Dimosthenis Ioannidis

Gait Recognition Using Compact Feature Extraction Transforms and Depth Information

This paper proposes an innovative gait identification and authentication method based on the use of novel 2-D and 3-D features. Depth-related data are assigned to the binary image silhouette sequences using two new transforms: the 3-D radial silhouette distribution transform and the 3-D geodesic silhouette distribution transform. Furthermore, the use of a genetic algorithm is presented for fusing information from different feature extractors. Specifically, three new feature extraction techniques are proposed: the two of them are based on the generalized radon transform, namely the radial integration transform and the circular integration transform, and the third is based on the weighted Krawtchouk moments. Extensive experiments carried out on USF ldquoGait Challengerdquo and proprietary HUMABIO gait database demonstrate the validity of the proposed scheme.

A Channel Coding Approach for Human Authentication From Gait Sequences

Human authentication using biometric traits has become an increasingly important issue in a large range of applications. In this paper, a novel channel coding approach for biometric authentication based on distributed source coding principles is proposed. Biometric recognition is formulated as a channel coding problem with noisy side information at the decoder and error correcting codes are employed for user verification. It is shown that the effective exploitation of the noise channel distribution in the decoding process improves performance. Moreover, the proposed method increases the security of the stored biometric templates. As a case study, the proposed framework is employed for the development of a novel gait recognition system based on the extraction of depth data from human silhouettes and a set of discriminative features. Specifically, gait sequences are represented using the radial and the circular integration transforms and features based on weighted Krawtchouk moments. Analytical models are derived for the effective modeling of the correlation channel statistics based on these features and integrated in the soft decoding process of the channel decoder. The experimental results demonstrate the validity of the proposed method over state-of-the-art techniques for gait recognition.

Spatiotemporal analysis of human activities for biometric authentication

This paper presents a novel framework for unobtrusive biometric authentication based on the spatiotemporal analysis of human activities. Initially, the subjects actions that are recorded by a stereoscopic camera, are detected utilizing motion history images. Then, two novel unobtrusive biometric traits are proposed, namely the static anthropometric profile that accurately encodes the inter-subject variability with respect to human body dimensions, while the activity related trait that is based on dynamic motion trajectories encodes the behavioral inter-subject variability for performing a specific action. Subsequently, score level fusion is performed via support vector machines. Finally, an ergonomics-based quality indicator is introduced for the evaluation of the authentication potential for a specific trial. Experimental validation on data from two different datasets, illustrates the significant biometric authentication potential of the proposed framework in realistic scenarios, whereby the user is unobtrusively observed, while the use of the static anthropometric profile is seen to significantly improve performance with respect to state-of-the-art approaches.

Gait Identification using the 3D Protrusion Transform

The present paper presents a novel approach for gait identification using 3D data and Krawtchouk moments to generate the descriptor feature vectors. The gait sequence is captured by a stereoscopic camera and the resulting 2.5D data are processed to generate a 3D hull of the captured silhouette. The 3D Protrusion Transform is then proposed that generates a silhouette image containing protrusion information. Finally, the descriptor vector of the extended silhouette is calculated using the Krawtchouk moments. Experimental evaluation illustrates that the proposed scheme is highly efficient in identifying gait sequences when compared to state of the art approaches.

Biometric template protection in multimodal authentication systems based on error correcting codes

The widespread deployment of biometric systems has raised public concern about security and privacy of personal data. In this paper, we present a novel framework for biometric template security in multimodal biometric authentication systems based on error correcting codes. Biometric recognition is formulated as a channel coding problem with noisy side information at the decoder based on distributed source coding principles. It is shown that the proposed method binds the biometric template in a cryptographic key which does not reveal any information about the original biometric data even if it is compromised by an attacker. Furthermore, the advantages of the proposed method in terms of security and impact on matching accuracy are discussed. We assess the performance of the proposed method in the context of HUMABIO, an EU Specific Targeted Research Project, where face and gait biometrics are employed in an unobtrusive application scenario for human authentication. Experimental evaluation on a multimodal biometric database demonstrates the validity of the proposed method.

