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
(this https URL).

本研究提出了一个大型数据集，其中包含详细的基准信息，用于人类手图像（背侧和掌侧）的性别识别和生物识别。利用卷积神经网络来训练，设计了一个两流的 CNN 来解决性别识别问题。该模型可以被用作特征提取器，为生物识别任务提供支持向量机分类器的输入。通过实验结果，发现正常像机拍摄的背侧图像与掌侧图像一样有效。