Gesture Recognition Implementation Using MATLAB

Gesture recognition represents a significant application in computer vision, and MATLAB-based implementations enable rapid algorithm validation. The Histogram of Oriented Gradients (HOG) serves as a widely-used feature extraction method that effectively captures directional information of gesture edges. This article presents a gesture recognition approach utilizing HOG features with Euclidean distance matching.

Feature extraction constitutes the core of gesture recognition. HOG features generate robust shape descriptors by computing gradient orientation distributions in local image regions. In MATLAB implementation, the process begins by converting input images to grayscale using rgb2gray() function, followed by calculating gradient magnitudes and directions for each pixel through Sobel or Prewitt operators. The orientation information is then compiled into histograms within localized cells using histcounts(), with all regional histograms concatenated to form the final feature vector via hog = extractHOGFeatures(I) function.

The matching process involves pre-storing HOG features of standard gestures as templates. When new gesture images are input, the system computes Euclidean distances between their feature vectors and all template features using pdist2(featureVector, templateFeatures, 'euclidean'). Smaller Euclidean distances indicate higher similarity between features, with the system selecting the template category yielding the minimum distance as the recognition result through [minDist, recognizedClass] = min(allDistances).

This methodology demonstrates stability under varying illumination conditions and minor rotations since HOG features maintain certain invariance to such transformations. Recognition efficiency depends on feature dimensionality, which can be optimized by adjusting HOG parameters like cell size and number of orientation bins through hog = extractHOGFeatures(I, 'CellSize', [8 8], 'NumBins', 9) to balance accuracy and computational speed.