Can Biological Motion be a Biometric? (original) (raw)
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Analysis of Human Gait for Designing a Recognition and Classification System
Intelligent Innovations in Multimedia Data Engineering and Management, 2019
Identification and recognition of a human subject by monitoring a video/image by using various biometric features such as fingerprints, retina/iris scans, palm prints have been of interest to researches. In this chapter, an attempt has been made to recognize a human subject uniquely by monitoring his/her gait. This has been done by analyzing sampled frames of a video sequence to first detect the presence of a human form and then extract the silhouette of the subject in question. The extracted silhouette is then used to find the skeleton from it. The skeleton contains a set of points that retains the connectivity of the form and maintains the geometric properties of the silhouette. From the skeleton, a novel method has been proposed involving the neighborhood of interest pixels to identify the end points representing the heel, toe, etc. These points finally lead to the calculation of gait attributes. The extracted attributes represented in the form of a pattern vector are matched usi...
Automatic recognition by gait: progress and prospects
Sensor Review, 2003
Recognising people by their gait is a biometric of increasing interest. Recently, analysis has progressed from evaluation by few techniques on small databases with encouraging results to large databases and still with encouraging results. The potential of gait as a biometric was encouraged by the considerable amount of evidence available, especially in biomechanics and literature. This potential motivated the development of new databases, new technique and more rigorous evaluation procedures. We adumbrate some of the new techniques we have developed and their evaluation to gain insight into the potential for gait as a biometric. In particular, we consider implications for the future. Our work, as with others, continues to provide encouraging results for gait as a biometric, let alone as a human identi er, with a special regard for recognition at a distance.
Biometrics System based on Human Gait Patterns
International Journal of Machine Learning and Computing, 2011
Today's commercially available biometric systems show good reliability. However, they generally lack user acceptance. In general, people favour systems with the least amount of interaction. Using gait as a biometric feature would lessen such problems since it requires no subject interaction other than walking by. Consequently, this would increase user acceptance. And since highly motivated users achieve higher recognition scores, it increases the overall recognition rate as well. The latest research on gait-based identification-identification by observation of a person's walking style provides evidence that such a system is realistic and is likely to be developed and used in the years to come. This article outlines the application of gait technologies for security and other purposes. Gait analysis and recognition can form the basis of unobtrusive technologies for the detection of individuals who represent a security threat or behave suspiciously.
Gait recognition methods in the task of biometric human identification, 2023
This article focuses on defining the problem of solving the problem of human identification by means of gait recognition in biometric identification systems. In order to determine the prospects of using gait recognition methods for human identification, a generalized model of a biometric identification system was considered, the main modules of the system were identified and a brief description of each module was provided. Next, the basic requirements for human biometric features were identified, the main biometric features were considered, and the features of their use in biometric identification systems were determined. The issue of using gait as a biometric identifier was also considered. It has been determined that the use of human gait allows to get rid of two main obstacles in the construction of biometric identification systems: users are not required to provide personal biometric information in advance, and the system does not require specialized equipment. Also, the issue of multi-view gait recognition was considered. Multi-view gait recognition involves capturing gait data from different angles and using this data to improve recognition accuracy. This approach has shown great promise in challenging scenarios such as low lighting conditions. Next, we analyzed scientific works in the field of gait recognition. It was determined that gait recognition methods can be divided into template-based and non-template-based methods. Template-based methods are aimed at obtaining patterns of torso or leg movements, i.e. they usually focus on the dynamics of movement in space or on spatio-temporal methods. Non-template-based methods consider shape and its features as more relevant characteristics, i.e., human recognition are performed using measurements that reflect the shape of the person. Next, we consider the use of different datasets in the process of training and testing human gait recognition methods. The main datasets were identified and their characteristics and features were collected. We considered the presence of various characteristics in the datasets, as well as the means of representing information about human gait. The research has identified the main problems and challenges facing researchers in this area, as well as the main trends in the field of human gait recognition in biometric identification systems.
