1. A method of hand position detection, comprising: extracting, via an image processing module of an electronics device, a feature set associated with hand gesture detection and hand pose inference from a plurality of input images by: tracking a region of interest (ROI) between subsequent video frames of the plurality of input images as a flock of features; triggering scale invariant feature transforms (SIFT) feature extraction; calculating an optical flow path of the flock of features; measuring brightness gradients in multiple directions across the plurality of input images; generating image pyramids from the measured brightness gradients; extracting pixel intensity and displacement features and the SIFT features using the generated image pyramids; and applying a cascade filter in association with extracting the pixel intensity/displacement features and the SIFT features from the generated image pyramids; inferring a hand pose type using a trained multiclass support vector machine (SVM) by: detecting at least one feature within a training image and the plurality of input images; and performing a one-to-one mapping of instances of the at least one feature within the plurality of input images with at least one label drawn from a finite set of elements, where the at least one label comprises at least one label generated during a training phase based upon a motion capture three dimensional (3D) data set; and approximating the hand pose using inverse kinematics (IK) optimization by: partitioning the plurality of input images into a plurality of processing regions; determining a centroid of features within each of the plurality of processing regions; mapping a location of each feature centroid onto three dimensional (3D) pose data associated with a motion capture data set; comparing variances from each feature centroid to a closest match within the 3D pose data; determining which of a plurality of joint constraints affect the IK optimization; mapping each feature centroid to a closest joint stored within the 3D pose data; minimizing a distance of each mapped closest joint within the training image based upon the 3D pose data; and determining a final hand position based upon the minimized distance of each mapped closest joint within the training image.