A Multi Resolution Transform for Thermal Face Recognition Using Random Forest Classifier

Murugesan, Chengathir Selvi; Muneeswaran, K.; Prathiba, T.

doi:10.1166/jctn.2018.7160

Cited by 3 publications

(1 citation statement)

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“…e aggregated features are then entered into the classi er. [32], K nearest neighbor(KNN) [33], Bayesian classifier(NB) [34], decision tree(DT) [35], logistic regression(LR) [36], and random forest algorithm(RF) [37]. Ensemble learning improves the effectiveness of machine learning by combining several models.…”

Section: Multilevel and Multiscale Featurementioning

confidence: 99%

Multilevel and Multiscale Feature Aggregation in Deep Networks for Facial Constitution Classification

Huan

Wen

2019

Computational and Mathematical Methods in Medicine

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Constitution classification is the basis and core content of TCM constitution research. In order to improve the accuracy of constitution classification, this paper proposes a multilevel and multiscale features aggregation method within the convolutional neural network, which consists of four steps. First, it uses the pretrained VGG16 as the basic network and then refines the network structure through supervised feature learning so as to capture local image features. Second, it extracts the image features of different layers from the fine-tuned VGG16 model, which are then dimensionally reduced by principal component analysis (PCA). Third, it uses another pretrained NASNetMobile network for supervised feature learning, where the previous layer features of the global average pooling layer are outputted. Similarly, these features are dimensionally reduced by PCA and then are fused with the features of different layers in VGG16 after the PCA. Finally, all features are aggregated with the fully connected layers of the fine-tuned VGG16, and then the constitution classification is performed. The conducted experiments show that using the multilevel and multiscale feature aggregation is very effective in the constitution classification, and the accuracy on the test dataset reaches 69.61%.

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