The goal of this work is the identification of humans based on motion data in the form of natural hand gestures. In this paper, the identification problem is formulated as classification with classes corresponding to persons' identities, based on recorded signals of performed gestures. The identification performance is examined with a database of twenty-two natural hand gestures recorded with two types of hardware and three state-of-art classifiers: Linear Discrimination Analysis (LDA), Support Vector machines (SVM) and k-Nearest Neighbour (k-NN). Results show that natural hand gestures allow for an effective human classification.
Relatively new application of HCI elements are biometric technologies aimed to recognise a person based on their physiological or behavioural characteristic. A survey of behavioural biometrics is provided in @cite_16 where authors examine types of features used to describe human behaviour as well as compare accuracy rates for verification of users using different behavioural biometric approaches. Simple gesture recognition may be applied for authentication on mobile devices e.g. in @cite_19 authors present a study of light-weight user authentication system using an accelerometer while a multi-touch gesture-based authentication system is presented in @cite_28 . Typically however, instead of hand motion more reliable features like hand layout @cite_8 or body gait @cite_35 are employed.
@cite_35 @cite_8 @cite_28 @cite_19 @cite_16
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Human gait is an attractive modality for recognizing people at a distance. In this paper we adopt an appearance-based approach to the problem of gait recognition. The width of the outer contour of the binarized silhouette of a walking person is chosen as the basic image feature. Different gait features are extracted from the width vector such as the dowsampled, smoothed width vectors, the velocity profile etc. and sequences of such temporally ordered feature vectors are used for representing a person's gait. We use the dynamic time-warping (DTW) approach for matching so that non-linear time normalization may be used to deal with the naturally-occuring changes in walking speed. The performance of the proposed method is tested using different gait databases. In this paper, we present a novel multi-touch gesture-based authentication technique. We take advantage of the multi-touch surface to combine biometric techniques with gestural input. We defined a comprehensive set of five-finger touch gestures, based upon classifying movement characteristics of the center of the palm and fingertips, and tested them in a user study combining biometric data collection with usability questions. Using pattern recognition techniques, we built a classifier to recognize unique biometric gesture characteristics of an individual. We achieved a 90 accuracy rate with single gestures, and saw significant improvement when multiple gestures were performed in sequence. We found user ratings of a gestures desirable characteristics (ease, pleasure, excitement) correlated with a gestures actual biometric recognition rate - that is to say, user ratings aligned well with gestural security, in contrast to typical text-based passwords. Based on these results, we conclude that multi-touch gestures show great promise as an authentication mechanism. The proliferation of accelerometers on consumer electronics has brought an opportunity for interaction based on gestures or physical manipulation of the devices. We present uWave, an efficient recognition algorithm for such interaction using a single three-axis accelerometer. Unlike statistical methods, uWave requires a single training sample for each gesture pattern and allows users to employ personalized gestures and physical manipulations. We evaluate uWave using a large gesture library with over 4000 samples collected from eight users over an elongated period of time for a gesture vocabulary with eight gesture patterns identified by a Nokia research. It shows that uWave achieves 98.6 accuracy, competitive with statistical methods that require significantly more training samples. Our evaluation data set is the largest and most extensive in published studies, to the best of our knowledge. We also present applications of uWave in gesture-based user authentication and interaction with three-dimensional mobile user interfaces using user created gestures. This study is a survey and classification of the state-of-the-art in behavioural biometrics which is based on skills, style, preference, knowledge, motor-skills or strategy used by people while accomplishing different everyday tasks such as driving an automobile, talking on the phone or using a computer. The authors examine current research in the field and analyse the types of features used to describe different types of behaviour. After comparing accuracy rates for verification of users using different behavioural biometric approaches, researchers address privacy issues which arise or might arise in the future with the use of behavioural biometrics.