Human body temperature is an important vital sign especially for health monitoring and exercise training. In this study, we propose a CNN plus support vector machine (SVM) approach (CNN-SVM) to estimate body temperature from a sequence of facial images. The sequence images could be from multiple shots or from video frames using a smartphone camera. First, the facial region is cropped out from a digital picture using a face detection algorithm, which can be implemented on the smartphone or at cloud side. Second, normalize the batch of facial images, and extract the facial features using a pretrained CNN model. Lastly, train a body temperature prediction model with the CNN features using a multiclass SVM classifier. The feature extraction and classification are performed in the cloud side with GPU acceleration and the predicted temperature is then sent back to the mobile app for display. We have a facial sequence database from 144 subjects. There are 12-18 shots of facial images taken from each subject. We selected AlexNet, ResNet-50, VGG-19, or Inception-ResNet-v2 models for feature extraction. The initial results show that the performance of the proposed method is very promising.
Convolutional neural networks (CNNs) become very useful tools in classification and feature extraction applications. In this research, we present a comparable study of several commonly-used CNNs in terms of performance. Most recently developed CNNs are selected in our study, which include NASNet-Large, Inception-Resnet-v2, DenseNet201, NASNet- Mobile, MovileNet-v2 as well as well-known ResNet50 and VGG19 for comparisons. In our classification experiments there are eight different geometrical shapes, each of which includes 486 to 620 computer-generated images. Two basic shapes, triangle and square, vary with solid or hollow shapes, and then overlapping with or without three-disk distractors. CNNs training and testing both can use the shape images as the experiments conducted on the ImageNet. On the other hand, we can use the pretrained CNNs on ImageNet to extract features, then train a multiclass support vector machine (SVM) to do classification. Training images may include four shapes or two categories (solid or hollow), while testing images are four shapes or two categories with distractors. The performance of CNNs includes classification accuracies and time costs in training and testing. The experimental results will provide guidance in selecting CNN models.
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