A biometric security based electronic gadget control using hand gestures

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.

To examine the feasibility of a novel technology platform that enables real-time touchless interaction with radiology images in both a simulated and an actual clinical setting. This platform offers three different modes for image interaction. The gesture recognition mode uses a depth camera to detect the user's hand gestures which are translated to image manipulation commands. The light projection mode uses the same camera to detect finger point-and-tap movements above the icons which are projected on a surface to activate the commands. The capacitive sensing mode is enabled by a handheld, portable device, over which finger movements are detected by capacitive sensors to control the image review. Following initial feedback, light projection and capacitive sensing modes were selected for further testing by comparing with the conventional mode of image interaction in time trials for performing a series of standardized image manipulation tasks. Finally, the usability of the technology platform was examined in actual clinical procedures. The light projection and the capacitive sensing modes were evaluated in the time trials and exhibited 60% and 71% reduction in time, respectively, relative to the control mode (p < 0.001). Clinical feasibility for this platform was demonstrated in three actual interventional radiology cases. Accessing, navigating, and extracting relevant information from patient images intraprocedurally are cumbersome and time-consuming tasks that affect safety, efficiency, and decision-making during image-guided procedures. This study demonstrated that the novel technology addressed this issue by allowing touchless interaction with these images in the sterile field.

In Deep Learning, recent works show that neural networks have a high potential in the field of biometric security. The advantage of using this type of architecture, in addition to being robust, is that the network learns the characteristic vectors by creating intelligent filters in an automatic way, grace to the layers of convolution. In this paper, we propose an algorithm “FMnet” for iris recognition by using Fully Convolutional Network (FCN) and Multi-scale Convolutional Neural Network (MCNN). By taking into considerations the property of Convolutional Neural Networks to learn and work at different resolutions, our proposed iris recognition method overcomes the existing issues in the classical methods which only use handcrafted features extraction, by performing features extraction and classification together. Our proposed algorithm shows better classification results as compared to the other state-of-the-art iris recognition approaches.

Puppet play is often seen in many countries as a type of traditional show, which is a form of theater or performance that involves the control of puppets. The actors rely on the story outline and add their own creative improvisation to complete a perfect performance. Recently, this traditional show is becoming less popular due to their high cost such as professional stage, expensive puppets and the preparation time for a show. In this paper, we propose a method that user can play a puppet show on computer. The Leap Motion will catch user's hand gesture and show result of controlling puppet in computer screen. User can choose different puppets, scenes and music in our system. Anyone can play a puppet show easily by themselves.

This study is to propose a realistic game system using a multi-modal interface, including gaze tracking, hand gesture recognition and bio-signal analysis. Our research is novel in the following four ways, compared to previous game systems. First, a highly immersive and realistic game is implemented on a head mounted display (HMD), with a gaze tracker, a gesture recognizer and a bio-signal analyzer. Second, since the camera module for eye tracking is attached below the HMD, a user's gaze position onto the HMD display can be calculated without wearing any additional eye tracking devices. Third, an aiming cursor in the game system is controlled by the gaze tracking. The grabbing and throwing behaviors toward a target are performed by the user's hand gestures using a data glove. Finally, the level of difficulty in the game system is adaptively controlled according to the measurement and analysis of a user's bio-signals. Experimental results show that the proposed method provides more effect on experience of immersion and interest than conventional device such as a keyboard and or a mouse.

This paper presents a teleoperation system that includes robot perception and intent prediction from hand gestures. The perception module identifies the objects present in the robot workspace and the intent prediction module which object the user likely wants to grasp. This architecture allows the approach to rely on traded control instead of direct control: we use hand gestures to specify the goal objects for a sequential manipulation task, the robot then autonomously generates a grasping or a retrieving motion using trajectory optimization. The perception module relies on the model-based tracker to precisely track the 6D pose of the objects and makes use of a state of the art learning-based object detection and segmentation method, to initialize the tracker by automatically detecting objects in the scene. Goal objects are identified from user hand gestures using a trained a multi-layer perceptron classifier. After presenting all the components of the system and their empirical evaluation, we present experimental results comparing our pipeline to a direct traded control approach (i.e., one that does not use prediction) which shows that using intent prediction allows to bring down the overall task execution time.

A novel hand biometric authentication method based on measurements of the user’s stationary hand gesture of hand sign language is proposed. The measurement of hand gestures could be sequentially acquired by a low-cost video camera. There could possibly be another level of contextual information, associated with these hand signs to be used in biometric authentication. As an analogue, instead of typing a password ‘iloveu’ in text which is relatively vulnerable over a communication network, a signer can encode a biometric password using a sequence of hand signs, ‘i’ , ‘l’ , ‘o’ , ‘v’ , ‘e’ , and ‘u’. Subsequently the features from the hand gesture images are extracted which are integrally fuzzy in nature, to be recognized by a classification model for telling if this signer is who he claimed himself to be, by examining over his hand shape and the postures in doing those signs. It is believed that everybody has certain slight but unique behavioral characteristics in sign language, so are the different hand shape compositions. Simple and efficient image processing algorithms are used in hand sign recognition, including intensity profiling, color histogram and dimensionality analysis, coupled with several popular machine learning algorithms. Computer simulation is conducted for investigating the efficacy of this novel biometric authentication model which shows up to 93.75% recognition accuracy.

