11K Hands: Gender recognition and biometric identification using a large dataset of hand images
This work addresses biometric identification and gender recognition using hand images, but it is incremental as it applies existing methods to a new dataset.
The authors tackled gender recognition and biometric identification by creating a large dataset of hand images and training a two-stream CNN, achieving effective results with the dorsal side performing similarly to or better than the palmar side.
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).