Review by g siminon latest 2011


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Review by g siminon latest 2011

  1. 1. Recent Researches in Circuits, Systems, Mechanics and Transportation Systems A Brief Review of Vision Based Hand Gesture Recognition GEORGIANA SIMION (1), VASILE GUI (2), MARIUS OTEȘTEANU Department of Communication Politehnica University of Timișoara Bd. Vasile Pârvan No.2, Timișoara ROMANIA,, marius.otesteanu@etc.upt.roAbstract: - The evolution of user interfaces shapes the changes in Human-Computer Interaction (HCI). Directuse of hand as an input device is an attractive method for providing natural HCI. The applications of gesturerecognition are manifold, ranging from sign language to medical rehabilitation to virtual reality. In this paperwe present a brief review of vision based hand gesture recognition.Key-Words: - hand gestures, recognition, model based approach, view based approach, human computerinteraction, applications1 Introduction there are non invasive and are based on the way People perform various gestures in their human beings perceive information about theirdaily lives. It is in our nature to use gestures in order surroundings. Although it is difficult to design ato improve the communication between us. Try to vision based interface for generic usage, yet it isimagine speaking with a person who makes no feasible to design such an interface for a controlledgesture. It is very difficult to understand if your environment but has no lake of challenges includingmessage is clear for him or her, if he or she agrees accuracy, processing speed.with your saying, in other words it is very hard to This paper is organized as follows: In section 2guess what type of reaction your message produces. we provide a survey on vision based hand gestureBetween all kind of gestures that we perform, hand recognition. In section 3 we present variousgestures play an important role. Hand gestures can applications areas for gesture recognition and inhelp us say more in less time. In these days, section 4 we give the conclusions.computers have become an important part in ourlives, so why not use hand gesture in order tocommunicate with them. 2 Problem Formulation The direct use of the hand as an input device is The approaches to Vision based hand gesturean attractive method for providing natural Human– recognition can be divided into two categories: 3 DComputer Interaction. Two approaches are hand model based approaches and appearance basedcommonly used to interpret gestures for Human approaches [1].Computer Interaction. Methods Which Use Data Gloves: Since 2.1 Model based approachnow, the only technology that satisfies the advanced Model based approaches attempt to infer therequirements of hand-based input for HCI is glove- pose of the palm and the joint angles, this approachbased sensing This method employs sensors is ideal for realistic interactions in virtual(mechanical or optical) attached to a glove that environments. By large, the approach consists oftransducers’ finger flexions into electrical signals searching for the kinematic parameters that bringsfor determining the hand posture. Several the 2D projection of a 3D model of hand intodrawbacks make this technology not so popular: correspondence with an edge-based image of afirst of all interaction with the computer-controlled hand.environment loses naturalness and easiness the user The model of the hand can be more or lessis forced to carry a load of cables which are elaborated.connected to the computer and it also requires A 3D model with 27 degrees of freedomcalibration and setup procedures. (DOF) was introduced and, it has been used in many Methods which are Vision Based: Computervision based techniques have the potential toprovide more natural and non-contact solutions,ISBN: 978-1-61804-062-6 181
  2. 2. Recent Researches in Circuits, Systems, Mechanics and Transportation Systemsstudies and it is shown in Fig. 1 a. between the profiles and edges extracted from the images. In [10] they have reformulated the problem within a Bayesian (probabilistic) framework. Bayesian approaches allow for the pooling of multiple sources of information (e.g. system dynamics, prior observations) to arrive at both an optimal estimate of the parameters and a probability distribution of the parameter space to guide future search for parameters. On contrary to Kalman filter approach, Bayesian approaches allow nonlinear . system formulations and non- Gaussian (multi- a) b) modal) uncertainty (e.g.caused by occlusions) at the Fig.1. Skeletal hand model: (a) Hand expense of a closed-form solution of the uncertainty. anatomy, (b) the kinematic model according to [7] In [12], a model-based visual hand posture The CMC joints are assumed to be fixed, tracking algorithm is proposed to guide a dexterouswhich quite unrealistically models the palm as a robot hand. The approach adopts a 3D model-basedrigid body. The fingers are modeled as planar serial framework with full-DOF kinematic and ankinematic chains attached to the palm at anchor effective measurement method based on chamferpoints located at MCP joints. distance for both silhouette and edges. GA is Over the years the kinematic model was integrated to traditional PF as a solution of high-improved by adding extra twist motion to MCP dimensional and multi-modal tracking.joints [2], [3] introducing one flexion/extension Experimental results show a significantDOF to CMC joints [4] or using a spherical joint for improvement of tracking performance comparedTM [5] with traditional PF. Rehg and Kanade [6] proposed one of theearliest model based approaches to the problem ofbare hand tracking. They used a 3D model with 27DOF for their system called DigitEyes. Heap et al.