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| -rw-r--r-- | experimental.tex | 12 | ||||
| -rw-r--r-- | uniqueness.tex | 27 |
2 files changed, 20 insertions, 19 deletions
diff --git a/experimental.tex b/experimental.tex index 57189ab..b4d781c 100644 --- a/experimental.tex +++ b/experimental.tex @@ -41,11 +41,13 @@ operates in real-time without calibration. We collect data using the Kinect SDK over a period of a week in a research laboratory setting. The Kinect is placed at the tee of a frequently used -hallway. The view of the Kinect is seen in \fref{fig:hallway}, showing the -color image, the depth image, and the fitted skeleton of a person in a single -frame. Skeletons are fit from roughly 1-5 meters away from the Kinect. For each -frame where a person is detected and a skeleton is fit we capture the 3-D -coordinates of 20 body joints, and the color image. +hallway. For each frame, the Kinect SDK performs figure detection to identify +regions of interest. Then, it fits a skeleton to the identified figures and +outputs a set of joints in real world coordinates. The view of the Kinect is +seen in \fref{fig:hallway}, showing the color image, the depth image with +figures, and the fitted skeleton of a person in a single frame. Skeletons are +fit from roughly 1-5 meters away from the Kinect. For each frame with a +skelton we record color image and the positions of the joints. \begin{figure}[t] \begin{center} diff --git a/uniqueness.tex b/uniqueness.tex index ce1cd3d..8f0b369 100644 --- a/uniqueness.tex +++ b/uniqueness.tex @@ -14,20 +14,19 @@ problem}. In this problem you are given two measurements of the metric and you want to decide whether they come from the same individual (matched pair) or from two different individuals (unmatched pair). -This benchmark is standard for face recognition using the -\emph{Labeled Faces in the Wild} \cite{lfw} database. Raw data of -this benchmark is publicly available and has been derived as follows: -the database is split into 10 subsets. From each of these subsets, 300 -matched pairs and 300 unmatched pairs are randomly chosen. Each -algorithm runs 10 separate leave-one-out cross-validation experiments -on these sets of pairs. The average of the false-positive rates and -the true-positive rates across the 10 experiments for a given -threshold gives one operating point on the receiver operating -characteristic (ROC) curve (Figure~\ref{fig:roc}). Note that in this -benchmark, the identity information of the individuals appearing in the -pairs is not available, which means that the algorithms cannot form -additional image pairs from the input data. This is referred to as the -\emph{image-restricted} setting in the LFW benchmark. +This benchmark is standard for face recognition, which uses the \emph{Labeled +Faces in the Wild} \cite{lfw} database for the pairs. Raw data of this +benchmark is publicly available and has been derived as follows: the database +is split into 10 subsets. From each of these subsets, 300 matched pairs and 300 +unmatched pairs are randomly chosen. Each algorithm runs 10 separate +leave-one-out cross-validation experiments on these sets of pairs. The average +of the false-positive rates and the true-positive rates across the 10 +experiments for a given threshold gives one operating point on the receiver +operating characteristic (ROC) curve (Figure~\ref{fig:roc}). Note that in this +benchmark, the identity information of the individuals appearing in the pairs +is not available, which means that the algorithms cannot form additional image +pairs from the input data. This is referred to as the \emph{image-restricted} +setting in the LFW benchmark. \subsection{Experiment design} |