This package contains: 1. SUFR-W, a dataset of “in the wild” natural images of faces gathered from the internet. The protocol used to create the dataset is described in Leibo, Liao and Poggio (2014) - https://cbmm.mit.edu/publications/conference-abstracts/subtasks-unconstrained-face-recognition 2. The full set of SUFR synthetic datasets, called the “Subtasks of Unconstrained Face Recognition Challenge” in Leibo, Liao and Poggio (2014) - https://cbmm.mit.edu/publications/conference-abstracts/subtasks-unconstrained-face-recognition
Pattern recognition has gained significant attention due to the rapid explosion of internet- and mobile-based applications. Among the various pattern recognition applications, face recognition is always being the center of attraction. With so much of unlabeled face images being captured and made available on internet (particularly on social media), conventional supervised means of classifying face images become challenging. This clearly warrants for semi-supervised classification and subspace projection. Another important concern in face recognition system is the proper and stringent evaluation of its capability. This book is edited keeping all these factors in mind. This book is composed of five chapters covering introduction, overview, semi-supervised classification, subspace projection, and evaluation techniques.
