Abstract

Deformable objects are everywhere. Faces, cars, bicycles, chairs etc. Recently, there has been a wealth of research on training deformable models for object detection, part localization and recognition using annotated data. In order to train deformable models with good generalization ability, a large amount of carefully annotated data is required, which is a highly time consuming and costly task. We propose the fifirst - to the best of our knowledge - method for automatic construction of deformable models using images captured in totally unconstrained conditions, recently referred to as “in-the-wild”. The only requirements of the method are a crude bounding box object detector and apriori knowledge of the object’s shape (e.g. a point distribution model). The object detector can be as simple as the Viola-Jones algorithm (e.g. even the cheapest digital camera features a robust face detector). The 2D shape model can be created by using only a few shape examples with deformations. In our experiments on facial deformable models, we show that the proposed automatically built model not only performs well, but also outperforms discriminative models trained on carefully annotated data. To the best of our knowledge, this is the fifirst time it is shown that an automatically constructed model can perform as well as methods trained directly on annotated data