Amir Akbarzadeh

A robust elastic and partial matching metric for face recognition

We present a robust elastic and partial matching metric for face recognition. To handle challenges such as pose, facial expression and partial occlusion, we enable both elastic and partial matching by computing a part based face representation. In which N local image descriptors are extracted from densely sampled overlapping image patches. We then define a distance metric where each descriptor in one face is matched against its spatial neighborhood in the other face and the minimal distance is recorded. For implicit partial matching, the list of all minimal distances are sorted in ascending order and the distance at the αN-th position is picked up as the final distance. The parameter 0 ≤ α ≤ 1 controls how much occlusion, facial expression changes, or pixel degradations we would allow. The optimal parameter values of this new distance metric are extensively studied and identified with real-life photo collections. We also reveal that filtering the face image by a simple difference of Gaussian brings significant robustness to lighting variations and beats the more utilized self-quotient image. Extensive evaluations on face recognition benchmarks show that our method is leading or is competitive in performance when compared to state-of-the-art.

Which faces to tag: Adding prior constraints into active learning

We introduce an algorithm that guides the user to tag faces in the best possible order during a face recognition assisted tagging scenario. In particular, we extend the active learning paradigm to take advantage of constraints known a priori. For example, in the context of personal photo collections, if two faces come from the same source photograph, we know that they must be of different people. Similarly, in the context of video, we know that the faces from a single track must be of the same person. Given a set of unlabeled images and constraints, we use a probabilistic discriminative model that models the posterior distributions by propagating label information using a message passing scheme. The uncertainty estimate provided by the model naturally allows for active learning paradigms where the user is consulted after each iteration to tag additional faces. Our experiments show that performing active learning while incorporating a priori constraints provides a significant boost in many real-world face recognition tasks.