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Deep-learning-based facial classifier applying 3D minimum spanning tree

The study designs a deep-learning-based facial classifier by applying the minimum spanning tree algorithm. A new data format is developed by adding the 3D information into 2D color images. This new type of data can magnificently raise the classifier's accuracy. This data can also recognize the target even in the bad situation. Firstly, the 3D features are extracted from Kinect sensor. After that, some of the extracted features are chosen to compose the minimum spanning tree (MST). The data represented by the minimum spanning tree is then put into the color image to form the new data. The new data will be trained by a deep-learning neural network to create the facial classifier. Comparing with the methods of image-only data with support vector machine (SVM), MST-image data with SVM and image-only data with deep-learning neural network (DLNN), the proposed method of MST-image data with DLNN has the highest identifying accuracy.