Vision-Based Hand Gesture Spotting and Recognition Using CRF and SVM

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

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.

This article presents a robotic visual system that allows effective recognition of multiple-angle hand gestures in finger guessing games. We shot images of hand gestures at various angles to train support vector machine (SVM) for the construction of a multiple-angle hand gesture recognition system. Our experimental results show that the hand gesture recognition system presented by this article can effectively recognize hand gestures, at over 95%, of different angles and sizes, with different ornaments, and of different skin colors, while recognizing right or left hand gestures, at up to 90% recognition rate.

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.

Communication for mute people has always been very difficult. Mute people use hand gestures, also known as sign language to communicate with normal people. Hands gestures have their assigned meanings which may differ from person to person and hence cannot be understood by normal people. To overcome this difficulty many vision-based hand gesture recognition systems and data glove based hand gesture recognition systems have been proposed. This paper illustrates about two different techniques of vision-based hand gesture recognition and one data glove based technique. The vision — based techniques are static hand gesture recognition technique and real-time hand gesture recognition technique. In both the techniques, MATLAB software is used for processing input images and no dataset is used for decision making which makes this system more accurate than the existing system designs. In data glove based technique, the glove consists of five flex sensors. The change in resistance of flex sensor is used to recognize the hand gesture. All the three techniques are performed on 10 subjects and compared to find the most accurate technique. It was found that both the vision based techniques showed 100% accuracy in bright lighting condition with a white background while the data glove based technique showed an accuracy of 86%. This which clearly shows that mentioned vision based is more stable and reliable compared to the data glove based technique.

In this research paper, an effort has been placed to convert 24 American Sign Language (ASL) signer independent, real time hand gesture alphabets into human or machine recognizable English text. In the proposed work, the ASL hand gestures used for cognition and recognition process is completely invariant to scale, luminance, gender, and distance in the complex background of indoor location. The Viola-Jones algorithm, CIE Lab color model and canny approximation to the derivative are used for proper hand segmentation. In both the cognition and recognition process, the various features such as boundary, centroid, entropy, Hu moments, Zernike moments, Gabor filters, Histogram of Oriented Gradients (HOG) and Local Phase Quantization (LPQ) are extracted from the hand gestures. The K-Nearest Neighbor (KNN), Multiclass-Support Vector Machines (M-SVM) and Decision Tree (DT) classifiers are used for classifying the hand gestures. In recognition task, these classifiers are applied independently on the same set of hand gestures to check the optimality of recognition rate and recognition time. With the detailed experimentation, it is found that, the KNN classifier achieved an average recognition rate and average recognition time of 92.71% and 0.48 s per gestures. This recognition rate and time is better and optimal compared to M-SVM and DT classifiers. Also it is an inspiring result compared to state of art techniques in real time environment by considering various invariants.

<p><em>Hand gestures are the communication ways for the deaf people and the other. Each hand gesture has a different meaning. In order to better communicate, we need an automatic translator who can recognize hand movements as a word or sentence in communicating with deaf people. </em><em>This paper proposes a system to recognize hand gestures based on Indonesian Sign Language Standard. This system uses Myo Armband as hand gesture sensors. Myo Armband has 21 sensors to express the hand gesture data. Recognition process uses a Support Vector Machine (SVM) to classify the hand gesture based on the dataset of Indonesian Sign Language Standard. SVM yields the accuracy of 86.59% to recognize hand gestures as sign language.</em></p><p><em><strong>Keywords</strong></em><em>: </em><em>Hand Gesture Recognition, Feature Extraction, Indonesian Sign Language, Myo Armband, Moment Invariant</em></p>

This paper proposes a system to recognize isolated American Sign Language and Arabic numbers in real-time from stereo color image sequences using Hidden Markov Models (HMMs). Our system is based on three main stages; preprocessing, feature extraction and classification. In preprocessing stage, color and 3D depth map are used to detect and track the hand. The second stage, 3D combined features of location, orientation and velocity with respected to Cartesian and Polar systems are used. Additionally, k-means clustering is employed for HMMs code-word. In the final stage, the hand gesture path is recognized using Left-Right Banded topology (LRB) in conjunction Viterbi path. Experimental results demonstrate that, our system can successfully recognize isolated hand gestures with 98.33% recognition rate.

