A static hand gesture recognition model based on the improved centroid watershed algorithm and a dual-channel CNN

In order to achieve static hand gesture recognization within complex skin-like background regions in an effective and intelligent manner, this study proposed an integrated hand gesture recognition model based on the improved centroid watershed algorithm (ICWA) and a dual-channel convolutional neural network (DCCNN) structure. The effectiveness of this approach stemmed from more accurate segmentation of hand gestures from an original image by using the ICWA. The segmented image and the corresponding Local Binary Patterns (LBP) features extracted from the original image then serve as inputs for two channels of the devised DCCNN respectively for classification. The contributions of this study included an innovative method for reducing the image gradient difference while segmenting in the YCrCb color space, and the fusion of both Principal Component Analysis (PCA) for dimension reduction and a convexity detection process for identifying the secant line between the palm and arm. The devised DCCNN enables significant improvement on the static hand gesture classification accuracy by employing independent dual-convolution neural network framework for dealing with richer features at different scales. Tests and evaluations on benchmarking databases demonstrated that the devised models and techniques outperform classic methods with distinctive advantages when operating under challenging skin-like background conditions.