[8] proposed a deformable 3Dhand model and modeled the entire surface of thehand by a surface mash constructed via PCA fromtraining examples. Fig.3. a) The 3D model presented in [12],b) The 3D model presented in [13] In [13] proposed a realistic 3D model of the hand. This deformable model consists of a polygonal skin, driven by an underlying skeleton. A new pose is computed by linearly blending the motions that each skin vertex would undergo when rigidly coupled to a subset of the skeleton joints. The model is used in a) b) a particle filter framework. A novel algorithm whichFig.2. a) Hand tracking using 3D Point Distribution combines the SMD (Stochastic Meta-Descent) Model from [8] and b) Quadrics-based hand model optimization with a particle filter to form ‘smart particles‘ from [9] is proposed. After propagating the particles, SMD is Stenger et al. [9] used quadrics as shape performed and the resulting new particle set is includedprimitives. The use of quadrics to build the 3D such that the original Bayesian distribution is not altered.model yields a practical and elegant method for In [14,15] an approach to the recovery ofgenerating the contours of the model, which are then geometric and photometric pose parameters of a 3Dcompared with the image data. The pose of the hand model with 28 DOF from monocular imagemodel is estimated with an Unscented Kalman filter sequences is presented.(UKF), which minimizes the geometric errorISBN: 978-1-61804-062-6 182
  3. 3. Recent Researches in Circuits, Systems, Mechanics and Transportation Systems The 3D hand pose, the hand texture and the Another approach is to look for skin coloredilluminant are dynamically estimated through regions in the image. This is a very popular methodminimization of an objective function. Derived from [18], [19], [20], [21] but has some drawbacks. First,an inverse problem formulation, the objective skin color detection is very sensitive to lightingfunction enables explicit use of texture temporal conditions. While practicable and efficient methodscontinuity and shading information, while handling exist for skin color detection under controlled (andimportant self-occlusions and time-varying known) illumination, the problem of learning aillumination. The minimization is done efficiently flexible skin model and adapting it over time isusing a quasi-Newton method, for which was challenging. Lindberg [16] used scale-space colorproposed a rigorous derivation of the objective features to recognize hand gestures. Multi scalefunction gradient. features can be found in an image at different scales. In [16] truncated quadrics are used to build Therefore, the hand can be described as one biggera 3D hand model where the DOF for each joint blob feature for the palm, having smaller blobcorrespond to the DOF of a real hand. features representing the finger tips which areQuadratic chamfer distance function is used to connected by some rigid features. Furthermore, itcompute the edge likelihood and the silhouette was proposed to perform the feature extractionlikelihood is performed by a Bayesian classifier and directly in the color space, as this allows theonline adaptation of skin color probabilities. Particle combination of probabilistic skin colors directly infiltering is used to track the hand by predicting the the extraction phase. The advantage of directlynext state of 3D hand model. working on a color image lies in the better The 3D hand models are articulated distinction of hand and background regions, but thedeformable objects with many degrees of freedom; a authors showed real time application only with novery large image database is required to cover all other skin colored objects present in the scene.the characteristic shapes under different views. Another approach is to use the eigenspace.Another common problem with model based Given a set of images, eigenspace approachesapproaches is the problem of feature extraction and construct a small set of basis images thatlack of capability to deal with singularities that arise characterize the majority of the variation in thefrom ambiguous views. training set and can be used to approximate any of the training images. To reconstruct an image in the2.2 Appearance based approaches training set, a linear combination of the basis Appearance-based models are derived directly vectors (images) are taken, where the coefficients offrom the information contained in the images and the basis vectors are the result of projecting thehave traditionally been used for gesture recognition. image to be reconstructed on to the respective basisNo explicit model of the hand is needed; this means vectors. In [17] an approach for tracking hands byno internal degrees of freedom to be specifically an eigenspace approach is presented. The authorsmodeled. provide three major improvements to the original When only the appearance of the hand in the eigenspace approach formulation, namely, a largevideo frames is known, differentiating between invariance to occlusions, some invariance togestures