RWTH-PHOENIX-Weather 2014 MS Handshapes dev set
Introduced in Deep Hand: How to Train a CNN on 1 Million Hand Images When Your Data Is Continuous and Weakly Labelled2016
We manually labelled 3359 images from the RWTH-PHOENIX-Weather 2014 Development set.
Some of the 45 encountered pose-independent hand shape classes are depicted in Figure 1. They show the large intra-class variance and the strong similarity between several classes. The hand shapes occur with different frequency in the data. The distribution of counts per class can be verified in Figure 2 showing that the top 14 hand shapes explain 90% of the annotated samples.
For our works on hand shape recognition we follow the hand shape taxonomy by the danish sign language lexicon team (Jette H. Kristoffersen and Thomas Troelsgård, Center for Tegnsprog, Denmark http://www.tegnsprog.dk), which amounts to over 60 different hand shapes, often with very subtle differences such as a flexed versus straight thumb. The employed classes are shown in Table1.
