  The human hand possesses distinctive features which can reveal gender
information. In addition, the hand is considered one of the primary biometric
traits used to identify a person. In this work, we propose a large dataset of
human hand images (dorsal and palmar sides) with detailed ground-truth
information for gender recognition and biometric identification. Using this
dataset, a convolutional neural network (CNN) can be trained effectively for
the gender recognition task. Based on this, we design a two-stream CNN to
tackle the gender recognition problem. This trained model is then used as a
feature extractor to feed a set of support vector machine classifiers for the
biometric identification task. We show that the dorsal side of hand images,
captured by a regular digital camera, convey effective distinctive features
similar to, if not better, those available in the palmar hand images. To
facilitate access to the proposed dataset and replication of our experiments,
the dataset, trained CNN models, and Matlab source code are available at
(https://goo.gl/rQJndd).