is not as straight forward as with the model differences in background from the input imagesbased approach. The gesture recognition will and the training images, and the ability to handletherefore typically involve some sort of statistical both small and large affine transformations (i.e.classifier based on a set of features that represent the scale and rotation) of the input image with respect tohand. In many gesture applications all that are the training images. The authors demonstrate theirrequired is a mapping between input video and approach with the ability to track four hand gesturesgesture. Therefore, many have argued that the full using 25 basis images.reconstruction of the hand is not essential for In the last years is noticeable a new trend, moregesture recognition. Instead many approaches have and more approaches use invariant local featuresutilized the extraction of low-level image [24], [25], [26], [27], [28], [29], [30], [31].measurements that are fairly robust to noise and can In [24], Adaboost learning algorithm with SIFTbe extracted quickly. Low-level features that have features is used. The Scale Invariant Featurebeen proposed in the literature include: the centroid Transform (SIFT) introduced by Lowe [32] consistsof the hand region [16], principle axes defining an of a histogram representing gradient orientation andelliptical bounding region of the hand, and the magnitude information within a small image patch.optical flow/affine flow [17] of the hand region in a SIFT is a rotation and scale invariant feature and isscene. robust to some variations of illuminations,ISBN: 978-1-61804-062-6 183
  4. 4. Recent Researches in Circuits, Systems, Mechanics and Transportation Systemsviewpoints and noise. The accuracy of multi-class mixture of the part distributions. From all candidatehand posture recognition is improved by the sharing compositions, relevant compositions must befeature concept. However, different features such as selected. There are two types of relevantcontrast context histogram need to be studied and compositions: those compositions that occurapplied to accomplish hand posture recognition in frequently in all categories and also those which arereal time. specific for a category. The category posterior of In [25] Bag-of-Words representation (BoW) compositions is learned in the training phase, and itand SIFT features is used. In a typical BoW is a measure of relevance. The entropy of therepresentation, “interesting” local patches are first category posterior helps to discriminate betweenidentified from an image, either by densely categories. A cost function is obtained by combiningsampling, or by an interest point detector. These the priors of the prototypes and the entropy. Thelocal patches, represented by vectors in a high process of recognition is based on bag ofdimensional space, are often referred to as the key composition method, where a discriminativepoints. The bag-of-words methods main idea is to function is defined.quantize each extracted key point into one of the In [28] Maximally Stable Extremal Regionvisual words, and then represent each image by a (MSER) detector and color likelihood maps are usedhistogram of visual words. A clustering algorithm is for hand tracking. Such a combination allowsgenerally used to generate the visual words performing repeated figure/ground segmentation indictionary. In [25] K-means algorithm has been used every frame in an efficient manner.for clustering. A multi-class SVM was used to train The MSER detector is one of the best interest regionthe classifier model. In the testing stage, the detectors in computer vision [35]. MSER detectionkeypoints were extracted from every image captured is mostly applied to single gray scale images, but thefrom the webcam and fed into the cluster model to method can be easily extended for analysis of colormap them with one (Bag-of-words) vector, which is images by defining a suitable ordering relationshipfinally fed into the multi-class SVM training on the color pixels. In general the MSER detectorclassifier model to recognize the hand gesture. finds bright connected regions which have In [26] the ARPD descriptor (Appearance and consequently darker values along their boundaries.Relative Position Descriptor) is proposed. This The set of MSERs is closed under continuousdescriptor includes color histogram, relative- geometric transformations and is invariant to affineposition information, and SURF [33]. The process intensity changes. Furthermore MSERs are detectedof constructing ARPD includes two steps: extracting at all scales. Therefore, due to these propertiesSURF keypoints and color histogram from images, MSER detection is suited for segmentationand computing relative-position information of purposes.every keypoint within images, the relative-position In [29], [30], [31] Haar like features are usedinformation is also included as part of ARPD. The for the task of hand detection. Haar like featuresARPD was used in the BoW representation. focus more on the information within a certain areaThe BoW was used to detect and recognize hand of the image rather than each single pixel. Toposture based on sliding-window framework. To improve classification accuracy and achievemeet real-time request, several approaches were realtime performance, AdaBoost learning algorithmproposed to speed up hand posture recognition