Generally, users have controlled objects in a 3D virtual space by using a mouse and a keyboard. However, it is not easy to carry out actions in 3D virtual space through the devices since others could be used to communicate about some issues. Therefore, in this paper, we propose a system in which hand gestures of users can be used to control objects. In the proposed system, an object can be picked up through a specific hand gesture, and it can then be translated, rotated, and scaled in x, y, z directions according to the recognized hand gestures.

The goal of static hand gesture recognition is to classify the given hand gesture data represented by some features into some prede ned nite number of gesture classes. The main objective of this e ort is to explore the utility of two feature extraction methods, namely, hand contour and complex moments to solve the hand gesture recognition problem by identifying the primary advantages and disadvantages of each method. Arti cial neural network is built for the purpose of classi cation by using the back-propagation learning algorithm. The proposed system presents a recognition algorithm to recognize a set of six speci c static hand gestures, namely: Open, Close, Cut, Paste, Maximize, and Minimize. The hand gesture image is passed through three stages, namely, pre-processing, feature extraction, and classi cation. In the pre-processing stage some operations are applied to extract the hand gesture from its background and prepare the hand gesture image for the feature extraction stage. In the rst method, the hand contour is used as a feature which treats scaling and translation problems (in some cases). The complex moments algorithm is, however,

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.

The programmed way of establishing and validating the existing person upon their corporal and observable characters are termed as biometric technology. Due to the accuracy of the iris recognition, it becomes more dominant in the available biometric techniques. The current study aims at a new technology for iris recognition which helps us to identify a human by the iris from various places. This method is more valid and protected when compared with the other biometric technologies. In this biometric, the human characters are used which will not change during the lifetime of that particular individual. The time taken for identification of individual human is very less. Iris recognition uses the uniqueness of the eye and the information is stored in the iris database. The movement of the human individual is recognized by the gait analysis and iris recognition is more liable in the existing biometric systems. The study mainly focuses on the iris preprocessing, edge detection, and feature extraction, finally gait and iris fusion classification techniques in the research area. Without the help of a particular individual, we can use the gait analysis for iris recognition. The security places such as banks, places which are used in elections, military installations and even airport, where more restriction to provide the details of the human use this biometric technology.

A human gesture prediction system can be used to estimate human gestures in advance of the actual action to reduce delays in interactive systems. Hand gestures are particularly necessary for human–computer interaction. Therefore, the gesture prediction system must be able to capture hand movements that are both complex and quick. We have already reported a method that allows strain sensors and wearable devices to be fabricated in a simple and easy manner using pyrolytic graphite sheets (PGSs). The wearable electronics could detect various types of human gestures with high sensitivity, high durability, and fast response. In this study, we demonstrated hand gesture prediction by artificial neural networks (ANNs) using gesture data obtained from data gloves based on PGSs. Our experiments entailed measuring the hand gestures of subjects for learning purposes and we used these data to create four-layered ANNs, which enabled the proposed system to successfully predict hand gestures in real time. A comparison of the proposed method with other algorithms using temporal data analysis suggested that the hand gesture prediction system using ANNs would be able to forecast various types of hand gestures using resistance data obtained from wearable devices based on PGSs.

Iris recognition systems are among the most accurate of all biometric technologies with immense potential for use in worldwide security applications. This study examined the effect of eye pathology on iris recognition and in particular whether eye disease could cause iris recognition systems to fail. The experiment involved a prospective cohort of 54 patients with anterior segment eye disease who were seen at the acute referral unit of the Princess Alexandra Eye Pavilion in Edinburgh. Iris camera images were obtained from patients before treatment was commenced and again at follow-up appointments after treatment had been given. The principal outcome measure was that of mathematical difference in the iris recognition templates obtained from patients' eyes before and after treatment of the eye disease. Results showed that the performance of iris recognition was remarkably resilient to most ophthalmic disease states, including corneal oedema, iridotomies (laser puncture of iris) and conjunctivitis. Problems were, however, encountered in some patients with acute inflammation of the iris (iritis/anterior uveitis). The effects of a subject developing anterior uveitis may cause current recognition systems to fail. Those developing and deploying iris recognition should be aware of the potential problems that this could cause to this key biometric technology.

Human–computer communication using hand gestures has always been difficult. More than half a century ago, people used differentways of interactionwith computers from the early mediums such as perforated game cards. Nowadays, if a richer lexicon of gestures is given, people can communicate more effectively with computers. Machine learning is now used to recognize and classify hand gestures in amore preciseway. In order to increase the communication between computers and humans, we proposed a technique, which uses a wearable low-cost device to generate the electrical impedance tomography (EIT) images to recover the inner impedance structure of a user’s wrist. This is done by measuring the transverse impedance between all the 16 pairs of electrodesofwrist band that lie on the skin of the user hand. The proposedtechnique is enough to integrate the technology into the prototypewrist band tomonitor and classify gestures in real time. We have conducted a study of 16 gestures with a focus on gross hand and pinch finger gestures. The results evaluation shows that the gross hand gestures achieved 90% accuracy inwrist position, while pinch gestures achieved 93% accuracy.

New human machine interfaces are becoming more and more necessary in robotics applications where the robot operates in close cooperation with a human, and one near to each other. Many of these applications require intuitive interaction systems since they are used by non robotic experts, thus the use of natural language based interfaces (either voice or gestures) are highly convenient. This research tackles the problem of interpreting the user arm and hand gestures as orders to the system, while the user is working close to the robot. The proposed human-machine interface looks for a compromise between classical interfaces and the hands free operation requirements.