by-step Implementation Of Delaunay Triangulationmentioning

confidence: 99%

Face Recognition using PCA Integrated with Delaunay Triangulation

Adeshara¹,

Elangovan²

2020

Computer Science &Amp; Information Technology (CS &Amp; IT)

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Face Recognition is most used for biometric user authentication that identifies a user based on his or her facial features. The system is in high demand, as it is used by many businesses and employed in many devices such as smartphones and surveillance cameras. However, one frequent problem that is still observed in this user-verification method is its accuracy rate. Numerous approaches and algorithms have been experimented to improve the stated flaw of the system. This research develops one such algorithm that utilizes a combination of two different approaches. Using the concepts from Linear Algebra and computational geometry, the research examines the integration of Principal Component Analysis with Delaunay Triangulation; the method triangulates a set of face landmark points and obtains eigenfaces of the provided images. It compares the algorithm with traditional PCA and discusses the inclusion of different face landmark points to deliver an effective recognition rate.

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