that can adaptively select the best features in eachprocess. In tracking process, CAMESHIFT step and combine them into a strong classifier canalgorithm to track hand motion and a strategy based be used. The training algorithm based on AdaBooston histogram to reinitialize tracking process were learning algorithm takes a set of “positive” samples,used. which contain the object of interest and a set of In [27] compositional techniques are used for “negative” samples, i.e., images that do not containhand posture recognition. A hand posture objects of interest.representation is based on compositions of parts: This invariant features allowed us to model thedescriptors are grouped according to the perceptual hand as collection of characteristic parts. Key pointslaws of grouping [34] obtain a set of possible or characteristic regions are extracted. Using suchcandidate compositions. These groups are a sparse features the hand gesture is decomposed in simplerrepresentation of the hand posture based on parts which are easier to recognize. This approachoverlapping subregions. has major advantages: even if some parts are The detected part descriptors are represented as missing a gestures still can be recognized, so thereprobability distributions over a codebook which is are robust to partials occlusions, changes in viewobtained in the learning phase. A composition is a point and considerable deformations. Bag of WordsISBN: 978-1-61804-062-6 184
  5. 5. Recent Researches in Circuits, Systems, Mechanics and Transportation Systemsmethods and compositional methods become more annotating and editing documents using pen-basedand more popular in hand gesture recognition. These gestures [41]. This year eyeSight introduced gesturetechniques have been studied in many diverse fields recognition Technology for Android Tablets andsuch as linguistics, logic, and neuroscience, but Windows-based Portable Computers [50].compositionality is especially evident in the syntax Sign Language: Sign language is anand semantics of language where a limited number important case of communicative gestures. Sinceof letter scan form a huge variety of words and sign languages are highly structural, they are verysentences. In computer vision these techniques are suitable as testbeds for vision algorithms [42]. Atused in the context of a general problem: the same time, they can also be a good way to helpcategorization. Using these techniques we address the disabled to interact with computers. Signalso to the semantic gap that exists between the low language for the deaf (e.g. American Signlevel features and high level representations. The Language) is an example that has receivedhand posture is no longer modeled as a whole. significant attention in the gesture literature [43, 44,These characteristic regions are assembled to form 45 and 46].compositions; these compositions at their turn can Vehicle interfaces: A number of handbe group in compositions of compositions and so gesture recognition techniques for human vehicleon. interface have been proposed time to time [47,48]. The primary motivation of research into the use of3 Application Areas hand gestures for in-vehicle secondary controls is There is a large variety of applications broadly based on the premise that taking the eyeswhich involves hand gestures. Hand gestures can be off the road to operate conventional secondaryused to achieve natural human computer interaction controls can be reduced by using hand gestures.for virtual environments, or there can be used to Healthcare: Wachs et al. [49] developed acommunicate with deaf and dumb. An important hand-gesture recognition system that enablesapplication area is that of vehicle interfaces. doctors to manipulate digital images during medical In this section an overview of few procedures using hand gestures instead of touchapplication areas is given. screens or computer keyboards. A sterile human- Virtual Reality: Gestures for virtual and machine interface is of supreme importance becauseaugmented reality applications have experienced it is the means by which the surgeon controlsone of the greatest levels of uptake interactions [36] medical information, avoiding patientor 2D displays that simulate 3D interactions [37]. contamination, the operating room and the other Robotics and Telepresence: When robots surgeons. The gesture based system could replaceare moved out of factories and introduced into our touch screens now used in many hospital operatingdaily lives they have to face many challenges such rooms which must be sealed to preventas cooperating with humans in complex and accumulation or spreading of contaminants anduncertain environments or maintaining long-term requires smooth surfaces that must be thoroughlyhuman-robot relationships. Telepresence and cleaned after each procedure – but sometimes arent.telerobotic applications are typically situated within With infection rates at hospitals now atthe domain of space exploration and military-based unacceptably high rates, the hand gestureresearch projects. recognition system offers a possible alternative.The gestures used to interact with and control robotsare similar to fully-immersed virtual reality 4 Conclusioninteractions, however the worlds are often real, In this paper a review of vision based handpresenting the operator with video feed from gesture recognition methods has been presented. Incameras