Facial keypoints detection using Neural Network
Nowadays, facial keypoints detection has become a very popular topic and its applications include Snapchat, How old are you, have attracted a large number of users. The objective of facial keypoints detection is to find the facial keypoints in a given face, which is very challenging due to very different facial features from person to person. The idea of deep learning has been applied to this problem, such as neural network and cascaded neural network. And the results of these structures are significantly better than stateof the art methods, like feature extraction and dimension reduction algorithms. In our project, we would like to locate the keypoints in a given image using deep architectures to not only obtain lower loss for the detection task but also accelerate the training and testing process for real world applications. We have constructed two basic neural network structures, one hidden layer neural network and convolutional neural network as our baselines. And we have proposed an approach to better locate the coordinates of facial keypoints with introduced features other than raw input. Specifically, we use a block of pretrained Inception Model to extract intermediate features and using different deep structures to compute the final output vector. The experiments results have shown the effectiveness of deep structures for facial keypoints detection tasks, and using the pretrained Inception Model has slightly improved the performance of detection compared to baseline methods.