Hand Posture Recognition Using a Three-Dimensional Light Field Camera

This paper used a light field camera to capture two types of 3D hand posture depth images, contour hand posture and solid hand posture, without a complicated setup and superfluous preprocess. The images were captured under two recognition conditions: in-plane and out-of-plane rotations. The posture recognition features two methods: the first method is the principal component analysis (PCA), which is used to obtain the required feature vectors, associated with the k-nearest neighbor (k-NN) algorithm as a classifier; the second method involves using 2D optimal-PCA (2DOPCA) combined with a genetic algorithm (GA) for feature selection, and the Mahalanobis distance is then used for classification. The variations in the test images include in-plane rotation, out-of-plane rotation, and Gaussian noise added to simulate the lighting interference in a real situation. The results showed that the PCA combined with the k-NN yields high recognition rates for grayscale contour images with in-plane and out-of-plane rotations and color solid posture images with in-plane rotation. For color solid posture images with out-of-plane rotation, the projection color space was combined with the PCA and k-NN methods to obtain high recognition rates. Moreover, the contour and color solid posture images with noise more than 5% require the 2DOPCA combined with the GA to obtain a satisfactory result and maintain recognition rate stability.