located on the robot [38]. Here, gestures the last years remarkable progress in the field ofcan control a robots hand and arm movements to vision based hand gesture recognition has beenreach for and manipulate actual objects, as well its done. Further research in the areas of featuremovement through the world. extraction, classification methods and gestureHand gesture recognition for robotic control is representation are required to realize the ultimatepresented in [24, 39] goal of humans interfacing with machines on their Desktop and Tablet PC Applications: In own natural terms.desktop computing applications, gestures can It is obviously that the near future belongsprovide an alternative interaction to the mouse and to hand gesture recognition. Probably sooner thatkeyboard [40]. Many gestures for desktop one may think the surrounding devices will be handcomputing tasks involve manipulating graphics, or gesture interfaced.ISBN: 978-1-61804-062-6 185
  6. 6. Recent Researches in Circuits, Systems, Mechanics and Transportation Systems ACKNOWLEDGMENT hierarchical Bayesian filter. IEEE Transactions (1) This paper was supported by the project on Pattern Analysis and Machine Intelligence"Develop and support multidisciplinary postdoctoral (2006)programs in primordial technical areas of national [11] Jinshi Cui, Zengqi Sun, Model-based visualstrategy of the research - development - innovation" hand posture tracking for guiding a dexterous4D-POSTDOC, contract nr. POSDRU robotic hand, Optics Communications 235/89/1.5/S/52603, project co-funded from European (2004) 311–318Social Fund through Sectorial Operational Program [12] Bay M, Koller-Meier, Gool L.V., SmartHuman Resources 2007-2013. particle filtering for 3D hand tracking, in: Sixth (2) This work was supported by the national IEEE International Conference on Automaticgrant ID 931, contr. 651/19.01.2009. Face and Gesture Recognition, Los Alamitos, CA, USA, 2004, pp 675References: [13] Martin de La Gorce, Nikos Paragios, David J. [1] H. Zhou, T.S. Huang, Tracking articulated Fleet, Model-Based Hand Tracking with hand motion with Eigen dynamics analysis, In Texture, Shading and Self-occlusions, IEEE Proc. Of International Conference on Conference on Computer Vision and Pattern Computer Vision, Vol 2, 2003, pp. 1102-1109 Recognition, Alaska, 2008 [2] Bray M., Koller-Meier E., Gool L.V., Smart [14] Martin de La Gorce, David J. Fleet, and Nikos particle filtering for 3D hand tracking. Sixth Paragios, Model-Based 3D Hand Pose IEEE International Conference on Automatic Estimation from Monocular Video, IEEE Face and Gesture Recognition (2004): 675 Transactions On Pattern Analysis And [3] Bray M., Koller-Meier E., Muller P, Gool L.V., Machine Intelligence, 2011 Schraudolph N.N., 3D Hand tracking by rapid [15] Chutisant Kerdvibulvech, Hideo Saito, Model- stochastic gradient descent using a skinning Based Hand Tracking by Chamfer Distance and model.First European Conference on Visual Adaptive Color Learning Using Particle Filter Media Production (2004): 297-302 EURASIP Journal on Image and Video[4] Nirei K., Saito H., Mochimaru M., Ozawa S., Processing 2009 Human hand tracking from binocular image [16] New, J. R., Hasanbelliu, E. and Aguilar, M. sequences. In 22th International Conference on Facilitating User Interaction with Complex Industrial Electronics, Control, and Systems via Hand Gesture Recognition. In Instrumentation Proc of Southeastern ACM Conf., Savannah [5] Kuch J.J, Huang T.S , Human computer 2003 interaction via the human hand: a hand model,. [17] Yang M. H., Ahuja N., and Tabb M., Twenty-Eighty Asilomar Conference on Signal, “Extraction of 2-D Motion Trajectories and its Systems, and Computers (1994): 1252– 56 Application to Hand Gesture Recognition,” in [6] Rehg J., Kanade T., Visual tracking of high PAMI., 29(8) (2002) 1062–1074 DoF articulated structures: An application to [18] Mo Z., Lewis J.P., Neumann U., Smartcanvas: human hand tracking. In European Conference a gesture-driven intelligent drawing desk on Computer Vision and Image Understanding system, In 10th International Conference on (1994): 35–46 Intelligent User Interfaces, ACM Press (2005): [7] Ali Erol, George Bebis, Mircea Nicolescu, 239-43 Richard D. Boyle, Xander Twombly., Vision- [19] Martin J., Devin V. , Crowley J.L., Active hand based hand pose estimation: A review. tracking, 3rd. International Conference on Computer Vision and Image Understanding Face & Gesture Recognition, IEEE Computer 108 (2007), pp 52–73 Society (1998): 575 [8] Heap A. J., Hogg D. C., Towards 3-D hand [20] Kjeldsen R., Kender J., Toward the use of tracking using a deformable model. In 2nd gesture in traditional user interfaces, International Face and Gesture Recognition International Conference on Automatic Face Conference (1996), pp 140–45 and Gesture Recognition (1996): 151–56 [9] Stenger B. , Mendonc P. R. S., Cipolla R., [21] O’Hagan R.G., Zelinsky A., . Rougeaux S. Model-Based 3D Tracking of an Articulated Visual gesture interfaces for virtual Hand." Proc. British Machine Vision environments, Interacting with Computers 14 Conference 1 (2001): 63-72 (2002): 231–50 [10] Stenger B., Thayananthan A., Torr P.H.S., [22] Lars Bretzner, Ivan Laptev and Tony Cipolla R. Model-based hand tracking using a Lindeberg, Hand gesture recognition usingISBN: 978-1-61804-062-6 186
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