2D Hand Detection Using Multi-Feature Skin Model Supervised Cascaded CNN

Abstract

Hand gesture recognition is one of the most popular Human Computer Interface. The first step in most vision-based gesture recognition system is the hand detection and segmentation. Since hands are involved in a variety of daily tasks, the detection work suffers from both extreme illumination changes and the intrinsic variability of hand appearance. To overcome these problems, we propose a new method for 2D hand detection which can be considered as a combination of Multi-Feature based hand proposal generation and cascaded convolutional neural network (CCNN) classification. Considered various luminance, we choose color, Gabor, HOG and SIFT feature to discriminate skin region and generate hand proposal. Also, we propose a cascaded CNN that keeps the deep context information to detect hand among the proposals. The proposed Multi-Feature Supervised Cascaded CNN (MFS-CCNN) method is tested on a combination of several datasets including Oxford Hands Dataset, VIVA hand detection and Egohands Dataset as positive sample and ImageNet 2012, FDDB dataset as negative sample. The proposed method achieves competitive results.