As computers become more pervasive in society, facilitating natural human---computer interaction (HCI) will have a positive impact on their use. Hence, there has been growing interest in the development of new approaches and technologies for bridging the human---computer barrier. The ultimate aim is to bring HCI to a regime where interactions with computers will be as natural as an interaction between humans, and to this end, incorporating gestures in HCI is an important research area. Gestures have long been considered as an interaction technique that can potentially deliver more natural, creative and intuitive methods for communicating with our computers. This paper provides an analysis of comparative surveys done in this area. The use of hand gestures as a natural interface serves as a motivating force for research in gesture taxonomies, its representations and recognition techniques, software platforms and frameworks which is discussed briefly in this paper. It focuses on the three main phases of hand gesture recognition i.e. detection, tracking and recognition. Different application which employs hand gestures for efficient interaction has been discussed under core and advanced application domains. This paper also provides an analysis of existing literature related to gesture recognition systems for human computer interaction by categorizing it under different key parameters. It further discusses the advances that are needed to further improvise the present hand gesture recognition systems for future perspective that can be widely used for efficient human computer interaction. The main goal of this survey is to provide researchers in the field of gesture based HCI with a summary of progress achieved to date and to help identify areas where further research is needed.

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Vision based hand gesture recognition for human computer interaction: a survey

Recent methods and databases in vision-based hand gesture recognition

A system for the detection, segmentation and recognition of multi-class hand postures against complex natural backgrounds is presented. Visual attention, which is the cognitive process of selectively concentrating on a region of interest in the visual field, helps human to recognize objects in cluttered natural scenes. The proposed system utilizes a Bayesian model of visual attention to generate a saliency map, and to detect and identify the hand region. Feature based visual attention is implemented using a combination of high level (shape, texture) and low level (color) image features. The shape and texture features are extracted from a skin similarity map, using a computational model of the ventral stream of visual cortex. The skin similarity map, which represents the similarity of each pixel to the human skin color in HSI color space, enhanced the edges and shapes within the skin colored regions. The color features used are the discretized chrominance components in HSI, YCbCr color spaces, and the similarity to skin map. The hand postures are classified using the shape and texture features, with a support vector machines classifier. A new 10 class complex background hand posture dataset namely NUS hand posture dataset-II is developed for testing the proposed algorithm (40 subjects, different ethnicities, various hand sizes, 2750 hand postures and 2000 background images). The algorithm is tested for hand detection and hand posture recognition using 10 fold cross-validation. The experimental results show that the algorithm has a person independent performance, and is reliable against variations in hand sizes and complex backgrounds. The algorithm provided a recognition rate of 94.36 %. A comparison of the proposed algorithm with other existing methods evidences its better performance.

Attention Based Detection and Recognition of Hand Postures Against Complex Backgrounds

ICIP is the premier international forum for the technological advances and research results in the fields of theoretical, experimental, and applied image and video processing. The series is sponsored by the IEEE Signal Processing Society and has been held annually since 1994. Research frontiers in fields ranging from traditional image processing applications to evolving multimedia and video technologies are regularly advanced by results first reported in ICIP technical sessions. Topics include, but are not limited to: Procedure 1. Send notice of intent to bid to the Vice President – Conferences and SPS Conference Services staff at sps-conf-proposals@ieee.org. Include in the notice your contact information and the proposed dates and location. 2. Conference Services staff will issue the proposal submission forms upon receipt of the letter of intent. 3. The forms must be completed and the proposal submitted to the Conference Services staff at least three months prior to the next ICIP meeting. See below for more information on the Proposal Timeline. 4. The receipt of the proposal may prompt an invitation to present from the IVMSP Technical Committee Chair to the Committee. 5. The IVMSP Technical Committee will review the proposal, and if it so chooses, will endorse the proposal and forward it to the Conference Board. 6. The Vice President – Conferences may issue an invitation to present to the Conference Board. 7. The Conference Board, if it so chooses, will endorse the proposal and forward it to the Board of Governors for final approval.

