A Vision-Based Method to Find Fingertips in a Closed Hand

MuViH: Multi-View Hand gesture dataset and recognition pipeline for human–robot interaction in a collaborative robotic finishing platform

Human hand recognition plays an important role in a wide range of applications ranging from sign language translators, gesture recognition, augmented reality, surveillance and medical image processing to various Human Computer Interaction (HCI) domains. Human hand is a complex articulated object consisting of many connected parts and joints. Therefore, for applications that involve HCI one can find many challenges to establish a system with high detection and recognition accuracy for hand posture and/or gesture. Hand posture is defined as a static hand configuration without any movement involved. Meanwhile, hand gesture is a sequence of hand postures connected by continuous motions. During the past decades, many approaches have been presented for hand posture and/or gesture recognition. In this paper, we provide a survey on approaches which are based on Hidden Markov Models (HMM) for hand posture and gesture recognition for HCI applications.

With the escalating role of computers in educational system, human computer interaction, is becoming gradually more important part of it. The general believe is that with the progress in computing speed, communication technologies, and display techniques the existing HCI techniques may become a constraint in the effectual utilization of the existing information flow. The development of user interfaces influences the changes in the Human-Computer Interaction (HCI). Human hand gestures have been a mode of non verbal interaction widely used. The vocabulary of hand gesture communication has many variations. It ranges from simple action of using our finger to point at and using hands to move objects around for more complex expressions for the feelings and communicating with others. Also the hand gestures play a prominent role in teaching considering the explanations and exemplifications being highly dependent on hand gestures. Naturalistic and intuitiveness of the hand gesture has been an immense motivating aspect for the researchers in the field of Human Computer Interaction to put their efforts to research and develop the more promising means of interaction involving human and computers. The pursuance for the Human Computer Interaction research is moved by the central dogma of removing the complex and cumbersome interaction devices and replacing them with more obvious and expressive means of interaction which easily comes to the users with least cognitive burden like hand gestures. This paper designs a simple, natural system for gestural interaction between the user and computer for providing a dynamic user interface. The gesture recognition system uses image processing techniques for detection, segmentation, tracking and recognition of hand gestures for converting it to a meaningful command. This hand gesture recognition system has been proposed, designed and developed with the intensions to make it a substitute for mouse while making dynamic user interface between human and machine. Hence instead of making effort to develop a new vocabulary of hand gesture we have matched control instruction set of mouse to subset of most discriminating hand gestures, so that we get a robust interface. The interface being proposed here can be substantially applied towards different applications like image browser, games etc.

In this paper we have implemented a wireless vision based mobile robot control through hand gesture recognition based on perceptual color space such as HIS, HSV/HSB, HSL. Vision-based hand gesture recognition is an important problem in the field of human-computer interaction, since hand motions and gestures could potentially be used to interact with computers in more natural ways. The robot control was done purely based on the orientation histograms a simple and fast algorithm on the system which would recognize static hand gestures with HSV color spaces as major parameters. The wireless based mobile robot system using hand gestures is a new innovative user interface that resolves the complications of using numerous remote controls for various applications. Based on one unified set of hand gestures, this system interprets the user hand gestures into pre-defined commands to control the remote robot. The experimental results are very encouraging as the system produces real-time responses and highly accurate recognition towards various gestures under different lighting conditions.

The use of hand gestures provides a natural alternative to cumbersome interface devices for Human-Computer Interaction (HCI) systems. However, real-time recognition of dynamic micro hand gestures from video streams is challenging for in-vehicle scenarios since (i) the gestures should be performed naturally without distracting the driver, (ii) micro hand gestures occur within very short time intervals at spatially constrained areas, (iii) the performed gesture should be recognized only once, and (iv) the entire architecture should be designed lightweight as it will be deployed to an embedded system. In this work, we propose an HCI system for dynamic recognition of driver micro hand gestures, which can have a crucial impact in automotive sector especially for safety related issues. For this purpose, we initially collected a dataset named Driver Micro Hand Gestures (DriverMHG), which consists of RGB, depth and infrared modalities. The challenges for dynamic recognition of micro hand gestures have been addressed by proposing a lightweight convolutional neural network (CNN) based architecture which operates online efficiently with a sliding window approach. For the CNN model, several 3-dimensional resource efficient networks are applied and their performances are analyzed. Online recognition of gestures has been performed with 3D-MobileNetV2, which provided the best offline accuracy among the applied networks with similar computational complexities. The final architecture is deployed on a driver simulator operating in real-time. We make DriverMHG dataset and our source code publicly available 1.1https://www.mmk.ei.tum.de/DriverMHG/

