Cambridge
Hand Gesture Data set
ftp://mi.eng.cam.ac.uk/pub/CamGesData
The
size of the data set is about 1GB. A citation paper for
this data set is
TK.
Kim, SF. Wong and R. Cipolla, Tensor Canonical
Correlation Analysis for Action Classification, In
Proc. of IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), Minneapolis, MN, 2007.

The data set
consists of 900 image sequences of 9 gesture classes,
which are defined by 3 primitive hand shapes and 3
primitive motions (see Figure 1). Therefore, the target
task for this data set is to
classify different shapes as well as
different motions at a time.
Figure
1. HandGesture Database. 9 different gesture
classes are generated by 3 different primitive shapes and
motions.

Each class
contains 100 image sequences (5 different illuminations x
10 arbitrary motions x 2
subjects). Each sequence was recorded in front of a fixed
camera having roughly isolated gestures
in space and time. Thus, fairly large intraclass
variations in spatial and temporal alignment
is reflected to the data set. See Figure 2 for typical
sample sequences of the 9 classes
and Figure 3 for 5 different illumination prototypes.
Figure
2. Sample sequences of the 9 gesture classes.
Figure
3. 5 different illumination conditions in the
database.
