Georgios Stavropoulos

Gait Recognition Using Geometric Features and Soft Biometrics

This letter presents a novel framework for gait recognition augmented with soft biometric information. Geometric gait analysis is based on Radon transforms and on gait energy images. User height and stride length information is extracted and utilized in a probabilistic framework for the detection of soft biometric features of substantial discrimination power. Experimental validation illustrates that the proposed approach for integrating soft biometric features in gait recognition advances significantly the identification and authentication performance.

3-D Model Search and Retrieval From Range Images Using Salient Features

This paper presents a novel framework for partial matching and retrieval of 3-D models based on a query-by-range-image approach. Initially, salient features are extracted for both the query range image and the 3-D target model. The concept behind the proposed algorithm is that, for a 3-D object and a corresponding query range image, there should be a virtual camera with such intrinsic and extrinsic parameters that would generate an optimum range image, in terms of minimizing an error function that takes into account the salient features of the objects, when compared to other parameter sets or other target 3-D models. In the context of the developed framework, a novel method is also proposed to hierarchically search in the parameter space for the optimum solution. Experimental results illustrate the efficiency of the proposed approach even in the presence of noise or occlusion.

Recognizing Daily Activities in Realistic Environments Through Depth-Based User Tracking and Hidden Conditional Random Fields for MCI/AD Support

This paper presents a novel framework for the automatic recognition of Activities of Daily Living (ADLs), such as cooking, eating, dishwashing and watching TV, based on depth video processing and Hidden Conditional Random Fields (HCRFs). Depth video is provided by low-cost RGB-D sensors unobtrusively installed in the house. The user’s location, posture, as well as point cloud -based features related to gestures are extracted; a standing/sitting posture detector, as well as novel features expressing head and hand gestures are introduced herein. To model the target activities, we employed discriminative HCRFs and compared them to HMMs. Through experimental evaluation, HCRFs outperformed HMMs in location trajectories-based ADL detection. By fusing trajectories data with posture and the proposed gesture features, ADL detection performance was found to further improve, leading to recognition rates at the level of 90.5 % for five target activities in a naturalistic home environment.

ANTIPROLIFERATIVE ACTIVITY OF SYNTHETIC TETRAPEPTIDES, ANALOGS OF AS-I PHYTOTOXIN, TOWARDS CANCER CELL LINES

The in vitro chemosensitivity of three cancer cell lines [HT29 (colon), HeLa (cervical) and T47D (breast)] to eight synthetic tetrapeptides, analogs of AS-I toxin, with phytotoxic effect on a series of plants was studied. Mouse fibroblast L929 cell line was also tested for chemosensitivity to these peptides. All cell lines were especially sensitive to Cys-Val-Gly-Glu tetrapeptide with IC50 values of 0.18, 0.3 and 0.63 mM for HT29, HeLa and T47D cells, respectively, whereas the IC50 value for the L929 cells was higher than 1 mM. Antiproliferative activity was also observed with peptides Tyr-Val-Gly-Glu and His-Val-Gly-Glu with IC50 values higher than those obtained for Cys-Val-Gly-Glu. For the rest of the peptides tested the IC50 values were found close to or higher than 3 mM.

A building performance evaluation & visualization system

A novel big data building performance evaluation knowledge processing and mining system utilizing visual analytics is going to be presented in this paper. A large dataset comprised of building information, energy consumption, environmental measurements, human presence and behavior and business processes is going to be exploited for the building performance evaluation. Building performance evaluation is one of the most important factors in engineering that leads to building renovation and construction with low energy consumption and gas emissions in conjunction with comfort, utility and durability. For this purpose, business processes occurring in the building are correlated with the energy consumption and the human flows in the spatiotemporal domain modeling the dynamic behavior of the building. These models lead to the extraction of useful semantic information and the detection of spatiotemporal patterns that are important for the evaluation of the building performance. Furthermore, a number of novel visual analytics techniques allow the end-users to process data in different temporal resolutions and with different temporal filters, assisting them to detect patterns that may be difficult to be detected otherwise. The proposed visual analytics techniques support design and energy management decisions by visualizing the building measurements regarding business and comfort aspects. To do so, the proposed system includes a variety of techniques and components, properly selected to offer quick identification of focal points and evaluation of the building performance. Considering the increasing interest and the green building goals of almost all world governments including EU, the suggested methodology and application could be rendered a very useful tool for the Architecture and Engineering Community working on Building Performance Simulation and Analysis, and all related communities in Architect, Engineering and Construction (AEC) industry.