In human-computer interaction or sign language interpretation, recognizing hand gestures and face detection become predominant in computer vision research. The primary goal of this proposed system is to create a system, which can identify hand gestures and facial detection to convey information for controlling media player. For those who are deaf and dumb sign language is a common, efficient and alternative way for talking, by using the hand and facial gestures we can easily understand them. Here hand and face are directly use as the input to the device for effective communication purpose of gesture identification there is no need of an intermediate medium.

Instant 3D design concept generation and visualization by real-time hand gesture recognition

Hand gesture recognition is an important area of research in the field of Human Computer Interaction (HCI). The geometric attributes of the hand play an important role in hand shape reconstruction and gesture recognition. That said, fingertips are one of the important attributes for the detection of hand gestures and can provide valuable information from hand images. Many methods are available in scientific literature for fingertips detection with an open hand but very poor results are available for fingertips detection when the hand is closed. This paper presents a new method for the detection of fingertips in a closed hand using the corner detection method and an advanced edge detection algorithm. It is important to note that the skin color segmentation methodology did not work for fingertips detection in a closed hand. Thus the proposed method applied Gabor filter techniques for the detection of edges and then applied the corner detection algorithm for the detection of fingertips through the edges. To check the accuracy of the method, this method was tested on a vast number of images taken with a webcam. The method resulted in a higher accuracy rate of detections from the images. The method was further implemented on video for testing its validity on real time image capturing. These closed hand fingertips detection would help in controlling an electro-mechanical robotic hand via hand gesture in a natural way.

Researchers have recently focused their attention on vision-based hand gesture recognition. However, due to several constraints, achieving an effective vision-driven hand gesture recognition system in real time has remained a challenge. This paper aims to uncover the limitations faced in image acquisition through the use of cameras, image segmentation and tracking, feature extraction, and gesture classification stages of vision-driven hand gesture recognition in various camera orientations. This paper looked at research on vision-based hand gesture recognition systems from 2012 to 2022. Its goal is to find areas that are getting better and those that need more work. We used specific keywords to find 108 articles in well-known online databases. In this article, we put together a collection of the most notable research works related to gesture recognition. We suggest different categories for gesture recognition-related research with subcategories to create a valuable resource in this domain. We summarize and analyze the methodologies in tabular form. After comparing similar types of methodologies in the gesture recognition field, we have drawn conclusions based on our findings. Our research also looked at how well the vision-based system recognized hand gestures in terms of recognition accuracy. There is a wide variation in identification accuracy, from 68% to 97%, with the average being 86.6 percent. The limitations considered comprise multiple text and interpretations of gestures and complex non-rigid hand characteristics. In comparison to current research, this paper is unique in that it discusses all types of gesture recognition techniques.

We present a novel way to use the Scale Invariance Feature Transform (SIFT) on binary images. As far as we know, we proposed employ SIFT on binary images for hand gesture recognition and provide more accurate result comparing to traditional template approaches. There exist many restrictions on template matching approaches, such as the rotation must be less than 15°, and the variation on scale, etc. However, our proposed approach is robust against rotations, scaling, illumination conditions, and can recognize hand gestures in real-time with only off-the-shelf camera such as webcams. The proposed approach employs the SIFT features on binary image, the k-means clustering to map keypoints into a unified dimensional histogram vector (bag-of-words), and the Support Vector Machine (SVM) to classify different hand gestures.

There has been growing interest in the development of new approaches and technologies for bridging the human-computer barrier. Hand gesture recognition is considered as an interaction technique having potential to communicate with machines. Human computer interaction (HCI) was never an easy task and lots of approaches are available to build such systems. Hand gesture recognition (HGR) using wearable data glove provides a solution to build a HCI system, but lags in terms of its computational time and poor interface. Pattern matching is one more solution which uses vision based techniques and provides strong interface to build HCI systems. But again, it requires complex algorithms which takes lots of computational time and hence limits its use in real time HCI applications. In this paper, we presented an eccentric approach for hand gesture recognition which is simple, fast and user independent and can be used to develop real time HCI applications. Based on proposed algorithm we built a system for Indian Sign Language recognition which converts Indian Sign numbers into text. The algorithm first captures the image of single handed gesture of speech/hearing impaired person using simple webcam and then using our proposed algorithm it classifies the gesture into its appropriate class. It uses simple logical conditions for gesture classification which make its use in real time HCI applications.

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.