Stationary hand gesture recognition can be applied in a variety of domains such as human-computer interaction (HCI), remote control, virtual reality etc. Hand gesture recognition is mainly the study of detection and recognition of various hand gestures like Sign Language hand gestures, by a computer. This work is focused on three main issues in developing a gesture recognition system. These are (i) Threshold (ii) illumination normalization (iii) user independent gesture recognition based on fusion of Moments. It is very demanding to segment hand regions from the static hand gesture color images, due to unreliable light conditions and composite background. Since skin pixels can vary with different explanation condition, to find the range of skin pixels, becomes a hard task in case of color space based skin color segmentation. These methods propose a learning algorithm based on customized clustering expanse to remove person skin color regions from the stationary hand gesture color descriptions. To improve the performance of classification, two feature synthesis strategies have been proposed in this work.

Considerable effort has been put toward the development of intelligent and natural interfaces between users and computer systems. In line with this endeavor, several modes of information (e.g., visual, audio, and pen) that are used either individually or in combination have been proposed. The use of gestures to convey information is an important part of human communication. Hand gesture recognition is widely used in many applications, such as in computer games, machinery control (e.g., crane), and thorough mouse replacement. Computer recognition of hand gestures may provide a natural computer interface that allows people to point at or to rotate a computer-aided design model by rotating their hands. Hand gestures can be classified into two categories: static and dynamic. The use of hand gestures as a natural interface serves as a motivating force for research on gesture taxonomy, its representations, and recognition techniques. This paper summarizes the surveys carried out in human--computer interaction (HCI) studies and focuses on different application domains that use hand gestures for efficient interaction. This exploratory survey aims to provide a progress report on static and dynamic hand gesture recognition (i.e., gesture taxonomies, representations, and recognition techniques) in HCI and to identify future directions on this topic.

The interaction between human and machine can be achieved by implementing human gesture applications which are known as human computer interaction (HCI). It has been developed several years ago, and now we can easily find it in many applications in real world e.g. in virtual reality, games, robotics, sign language recognition, etc. Gesture recognition has the potential to offer a natural way of communication between humans and machines. The most popular methods in human gesture application are hand gesture recognition. Several of hand gesture recognition methods still have the same problem when trying to recognize hand gesture accurately because of the existence of the uncontrolled environment problem, especially complex background. This study offered a method that can handle complex background in hand gesture recognition by analyzing pattern in spatio temporal domain. The system could reduce irregular noise in complex background significantly that affected to recognize the hand gesture. The experiment had been conducted using 180 videos with a total of 15 gestures. The result showed that the proposed method could recognize 15 hand gestures with accuracy of 98.33%.

Hand gesture for communication has proven effective for humans, and active research is ongoing in replicating the same success in computer vision systems. Human–computer interaction can be significantly improved from advances in systems that are capable of recognizing different hand gestures. In contrast to many earlier works, which consider the recognition of significantly differentiable hand gestures, and therefore often selecting a few gestures from the American Sign Language (ASL) for recognition, we propose applying deep learning to the problem of hand gesture recognition for the whole 24 hand gestures obtained from the Thomas Moeslund’s gesture recognition database. We show that more biologically inspired and deep neural networks such as convolutional neural network and stacked denoising autoencoder are capable of learning the complex hand gesture classification task with lower error rates. The considered networks are trained and tested on data obtained from the above-mentioned public database; results comparison is then made against earlier works in which only small subsets of the ASL hand gestures are considered for recognition.