Occupancy driven building performance assessment

Abstract In this paper, we focus on the building performance assessment using big data and visual analytics techniques driven by building occupancy. Building occupancy is a paramount factor in building performance, specifically lighting, plug loads and HVAC equipment utilization. Extrapolation of patterns from big data sets, which consist of building information, energy consumption, environmental measurements and namely occupancy information, is a powerful analysis technique to extract useful semantic information about building performance. To this end, visual analytics techniques are exploited to visualize them in a compact and comprehensive way taking into account properties of human cognition, perception and sense making. Visual Analytics facilitates the detailed spatiotemporal analysis building performance in terms of occupancy comfort, building performance and energy consumption and exploits innovative data mining techniques and mechanisms to allow analysts to detect patterns and crucial point that are difficult to be detected otherwise, thus assisting them to further optimize the building’s operation. The presented tool has been tested on real data information acquired from a building located at southern Europe demonstrating its effectiveness and its usability for building managers.

Gait and Anthropometric Profile Biometrics: A Step Forward

Emerging biometrics based on the measurements of body dynamic and static characteristics have gained increased importance in all the surveillance environments where the security is a mandatory priority. Some technology branches are involved to find unobtrusive solutions for authentication systems, where the human subject should not take care of the system itself so that he/she is free to perform his/her normal actions. In the first part of the chapter a novel gait recognition system is presented that introduces the use of range data for gait signal analysis. In the second part of the chapter, a description of system based on a sensing seat for event-related continuous authentication purpose in office and car scenarios is presented. Both biometric technologies introduce new means of verifying the user identity, by exploiting the analysis of common and every-day activities recorded in an unobtrusive manner and their recognition accuracy has been seen to be very high in the performed experiments.

Robust malfunction diagnosis in process industry time series

In this work, a modified version of a Slope Statistic Profile (SSP) method is proposed, capable to detect real-time incidents that occur in two interdependent time series. The estimation of incident time point is based on the combination of their linear trend profiles test statistics, computed on a consecutive overlapping data window. Furthermore, the proposed method uses a self-adaptive sliding data window. The adaptation of the size of the sliding data window is based on real-time classification of the linear trend profiles in constant and equal time intervals, according to two different linear trend scenarios, suitably adjusted to the conditions of the problem we face. The proposed method is used for the robust identification of a malfunction and it is demonstrated to real datasets from a chemical process pilot plant that is situated at the premises of CERTH / CPERI during the evolution of the performed experiments at the process unit.

Activity related authentication using prehension biometrics

This paper presents an extensive study on prehension-based dynamic features and their use for biometric purposes. The term prehension describes the combined movement of reaching, grasping and manipulating objects. The motivation behind the proposed study derives from both previous works related to the human physiology and human motion, as well as from the intuitive assumption that different body types and different characters would produce distinguishable, and thus valuable for biometric verification, activity-related traits. A novel approach for analyzing such movements is presented herein, based on the generation of an activity related manifold, the Activity hyper-Surface. The authentication capacity of the extracted features on the activity hyper-surface is evaluated in terms of their relative entropy and their mutual information within a complete framework targeting user verification. Experimental results on two datasets of 29 real subjects each and a third one of 100 virtual subjects show that the introduced concept constitutes a promising approach in the field of biometric recognition. HighlightsWe justify the validity of a prehension movement as biometric trait.We propose Activity hyper-Surfaces as descriptors for the arm movements.We analyze and combine Activity Curves of finger movements for completeness.Relative Entropy & Mutual Information based algorithm for dimensionality reduction.We evaluate our system on two real medium sized datasets and a large synthetic one.

Human tracking & visual spatio-temporal statistical analysis

In this work, a novel, multi-space, real-time and robust human tracking system is going to be presented. The system exploits a multi-camera network monitoring the multi-space dynamic environment under interest, detecting and tracking the humans in it. The system is able to handle the dynamic changes of the environment, as well as partial occlusions utilizing virtual top cameras. Furthermore, the system is able to real-time visualize the detection and tracking results on the architectural map of the dynamic environment, as well as a variety of statistics. The visual spatio-temporal analysis of the tracked data are presented in a consolidated form for the overall monitoring area and analytically for each space separately and for each tracked human. These statistics could be also combined with the energy consumption in the area, as well as with other environmental data providing semantic information such as comfort. The overall system is equipped with a number of visual interactive tools providing real-time spatio-temporal human presence analysis offering to the user the opportunity to capture and isolate the areas/spaces with high human presence, the days and times of high human presence, to correlate this information with the potential energy consumption and indicators such as comfort.