Face Verification: Strategies for Employing Deep Models

Features extracted with deep learning have now achieved state-of-the-art results in many tasks. However, to reuse a learned deep model, transfer learning with fine-tuning needs to be employed, which requires to re-train the whole model or part of it to extract useful features in the new domain. This step is burdensome and requires heavy computing power. Therefore, this work investigates alternatives in transfer-learning that do not involve performing fine-tuning for a model with the new domain. Namely, we explore the correlation of depth and scale in deep models, and look for the layer/scale that yields the best results for the new domain, we also explore metrics for the verification task, using locally connected convolutions to learn distance metrics. Our experiments use a model pre-trained in face identification and adapt it to the face verification task with different data, but still on the face domain. We achieve 96.65% mean accuracy on the Labeled Faces in the Wild dataset and 93.12% mean accuracy on the Youtube Faces dataset comparable to the state-of-the-art.