It is often desirable to be able to recognize when inputs to a recognition function learned in a supervised manner correspond to classes unseen at training time. With this ability, new class labels could be assigned to these inputs by a human operator, allowing them to be incorporated into the recognition function-ideally under an efficient incremental update mechanism. While good algorithms that assume inputs from a fixed set of classes exist, e.g. , artificial neural networks and kernel machines, it is not immediately obvious how to extend them to perform incremental learning in the presence of unknown query classes. Existing algorithms take little to no distributional information into account when learning recognition functions and lack a strong theoretical foundation. We address this gap by formulating a novel, theoretically sound classifier-the Extreme Value Machine (EVM). The EVM has a well-grounded interpretation derived from statistical Extreme Value Theory (EVT), and is the first classifier to be able to perform nonlinear kernel-free variable bandwidth incremental learning. Compared to other classifiers in the same deep network derived feature space, the EVM is accurate and efficient on an established benchmark partition of the ImageNet dataset.

Real-world tasks in computer vision often touch upon open set recognition: multi-class recognition with incomplete knowledge of the world and many unknown inputs. Recent work on this problem has proposed a model incorporating an open space risk term to account for the space beyond the reasonable support of known classes. This paper extends the general idea of open space risk limiting classification to accommodate non-linear classifiers in a multiclass setting. We introduce a new open set recognition model called compact abating probability (CAP), where the probability of class membership decreases in value (abates) as points move from known data toward open space. We show that CAP models improve open set recognition for multiple algorithms. Leveraging the CAP formulation, we go on to describe the novel Weibull-calibrated SVM (W-SVM) algorithm, which combines the useful properties of statistical extreme value theory for score calibration with one-class and binary support vector machines. Our experiments show that the W-SVM is significantly better for open set object detection and OCR problems when compared to the state-of-the-art for the same tasks.

An pipeline for reconstructing the 3D face model from an uncontrolled video sequence is presented which involves three major steps. Firstly, a generic deformable 3D face model is built from the 3D scans of one hundred individuals. Secondly, the 3D face shape from a video sequence is constructed by estimating poses of images using structure-from-motion technique and dense correspondences between those images by employing Huber-L1optical flow algorithm. Finally, the generated generic deformable 3D face model can be fitted to the reconstructed 3D face-shape from a video sequence provided that the deviation from the real 3D face is less than certain thresholds. The application is developed to reconstruct the 3D face-shape in nearly uncontrolled environment so the results cannot be expected to be very accurate. We discuss the steps taken to perform the first and second steps. The factors affecting the depth estimation in face region cause major accuracy problems. They are analyzed and possible improvements to enhance the 3D face-shape reconstruction are presented.