Static Hand Gesture Recognition System Research Articles

Static Hand Gesture Recognition Using Capacitive Sensing and Machine Learning

Automated hand gesture recognition is a key enabler of Human-to-Machine Interfaces (HMIs) and smart living. This paper reports the development and testing of a static hand gesture recognition system using capacitive sensing. Our system consists of a 6×18 array of capacitive sensors that captured five gestures-Palm, Fist, Middle, OK, and Index-of five participants to create a dataset of gesture images. The dataset was used to train Decision Tree, Naïve Bayes, Multi-Layer Perceptron (MLP) neural network, and Convolutional Neural Network (CNN) classifiers. Each classifier was trained five times; each time, the classifier was trained using four different participants' gestures and tested with one different participant's gestures. The MLP classifier performed the best, achieving an average accuracy of 96.87% and an average F1 score of 92.16%. This demonstrates that the proposed system can accurately recognize hand gestures and that capacitive sensing is a viable method for implementing a non-contact, static hand gesture recognition system.

An improved hand gesture recognition system using keypoints and hand bounding boxes

Hand gesture recognition is a significant problem for human–computer interaction. One form of hand gesture recognition is static hand gestures. This study developed a static hand gesture recognition system, consisting of three modules: Feature extraction Module, Processing Module, and Classification Module. The feature extraction module uses human pose estimation with a top-down method to extract not only the keypoints but also body and hand bounding boxes. After being normalized and processed in the processing module, its output will be used as the input for the classification module in which we proposed an architecture called Two-pipeline architecture. In this module, we also employ different methods to find the most suitable one for this task. Experiments were conducted on three datasets called HANDS, OUHANDS, and SHAPE. Results showed that the proposed Two-pipeline architecture with 2.5 million parameters obtained accuracy of 94%, 98%, and 94% on three datasets. In addition, the lightweight version with 0.22 million parameters also achieved accuracy of 91%, 94%, and 96%.

Recognition of static hand gesture with using ANN and SVM

Hand gesture recognition is a relevant study topic for a reason that sometimes we may not be in position to communicate verbally. There is need to design Hand gesture recognition systems in order to help people adopt to nonverbal communication mainly sign language. However, there is no clue to understand the meaning of gesture through the computers directly. So this calls for definitions that generalize models in a computer. That is why the machine-learning approaches are implemented in recognition systems. There are generally two types of hand gestures recognition systems which researches have concentrated on. These include static and dynamic Hand gesture recognition systems. However, in building Hand gesture recognition systems, various machine learning approaches have been used. For implementing the proposed system, MS Kinect depth sensor was used as a hardware. The Kinect depth sensor is composed of an infrared camera. This is an advantage to the systems that are designed basing on the depth sensing because factors like color, clothing and background have less effect on the performance. So Kinect based depth sensor systems have a high accuracy and performance making them relevant and applicable in our daily lives. In this paper, we propose a static hand gesture recognition system in real time using two machine learning methods namely Support Vector Machine and Artificial Neural Networks. We use of the newly launched Microsoft Kinect sensor for image extraction. The sensor helps us to extract the hand images. We implement the system on a Matlab platform for reasons that Matlab is widely used by researchers in different fields and that can handle complex computations. In the training of the model, we collect a hundred depth-based Histogram of Oriented Gradient features per alphabet from the hand gesture images which we trained, tested and validated using Artificial Neural Networks (ANN) and Support Vector Machine (SVM). From this dataset, we can generate the generalized gesture model for each alphabet image. For the proposed system, the classification with ANN proves a higher performance then SVM.

Hand gesture recognition using DWT and F ‐ratio based feature descriptor

This study demonstrates the development of vision based static hand gesture recognition system using web camera in real-time applications. The vision based static hand gesture recognition system is developed using the following steps: preprocessing, feature extraction and classification. The preprocessing stage consists of illumination compensation, segmentation, filtering, hand region detection and image resize. This study proposes a discrete wavelet transform (DWT) and Fisher ratio ( F -ratio) based feature extraction technique to classify the hand gestures in an uncontrolled environment. This method is not only robust towards distortion and gesture vocabulary, but also invariant to translation and rotation of hand gestures. A linear support vector machine is used as a classifier to recognise the hand gestures. The performance of the proposed method is evaluated on two standard public datasets and one indigenously developed complex background dataset for recognition of hand gestures. All above three datasets are developed based on American Sign Language (ASL) hand alphabets. The experimental result is evaluated in terms of mean accuracy. Two possible real-time applications are conducted, one is for interpretation of ASL sign alphabets and another is for image browsing.

Hand gesture recognition method based on HOG-LBP features for mobile devices

Hand Gesture recognition becomes a challenging task in computer vision field especially after the appearance of mobile devices. Although gesture recognition algorithms have been widely applied to the human-mobile interaction systems, it is difficult to meet the real time requirements and the robustness in different lightening conditions and backgrounds. This paper provides a description of a practical investigation into ways of improving hand gesture recognition algorithms for computationally limited handheld Android devices. We thoroughly researched possible approaches and find out how they affect the mobile device in terms of execution time and energy consumption. We have identified a combined HOG-LBP methodology whose performance improved upon the detection rate of other systems. In this paper, we present a static hand gesture recognition system for mobile devices by combining the histogram of oriented gradients (HOG) and local binary pattern (LBP) features, which can accurately detect hand poses. With the help of combining HOG and LBP features, we achieved a detection rate of approximately 92% on the enhanced NUS dataset I. This combination performs a better result than the results obtained when using only LBP or HOG features in term of recognition rate. However, in term of execution and detection time, LBP and HOG perform better than the combined HOG-LBP.

A robust static hand gesture recognition system using geometry based normalizations and Krawtchouk moments

Static hand gesture recognition involves interpretation of hand shapes by a computer. This work addresses three main issues in developing a gesture interpretation system. They are (i) the separation of the hand from the forearm region, (ii) rotation normalization using the geometry of gestures and (iii) user and view independent gesture recognition. The gesture image comprising the hand and the forearm is detected through skin color detection and segmented to obtain a binary silhouette. A novel method based on the anthropometric measures of the hand is proposed for extracting the regions constituting the hand and the forearm. An efficient rotation normalization method that depends on the gesture geometry is devised for aligning the extracted hand. These normalized binary silhouettes are represented using the Krawtchouk moment features and classified using a minimum distance classifier. The Krawtchouk features are found to be robust to viewpoint changes and capable of achieving good recognition for a small number of training samples. Hence, these features exhibit user independence. The developed gesture recognition system is robust to similarity transformations and perspective distortions. It can be well realized for real-time implementation of gesture based applications.