Using Guided Autoencoders on Face Recognition

In this master thesis we will create guided autoencoders (GAE) and apply them to face recognition. GAEs are agents that interact with images, using a novel combination of autoencoders and reinforcement learning. They perceive part of an image through a window, use an autoencoder to encode it, and react to what they see by moving the window. GAEs are trained to find and encode specific parts of the face – in our case the eyes, nose and mouth. We use the LFWC (cropped Labeled Faces in the Wild) dataset which is very varied and has many uncontrolled variables. We train GAEs using the CACLA reinforcement learning algorithm which can deal with continuous states and actions. To create a state, GAEs evaluate their separately trained autoencoder on what is visible through their window. The resulting state guides their actions. We show that GAEs are able to navigate the complex landscapes of the face images using only local information, which is quite remarkable. The experiments show that GAEs can find their goals if they are initialized relatively close to their goal. If we add position information to the encodings, the performance increases greatly. We also compare deep stacked autoencoders and shallow autoencoders. Surprisingly, deep GAEs do not outperform shallow GAEs on this task. The GAEs are finally used to classify the gender of faces and whether a person is smiling or not. They are able to do classification, but do not rival state-of-the-art systems. Their flexibility however allows them to be extended easily to improve performance. In summary, the GAEs are currently not able to perform better on classification than the state of the art. However, their ability to navigate complex images and their flexibility makes them promising tools for face recognition and computer vision.