The hand gesture provides a natural and intuitive communication medium for the human and machine interaction. Because, it can use in virtual reality, language detection, computer games, and other human-computer or human-machine instruction applications. Currently, the sensor and camera-based application is a field of interest for many researchers. This paper proposes a new hand gesture recognition system using the Kinect sensor's skeleton data, which works in an environment where people do not touch devices or communicate verbally. The proposed model focuses on mainly two modules, namely, hand area and fingertip detection, and hand gesture recognition. The hand area and fingertip are detected by positioning the palm point and find extreme of contour. And, the hand gesture is recognized by measuring the distance between different body indexes of skeleton information. Here, six gestures instructions are considered such as move right to left, move left to right, move up to down, move down to up, open and closed, and also recognize the numeric number using the fingertip. This system is able to detect the presence of hand area and fingers and to recognize different hand gestures. As a result, the average recognition accuracy of different hand gestures and stretched fingers numbers are 95.91% and 96%, respectively.

Interacting with mobile devices can be challenging in adverse working environments. Using hand gestures for interaction can overcome severe usability issues that users face when working with mobile devices in industrial settings. This chapter is dedicated to the design, implementation, and evaluation of mobile information systems with hand gesture recognition as means for human computer interaction. The chapter provides a concise theoretical background on human gestural interaction and gesture recognition, guidelines for the design of gesture vocabularies, recommendations for proper implementation, and parameterization of robust and reliable recognition algorithms on energy-efficient 8-bit architectures. It additionally presents an elaborated process for participatory design and evaluation of gesture vocabularies to ensure high usability in the actual context of use. The chapter concludes with a case study that proves the suitability of the proposed framework for the design of efficient and reliable hand gesture-based user interfaces.

Camera-based interactive wall display using hand gesture recognition

Hand gestures aid the user in unspoken communication and help interpret sign language. Modern nonverbal communication detection employs gesture recognition systems and allows us to recognize shapes and signals made by hand movements using numerous machine learning techniques. Gestures have a very high value in human-computer interaction and sign language detection systems. To implement the gesture recognition and detection the existing methodologies rely on the application of bodily sensors. Due to this, the effectiveness in communication becomes challenging and cumbersome. Hence targeting on the major fallacies of the existing approaches, the primary aspiration of this research work is to recognize hand gestures in real-time using depth sensors that support natural communication avoiding on body sensor dependency. A novel approach is presented to detect hand gestures using minimalistic training and the efficiency of other algorithms concerning this proposed technique has been compared. Experimental investigations on the proposed methodology have successfully confirmed that, the research exertion has obtained an overall gesture detection accuracy of 90.86% and a high precision of 98.25%.

In this paper, considering the characteristics of hand gestures that occur during a natural, unscripted human conversation, we report the results of experiments involving the data analysis of the video recordings of experiments. The recorded conversations between two subjects and the assistant in the experiments (the first author) are analyzed for findings that might be useful to future human-robot interaction (HRI), findings regarding the recognition of hand gestures in natural conversations. In the experiment, hand gestures that appeared naturally during the conversations were manually selected, and their elapsed times and number of appearances were analyzed. In addition, these unscripted conversations were analyzed with a voice recognition system, and the meanings of the hand gestures that appear in the recorded video were interpreted. It was observed that some gestures were made consciously and others unconsciously. Some gestures appeared multiple times with similar meanings and the same intention, especially gestures involving pointing at something to emphasize a sentence or word. Other observations included that there were times when (i) each subject made different hand gestures to convey the same meaning, (ii) both subjects made the same hand gesture but intended different meanings, and (iii) a specific gesture was made by the same subject but with different intended meanings. After considering the results of these experiments, we conclude that it is better to combine gestures with the utterances at the time in order to interpret their intended meanings correctly.

Technology becomes a part of human lives for decades, especially in human—computer interaction (HCI) that considered as the important research area involving with an assistive technology and a medical system. Hand gesture is classified as an intuitive method for human to interact with the computer. It is useful for elderly people who cannot express their feelings by words. This paper proposed the hand gesture recognition technique for the elderly by using contour detection with convex hull feature extraction and rule-based classification. Vision-based hand gesture recognition is considered in classifying six hand gestures as lingual description. From the experimental results, the hand gesture system provides good detection and classification results for all the six static hand gestures.