  Although many studies suggest high performance hand detection methods, those
methods are likely to be overfitting. Fortunately, the Convolution Neural
Network (CNN) based approach provides a better way that is less sensitive to
translation and hand poses. However the CNN approach is complex and can
increase computational time, which at the end reduce its effectiveness on a
system where the speed is essential.In this study we propose a shallow CNN
network which is fast, and insensitive to translation and hand poses. It is
tested on two different domains of hand datasets, and performs in relatively
comparable performance and faster than the other state-of-the-art hand
CNN-based hand detection method. Our evaluation shows that the proposed shallow
CNN network performs at 93.9% accuracy and reaches much faster speed than its
competitors.
