Hand posture recognition using jointly optical flow and dimensionality reduction

Abstract

Hand posture recognition is generally addressed by using either YC b C r (luminance and chrominance components) or HSV (hue, saturation, value) mappings which assume that a hand can be distinguished from the background from some colorfulness and luminance properties. This can hardly be used when a dark hand, or a hand of any color, is under study. In addition, existing recognition processes rely on descriptors or geometric shapes which can be reliable; this comes at the expense of an increased computational complexity. To cope with these drawbacks, this paper proposes a four-step method recognition technique consisting of (i) a pyramidal optical flow for the detection of large movements and hence determine the region of interest containing the expected hand, (ii) a preprocessing step to compute the hand contour while ensuring geometric and illumination invariance, (iii) an image scanning method providing a signature which characterizes non-star-shaped contours with a one-pixel precision, and (iv) a posture classification method where a sphericity criterion preselects a set of candidate postures, principal component analysis reduces the dimensionality of the data, and Mahalanobis distance is used as a criterion to identify the hand posture in any test image. The proposed technique has been assessed in terms of its performances including the computational complexity using both visual and statistical results.

Static Hand Gesture Recognition Using Freeman Chain Code and Neural Network

“…Hand gesture recognition is generally addressed by using either YCbCr (lu minance and chrominance components) or HSV (hue, saturation, value) mappings which assume that a hand can be distinguished fro m the background fro m some colorfu lness and luminance properties. In addition, cu rrent recognition methodsare dependent on descriptors or geometric shapes which can be reliable; however co mes at increased computational co mplexity [1]. The proposed HGR process can be depicted as from figure 2.…”

Joint Design of the Hardware and the Software of a Radar System with the Mixed Grey Wolf Optimizer: Application to Security Check

“…Then some features are computed out of the binarized radar images. We have checked the interest for our application of Histograms of Oriented Gradients (described by Yan et al [30] and Dalal et al [31]), shape descriptors based on Fourier transform (proposed by Slamani et al [32]), and a matrix signature dedicated to non star-shaped contours [33] proposed by Bougnim and some of the authors of this paper. For the purpose of classification we have selected support vector machine.…”

“…Starting from the binary image, we include several types of features which can be computed out of the segmented images: Histograms of Oriented Gradients (refer to the papers from Yan et al [30], and Dalal et al [31] (HOG), shape descriptors (sd) based on Fourier transform (refer to the paper from Slamani et al [32]) -forming a vector of 10 components-, a scalar sphericity criterion created by Boughnim and two of the authors of the current paper [33], and a matrix signature dedicated to non star-shaped contours [33] (proposed by Bougnim et al and denoted by Z in the following). We also included as a possibility the combination of sd, Z, and sphericity.…”