In today's technological era, gesture recognition is a trending topic. The primary goal is to identify human gestures using machine learning and mathematical algorithms for human-computer interaction. There are just a few forms of Human-Computer Interaction, such as keyboard, mouse, and touch screens. When it comes to adapting more adaptable technology to computers, each of these gadgets has its own constraints.
In this article, we will be discussing hand gesture recognition in detail. The article will show you:
- Why Hand Gesture Recognition is Important
- Implementing Hand Gesture Recognition
- Machine Learning Algorithms for Hand Gesture Recognition
- Support Vector Machine (SVM)
- K Nearest Neighbors (KNN)
- Decision Trees (DT)
- Convolutional Neural Networks(CNN)
Why is Hand Gesture Recognition Important?
Gesture recognition is one of the most important strategies for creating user-friendly interfaces. Gestures can arise from any physical move or condition, but most typically from the face or hand. Users may interact with gadgets via gesture recognition without physically touching them. The use of different technologies and machine learning algorithms may be used to recognize and comprehend distinct human gestures, making human-machine communication more successful. This might render conventional input devices like touchscreens, mouse pads, and keyboards obsolete.
Implementing Hand Gesture Recognition
Computer vision and machine learning algorithms analyze the photos in real-time, converting hand gestures into instructions based on a predefined library of signals. Gesture recognition software commands become simply another sort of input, akin to turning a dial, tapping a button, or touching a screen.
Initially, the process of hand gesture recognition includes two components. One is the collection of the training dataset, which is made up of various sorts of hand gestures that are used to train the system and then utilized to perform the activities. Another is feature extraction, the process of locating the centroid that splits a picture into two halves at its geometric center.
After collection and preprocessing of data is done, captured photos from a web camera or any other input device are sent into the model or system. Images recorded by the camera are compared to the dataset to identify the valid hand movements necessary to accomplish the required tasks. The input picture from the camera is required for hand detection. After preprocessing, the video input is stored frame by frame in a matrix.
Machine learning algorithms then enter into the process, which is covered in more detail in the section below.
Machine Learning Algorithms for Hand Gesture Recognition
There are several Machine Learning algorithms to recognize hand gestures, down below is the list of algorithms and the methodologies followed using these algorithms.
- Support Vector Machine (SVM)
SVM selects the extreme vectors and points that aid in the creation of the hyperplane. Support vectors, which are used to represent these extreme instances, form the basis for the SVM method. The SVM algorithm's objective is to establish the optimum decision boundary or line that can divide n-dimensional space into classes so that we may quickly classify fresh data points in the future.
For hand gesture recognition through SVM, first hand motion is acquired using a web camera, during processing. A bounding box is built to identify the hand during the pre-processing step in order to separate the foreground from the background. This is accomplished by using differences between successive frames. To facilitate quicker calculation, the captured picture is resized. The leading edge of the resized picture is used utilizing Canny's edge detection. Then the characteristic of the image is extracted from a gradient-based histogram (HOG). The characteristics that were extracted are kept in a database. SVM compares the most relevant image and displays the output on the screen if the database contains a matching feature.
- K Nearest Neighbors (KNN)
This algorithm's fundamental principle is to choose the k-nearest neighbors of a particular input from a training database, and then assign those neighbors to the output that received the most votes among those connected to the inputs that were chosen. It can be helpful to weight neighbor contributions in hand gesture detection and classification such that closer neighbors contribute more to the average than farther neighbors.
To extract the characteristics and locate the hand area, KNN requires entering parameters. In order to achieve this, the KNN system adapts four parameters: the ratio of the vertical and horizontal lines, the rectangle situation that contains the median point and covers the hand shape, and a parameter providing details about the horizontal distribution surrounding the rows' mean within the entire hand area, and the standard deviation.
The above-mentioned KNN model operates when a single-handed person appears successfully in the camera's range of view, the system properly detects them and then identifies the gesture.
- Decision Trees (DT)
Using a decision tree for a hand gesture offers several benefits, including scalability, rapid training and testing, and interpretability. Starting at the tree's root and working through it until reaching a leaf, a decision tree may be used to categorize a case. The case outcome for the test at each non-leaf decision node is established, and the focus then turns to the root of the subtree corresponding to this outcome. The class of the case is anticipated to be that which is recorded at the leaf when this method ultimately results in a leaf.
For hand gesture recognition each instance that has been tagged would be segmented using overlapping sliding window segmentation. An individual candidate shape let will be created for each type of input data in a segment. The decision tree method then calculates the separation between each potential shape let and each occurrence in the training dataset. The least distance between a candidate and the instance would be used to measure how similar they are. A Distance Matrix is then used to hold the distance values. The unlabeled data instance is classified using the built-in shape let-based decision tree.
Running through the leaves of the decision tree classification model, it is stopped once a class is satisfied. The procedure is the same as when building a distance matrix; the instance to be examined is first segmented to determine its ideal location match. In accordance with the location of the leaves in the tree model, the distance of each segment would be measured with the relevant shape let. The least distance would be used to determine what kind of hand gesture was recorded by the camera or any other input device.
- Neural Networks
CNN's are a subset of deep learning neural networks that are employed in the analysis of pictures and video. It is made up of an input layer, hidden layers, and an output layer. Backpropagation is used for increased accuracy and productivity. It trains and verifies the computer's recognition of human gestures. Page turning, and zooming in and out are examples of interactions.
For hand gesture recognition the number of fingers present in the hand gesture is calculated by CNN, using fault spots in the gesture. The acquired gesture is passed via a 3-Dimensional Convolutional Neural Network. CNN is used in succession to recognize the current gesture. The identified gesture is utilized as input to accomplish the user's desired actions.
Gesture recognition has several advantages, including increased safety and the convenience of using deliberate gestures to control any device functions rather than a potentially confusing menu system.
Gesture recognition is a promising technology, but it only represents a small portion of the huge capacity that ML algorithms have in regard to voice commands, eye movements, and body movements. In the coming time as technological advancement happens, we might see more exposure in the field of gesture recognition.