IEEE International Conference on Image Processing (ICIP)

Motion recognition is a topic in software engineering and dialect innovation with a goal of interpreting human signals through mathematical algorithm. Hand gesture is a strategy for nonverbal communication for individuals as it expresses more liberally than body parts. Hand gesture acknowledgment has more prominent significance in planning a proficient human computer interaction framework, utilizing signals as a characteristic interface favorable to circumstance of movements. Regardless, the distinguishing proof and acknowledgment of posture, gait, proxemics and human behaviors is furthermore the subject of motion to appreciate human nonverbal communication, thus building a richer bridge between machines and humans than primitive text user interfaces or even graphical user interfaces, which still limits the majority of input to electronics gadget. In this paper, a study on various motion recognition methodologies is given specific accentuation on available motions. A survey on hand posture and gesture is clarified with a detailed comparative analysis of hidden Markov model approach with other classifier techniques. Difficulties and future investigation bearing are also examined.

Hand posture and gesture recognition techniques for virtual reality applications: a survey

We present a novel eigenspace-based framework to model a dynamic hand gesture that incorporates both hand shape as well as trajectory information. We address the problem of choosing a gesture set that models an upper bound on gesture recognition efficiency. We show encouraging experimental results on a such a representative set.

https://www.cse.iitd.ac.in/~sumantra/publications/prl07_gesture.pdf

Hand gesture modelling and recognition involving changing shapes and trajectories, using a Predictive EigenTracker

This paper presents a robust shape-based on-line tracker for simultaneously tracking the motion of both hands, that is robust to cases of background clutter, other moving objects, occlusions of one hand by the other and a wide range of illumination variations. The tracker is based on an online predictive eigentracking framework. This framework allows efficient tracking of articulate objects, which change in appearance across views. We show results of successful tracking across all possible cases of motion dynamics of both hands during occlusion and a wide range of illumination conditions.

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Robust shape based two hand tracker

We present a novel predictive statistical framework to improve the performance of an eigentracker. In addition, we use fast and efficient eigenspace updates to learn new views of the object being tracked on the fly. We also incorporate a new importance sampling mechanism which increases the robustness of the eigentracker and enables it to track nonconvex objects better. Our eigentracker is flexible-it is possible to use it symbolically with other trackers. We show its successful application in hand gesture analysis; and face and person tracking.

/pdf/on-line-predictive-appearance-based-tracking-pd6uzr3lwk.pdf

On line predictive appearance-based tracking

In this paper we present a novel framework to model a dynamic hand gesture by k-dimensional vector that incorporates both the hand shape as well as the trajectory information. We introduce the notion of ‘distance’ between dynamic gestures to help choose a proper set of gestures for the gesture vocabulary. We also utilise inter-gesture distances for gesture recognition. We show encouraging results on a representative set of gestures selected according to the above criteria.

/pdf/dynamic-hand-gesture-recognition-using-predictive-eigen-wy91ds74d6.pdf

Dynamic Hand Gesture Recognition Using Predictive Eigen Tracker.

Tracking highly articulated 3-D objects such as human hands with an uncalibrated camera is not an easy task. In addition to changes in position, such objects change their shape and appearance with time. We present a robust tracker for such cases which works well in spite of cases of other similar moving objects, and background clutter. Our hand gesture analysis system is based on such a tracker. The system can recognise gestures involving the same hand shapes following different trajectories, and vice versa.

/pdf/modelling-and-recognising-spatio-temporal-hand-gestures-with-2opnmy3tzn.pdf

Kaustubh Srikrishna Patwardhan

Papers

Hand gesture modelling and recognition involving changing shapes and trajectories, using a Predictive EigenTracker

Robust shape based two hand tracker

On line predictive appearance-based tracking

Dynamic Hand Gesture Recognition Using Predictive Eigen Tracker.

Modelling and recognising spatio-temporal hand gestures with an uncalibrated camera