State-of-the-art methods of attribute detection from faces (like those used in Appliqant) almost always assume the presence of a full, unoccluded face. Hence, their performance degrades for partially visible and occluded faces. This paper discusses the use of a deep convolutional neural network-based method that is explicitly designed to perform attribute detection in partially occluded faces. Taking several facial segments and the full face as input, the proposed method takes a data driven approach to determine which attributes are localized in which facial segments.
