Physical measurement have been becoming increasingly helpful in monitoring the humans health status. Manual measurement of physical status is time consuming and may result in misdiagnosing, so an automatic method for ...
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ISBN:
(纸本)9781509016129
Physical measurement have been becoming increasingly helpful in monitoring the humans health status. Manual measurement of physical status is time consuming and may result in misdiagnosing, so an automatic method for identification the status of physical is urgently needed. This paper presents a novel feature extraction method based on using constrained high dispersal network for depth images and coped with Support Vector Machines (SVM) to measure human physical function. The proposed method can catch the most representative features of depth images belonging to different actions and statuses. We analyze the representation efficiency of hand-crafted features (HOG features, and LBP features), deep learning features (CNN features, and PCANet features) and our proposed deep learning features separately in order to validate the efficiency and accuracy of our proposed method. The results show superior performance of 85.19% on 3840 samples (three actions, each with four different statuses, and every status contains sixteen sequences) when the proposed deep features combined with SVM.
Facial expression recognition plays an important role in lie detection, and computer-aided diagnosis. Many deep learning facial expression feature extraction methods have a great improvement in recognition accuracy an...
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ISBN:
(纸本)9781509016129
Facial expression recognition plays an important role in lie detection, and computer-aided diagnosis. Many deep learning facial expression feature extraction methods have a great improvement in recognition accuracy and robutness than traditional feature extraction methods. However, most of current deep learning methods need special parameter tuning and ad hoc fine-tuning tricks. This paper proposes a novel feature extraction model called Locally Linear Embedding Network (LLENet) for facial expression recognition. The proposed LLENet first reconstructs image sets for the cropped images. Unlike previous deep convolutional neural networks that initialized convolutional kernels randomly, we learn multi-stage kernels from reconstructed image sets directly in a supervised way. Also, we create an improved LLE to select kernels, from which we can obtain the most representative feature maps. Furthermore, to better measure the contribution of these kernels, a new distance based on kernel Euclidean is proposed. After the procedure of multi-scale feature analysis, feature representations are finally sent into a linear classifier. Experimental results on facial expression datasets (CK+) show that the proposed model can capture most representative features and thus improves previous results.
With the improvement of people's living standards, there is no doubt that people are paying more and more attention to their health. However, shortage of medical resources is a critical global problem. As a result...
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ISBN:
(纸本)9781509016129
With the improvement of people's living standards, there is no doubt that people are paying more and more attention to their health. However, shortage of medical resources is a critical global problem. As a result, an intelligent prognostics system has a great potential to play important roles in computer aided diagnosis. Numerous papers reported that tongue features have been closely related to a human's state. Among them, the majority of the existing tongue image analyses and classification methods are based on the low-level features, which may not provide a holistic view of the tongue. Inspired by a deep convolutional neural network (CNN), we propose a deep tongue image feature analysis system to extract unbiased features and reduce human labor for tongue diagnosis. With the unbalanced sample distribution, it is hard to form a balanced classification model based on feature representations obtained by existing low-level and high-level methods. Our proposed deep tongue image feature analysis model learns high-level features and provide more classification information during training time, which may result in higher accuracy when predicting testing samples. We tested the proposed system on a set of 267 gastritis patients, and a control group of 48 healthy volunteers (labeled according to Western medical practices). Test results show that the proposed deep tongue image feature analysis model can classify a given tongue image into healthy and diseased state with an average accuracy of 91.49%, which demonstrates the relationship between human body's state and its deep tongue image features.
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