Static Hand Gesture Recognition and its Application based on Support Vector Machines

Abstract

Hand gesture recognition plays an important role in applications such as human computer interface, remote controlling, and virtual environments(VEs).An novel hand gesture recognition algorithm is proposed to identify whether the hand can meet the requirements of driving license test or not. The algorithm is mainly based on Hu moments and Support Vector Machines (SVMs). Firstly, Hu invariant moments are extracted into a vector of 7 dimensions. Then, a Support VectorMachines is used to find a decision border between the integrated hand and the defected hand. At testing time, a decision is taken using the previously learned SVMs. This algorithm brings a 3.5% error rate of identifying the hand.

Fingertip-based Real Time Tracking and Gesture Recognition for Natural User Interfaces

“…A commonly used method to detect fingers and fingertips is the mathematical morphology and related solutions [18][19][20]. Some approaches use Hu invariant moments [21][22], others k-curvature algorithm [23], combined with template matching [11], [19]. Noise, scale, and hand direction have a significant impact on finding fingers and fingertips using skeleton [24] or dominant points [12] principles.…”

Max-pooling convolutional neural networks for vision-based hand gesture recognition

“…Since SVMs [29] have gained much attention in recent times due to their powerful generalization capabilities as gesture classifiers [16], [18] we evaluate different feature learning schemes using SVMs. The following approaches are evaluated in this paper using our dataset: (i) The authors in [30], [31], [32] use Hu Invariant Moments for feature learning from images of different objects and gestures; (ii) Unsupervised feature learning is applied by authors in [33] using the Spatial Pyramid (generally referred to as Bag of Features or Bag of Words (BoW)) a combination of SIFT and k-means; (iii) Shape properties of objects such as roundness, form factor, compactness, eccentricity, perimeter, solidity etc are used by the authors in [31], [34]; (iv) Skeletonization has been proposed by the authors in [35], [36] for gesture recognition tasks, such as the counting the number of fingers; (v) Pyramid of Histogram Oriented Gradients (PHOG) [37], a variant of the famous HOG descriptor [38], gained popularity for its vectorized HOG feature learning approach; (vi) The Fast Fourier Transform (FFT) has been used by the authors in [39] to represent the shape of the hand contour in images using the spatial domain; (vii) CNNs called Tiled CNNs [40] are supervised feature learners and classifiers able to learn complex invariances such as scale and rotational invariance. The errors obtained on the test sets using these schemes are tabulated in Table 1.…”

Comparative evaluation of static gesture recognition techniques based on nearest neighbor, neural networks and support vector machines

“…After the image acquisition, image segmentation, contour selection, and classification, their work achieved a mean recognition rate of 92.89%. Liu et al [19] used an SVM with Hu moments to classify hand postures acquired from a camera, automating the verification of hand integrality for the Chinese Driver Physical Examination System. An error rate of 3.5% was generated after tests were executed with data from 20 people.…”