STUDY OF HAND GESTURE RECOGNITION AND CLASSIFICATION

To recognize different hand gestures and achieve efficient classification to understand static and dynamic hand movements used for communications.Static and dynamic hand movements are first captured using gesture recognition devices including Kinect device, hand movement sensors, connecting electrodes, and accelerometers. These gestures are processed using hand gesture recognition algorithms such as multivariate fuzzy decision tree, hidden Markov models (HMM), dynamic time warping framework, latent regression forest, support vector machine, and surface electromyogram. Hand movements made by both single and double hands are captured by gesture capture devices with proper illumination conditions. These captured gestures are processed for occlusions and fingers close interactions for identification of right gesture and to classify the gesture and ignore the intermittent gestures. Real-time hand gestures recognition needs robust algorithms like HMM to detect only the intended gesture. Classified gestures are then compared for the effectiveness with training and tested standard datasets like sign language alphabets and KTH datasets. Hand gesture recognition plays a very important role in some of the applications such as sign language recognition, robotics, television control, rehabilitation, and music orchestration

A Reduced Classifier Ensemble Approach to Human Gesture Classification for Robotic Chinese Handwriting

The paper presents an approach to applying a classifier ensemble to identify human body gestures, so as to control a robot to write Chinese characters. Robotic handwriting ability requires complicated robotic control algorithms. In particular, the Chinese handwriting needs to consider the relative positions of a character’s strokes. This approach derives the font information from human gestures by using a motion sensing input device. Five elementary strokes are used to form Chinese characters, and each elementary stroke is assigned to a type of human gestures. Then, a classifier ensemble is applied to identify each gesture so as to recognize the characters that gestured by the human demonstrator. The classier ensemble’s size is reduced by feature selection techniques and harmony search algorithm, thereby achieving higher accuracy and smaller ensemble size. The inverse kinematics algorithm converts each stroke’s trajectory to the robot’s motor values that are executed by a robotic arm to draw the entire character. Experimental analysis shows that the proposed approach can allow a human to naturally and conveniently control the robot in order to write many Chinese characters

Proceedings. 22. Workshop Computational Intelligence, Dortmund, 6. - 7. Dezember 2012

Dieser Tagungsband enthält die Beiträge des 22. Workshops "Computational Intelligence" des Fachausschusses 5.14 der VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik (GMA) der vom 6. - 7. Dezember 2012 in Dortmund stattgefunden hat. Die Schwerpunkte sind Methoden, Anwendungen und Tools für - Fuzzy-Systeme, - Künstliche Neuronale Netze, - Evolutionäre Algorithmen und - Data-Mining-Verfahren sowie der Methodenvergleich anhand von industriellen Anwendungen und Benchmark-Problemen

Self-adaptive structure semi-supervised methods for streamed emblematic gestures

Although many researchers try to improve the level of machine intelligence, there is still a long way to achieve intelligence similar to what humans have. Scientists and engineers are continuously trying to increase the level of smartness of the modern technology, i.e. smartphones and robotics. Humans communicate with each other by using the voice and gestures. Hence, gestures are essential to transfer the information to the partner. To reach a higher level of intelligence, the machine should learn from and react to the human gestures, which mean learning from continuously streamed gestures. This task faces serious challenges since processing streamed data suffers from different problems. Besides the stream data being unlabelled, the stream is long. Furthermore, “concept-drift” and “concept evolution” are the main problems of them. The data of the data streams have several other problems that are worth to be mentioned here, e.g. they are: dynamically changed, presented only once, arrived at high speed, and non-linearly distributed. In addition to the general problems of the data streams, gestures have additional problems. For example, different techniques are required to handle the varieties of gesture types. The available methods solve some of these problems individually, while we present a technique to solve these problems altogether. Unlabelled data may have additional information that describes the labelled data more precisely. Hence, semi-supervised learning is used to handle the labelled and unlabelled data. However, the data size increases continuously, which makes training classifiers so hard. Hence, we integrate the incremental learning technique with semi-supervised learning, which enables the model to update itself on new data without the need of the old data. Additionally, we integrate the incremental class learning within the semi-supervised learning, since there is a high possibility of incoming new concepts in the streamed gestures. Moreover, the system should be able to distinguish among different concepts and also should be able to identify random movements. Hence, we integrate the novelty detection to distinguish between the gestures that belong to the known concepts and those that belong to unknown concepts. The extreme value theory is used for this purpose, which overrides the need of additional labelled data to set the novelty threshold and has several other supportive features. Clustering algorithms are used to distinguish among different new concepts and also to identify random movements. Furthermore, the system should be able to update itself on only the trusty assignments, since updating the classifier on wrongly assigned gesture affects the performance of the system. Hence, we propose confidence measures for the assigned labels. We propose six types of semi-supervised algorithms that depend on different techniques to handle different types of gestures. The proposed classifiers are based on the Parzen window classifier, support vector machine classifier, neural network (extreme learning machine), Polynomial classifier, Mahalanobis classifier, and nearest class mean classifier. All of these classifiers are provided with the mentioned features. Additionally, we submit a wrapper method that uses one of the proposed classifiers or ensemble of them to autonomously issue new labels to the new concepts and update the classifiers on the newly incoming information depending on whether they belong to the known classes or new classes. It can recognise the different novel concepts and also identify random movements. To evaluate the system we acquired gesture data with nine different gesture classes. Each of them represents a different order to the machine e.g. come, go, etc. The data are collected using the Microsoft Kinect sensor. The acquired data contain 2878 gestures achieved by ten volunteers. Different sets of features are computed and used in the evaluation of the system. Additionally, we used real data, synthetic data and public data as support to the evaluation process. All the features, incremental learning, incremental class learning, and novelty detection are evaluated individually. The outputs of the classifiers are compared with the original classifier or with the benchmark classifiers. The results show high performances of the proposed algorithms