The Use of Body Motion Analysis as an Artificial Neural Network Method for Personal Identification
There is limited evidence to suggest an optimal biometric method in order to achieve an enhanced level of information security as well as recognition accuracy. Recently, novel approaches for the development of practical biometric identification systems have shown that body motion analysis seems to overcome most of the risks and vulnerabilities related to security and privacy and also characterized by simplicity and precision. This study examined the ability of a body motion analysis system to accurately identify individuals throughout specific periods of time. Specifically, thirteen males have performed three trials throughout a single day as well as pre and post an eight-week period. A high speed video camera was used to collect recordings of a full stride (two consecutive steps). Analysis of the video data was performed using a digitizing hardware system. After video analysis, various kinematic variables related to foot motion (total time, stride rate, stride length, flying time, contact time, velocity) were compared in order to measure body motion analysis' recognition efficiency. These kinematic variables are the inputs for a classical artificial neural network (ANN), which is used for the person's recognition. The output is the identity of the person. The ANN's is optimized regarding the values of crucial parameters such as the number of neurons, the time parameter and the initial value of the learning rate, etc. using the evaluation set. The evaluation criterion is the successful percentage of the person's identification. Statistics showed that trials' variations throughout day and the eight-week period for most kinematic variables were small, indicating high data reproducibility. The respective initial ANN results are encouraging and indicate an increased efficiency of body motion analysis on personal identification. In future, more measurement-trials per person during the reference period time and a larger participant sample may allow the results' generalization. It is also suggested that in order to obtain fast and accurate biometric identification even after a relatively long period of time, one may prefer body motion analysis over other biometric methods.
Using Gait to Recognize People
EUROCON 2005 - The International Conference on "Computer as a Tool", 2005
In modern society, the need for the establishment of defense and prevention mechanisms has encouraged the development of automatic human recognition systems based on biometrics, i.e. the analysis of a person's psychological and behavioral features. Gait, or the peculiar manner of walking, allows the recognition process to be made at a distance since it is possible to extract the gait information from a video sequence of a distant person walking. This paper proposes a gait recognition algorithm based on the averaged silhouette of a person over a gait cycle. A binary silhouette of the walking person is obtained by background subtraction; the binary silhouettes are then aligned and averaged over each gait period. The Euclidean distance between the averaged silhouettes of a number of persons is used for recognition purposes. Experimental results, using both lateral and oblique views, show very promising recognition rates.
Person identification from biological motion: Effects of structural and kinematic cues
2005
Abstract Human observers are able to identify a person based on his or her gait. However, little is known about the underlying mechanisms and the kind of information used to accomplish such a task. In this study, participants learned to discriminate seven male walkers shown as point-light displays from frontal, half-profile, or profile view. The displays were gradually normalized with respect to size, shape, and walking frequency, and identification performance was measured.
Kinematic cues for person identification from biological motion
2007
Abstract We examined the role of kinematic information for person identification. Observers learned to name seven walkers shown as point-light displays that were normalized by their size, shape, and gait frequency under a frontal, half-profile, or profile view. In two experiments, we analyzed the impact of individual harmonics as created by a Fourier analysis of a walking pattern, as well as the relative importance of the amplitude and the phase spectra in walkers shown from different viewpoints.
Performance prediction for individual recognition by gait
Pattern Recognition Letters, 2005
Existing gait recognition approaches do not give their theoretical or experimental performance predictions. Therefore, the discriminating power of gait as a feature for human recognition cannot be evaluated. In this paper, we first propose a kinematic-based approach to recognize human by gait. The proposed approach estimates 3D human walking parameters by performing a least squares fit of the 3D kinematic model to the 2D silhouette extracted from a monocular image sequence. Next, a Bayesian-based statistical analysis is performed to evaluate the discriminating power of extracted stationary gait features. Through probabilistic simulation, we not only predict the probability of correct recognition (PCR) with regard to different within-class feature variance, but also obtain the upper bound on PCR with regard to different human silhouette resolution. In addition, the maximum number of people in a database is obtained given the allowable error rate. This is extremely important for gait recognition in large databases.