Human hand detection using robust local descriptors

To date, human hand detection in images remains a challenging task due to the variable lighting conditions, hand appearances and background noise. In this paper, we present an effective strategy based on feature fusion for detecting hands with cluttered surroundings. To form the fusions, we propose three novel noise invariant features, namely: 1) NCHOG (Noise Compensated Histogram of Oriented Gradients), 2) NCLBP (Noise Compensated Local Binary Patterns), and 3) HPCP (Histograms of Pairs of Circumference Pixels). We show the superior performance of the NCHOG and the NCLBP descriptors over their existing traditional counterparts, i.e., HOG and LBP. Merging our novel features with existing features in different permutations, and applying Partial Least Squares (PLS) based feature weighting, yields excellent detection results on our own dataset of hand images with variegated and complex backgrounds.