Learning Explainable Facial Features from Noisy Unconstrained Visual Data
Hand, Emily Morgan
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Attributes are semantic features of objects, people, and activities. They allow computers to describe people and things in the way humans would, which makes them very useful for recognition. Facial attributes - gender, hair color, makeup, eye color, etc. - are useful for a variety of different tasks, including face verification and recognition, user interface applications, and surveillance, to name a few. The problem of predicting facial attributes is still relatively new in computer vision. Because facial attribute recognition is not a long-studied problem, a lack of publicly available data is a major challenge. As with many problems in computer vision, a large portion of facial attribute research is dedicated to improving performance on benchmark datasets. However, it has been shown that research progress on a benchmark dataset does not necessarily translate to a genuine solution for the problem. This dissertation focuses on learning models for facial attributes that are robust to changes in data, i.e. the models perform well on unseen data. We do this by taking cues from human recognition, and translating these ideas into deep learning techniques for robust facial attribute recognition. Towards this goal, we introduce several techniques for learning from noisy unconstrained visual data: utilizing relationships among attributes, a selective learning approach for multi-label balancing, a temporal coherence constraint and a motion-attention mechanism for recognizing attributes in video, and parsing faces according to attributes for improved localization. We know that facial attributes are related, e.g. heavy makeup and wearing lipstick or male and goatee. Humans are capable of recognizing and taking advantage of these relationships. For example, if a face of a subject is occluded, and facial hair can be seen, then the likelihood that the subject being male should increase. We introduce several methods for implicitly and explicitly utilizing attribute relationships for improved prediction. Some attributes are more common than others in the real world, e.g. male v. bald. These disparities are even more pronounced in datasets consisting of posed celebrities on the red carpet (i.e. there are very few celebrities not wearing makeup). These imbalances can cause a facial attribute model to learn the bias in the dataset, rather than a true representation for the attribute. To alleviate this problem, we introduce selective learning, a method of balancing each batch in a deep learning algorithm according to each attribute given a target distribution. Selective learning allows a deep learning algorithm to learn from a balanced set of data at each iteration during training, removing the bias from the label imbalance. Learning a facial attribute model from image data, and testing on video data gives unexpected results (e.g. gender changing between frames). When working with video, it is important to account for the temporal and motion aspects of the data. In order to stabilize attribute predictions in video, we utilized weakly-labeled data and introduced time and motion constraints in the model learning process. Introducing temporal coherence and motion-attention constraints during learning of an attribute model allows the use of weakly-labeled data, which is essential when working with video. Framing the problem of facial attribute recognition as one of semantic segmentation, where the goal is to predict attributes at each pixel, we are able to reduce the effect of unwanted relationships between attributes (e.g. high cheekbones and smiling ). Robust facial attribute recognition algorithms are necessary for improving the applications which use these attributes. Given limited data for training, we develop several methods for learning explainable facial features from noisy unconstrained visual data, introducing several new datasets labeled with facial attributes and improving over the state-of-the-art.