Biometric Keys for the Encryption of Multimodal Signatures

Biometrics have long been used as means to recognize people, mainly in terms of their physiological characteristics, for various commercial applications ranging from surveillance and access control against potential impostors to smart interfaces (Qazi (2004)) (Xiao (2005)). These systems require reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The biometric methods, that are usually incorporated in such systems, can be categorized to physiological and behavioral (Jain et al. (2004)), depending on the type of used features. The most popular physiological biometric traits are the fingerprint (Maltoni et al. (2009)) that is widely used in law enforcement for identifying criminals, the face (Chang et al. (2005)) and the iris (Sun & Tan (2009)). However, despite their high recognition performance, static biometrics have been recently overwhelmed by the new generation of biometrics, which tend to cast light on more natural ways for recognizing people by analyzing behavioural traits. Specifically, behavioural biometrics are related to specific actions and the way that each person executes them. In other words, they aim at recognizing livingness, as it is expressed by dynamic traits. The most indicative cases of behavioural biometric recognition is gait (Goffredo et al. (2009b)), facial expressions (Liu & Chen (2003)) or other activity related, habitual traits (Drosou, Ioannidis, Moustakas & Tzovaras (2010)). As a result behavioural biometrics have become much more attractive to researchers due to their significant recognition potential and their unobtrusive nature. They can potentially allow the continuous (on-the-move) authentication or even identification unobtrusively to the subject and become part of an Ambient Intelligence (AmI) environment. The inferior performance of behavioural biometrics, when compared to the classic physiological ones, can be compensated when they are combined in a multimodal biometric system. In general, multimodal systems are considered to provide an excellent solution to a series of recognition problems. Unimodal systems are more vulnerable to theft attempts, since an attacker can easily gain access by stealing or bypassing a single biometric feature. In the same concept, they have to contend with a variety of problems, such as noisy data, intraclass variations, restricted degrees of freedom, non-universality, spoof attacks, and unacceptable error rates, i.e., it is estimated that approximately 3% of the population does not have legible 8

Multiple object tracking based on motion segmentation of point trajectories

In this paper we propose an algorithm for multiple object tracking, a heavily researched but still challenging problem of computer vision. We follow the tracking by detection paradigm in an online fashion and formulate tracking as a typical assignment problem between detections and existing tracks that is solved by a modification of the Hungarian algorithm. Contrary to other methods that use a multitude of features based on appearance, optical flow and prior knowledge gained through training, we solely use clusters of point trajectories to link detections and tracks. Point trajectories are robust under partial occlusions and allow the expansion of a track even in the absence of a detection. At the core of our algorithm lies a motion segmentation method that extracts coherent clusters from triangulated point trajectories. Our algorithm achieves competitive results on the 2D MOT 2015 benchmark showcasing its potential.

Modeling people with motor disabilities to empower the automatic accessibility and ergonomic assessment of new products

Virtual User Models (VUMs) can be a valuable tool for accessibility and ergonomic evaluation of designs in simulation environments. As increasing the accessibility of a design is usually translated into additional costs and increased development time, the need for specifying the percentage of population for which the design will be accessible is crucial. This paper addresses the development of VUMs representing specific groups of people with disabilities. In order to create such VUMs, we need to know the functional limitations, i.e. disability parameters, caused by each disability and their variability over the population. Measurements were obtained from 90 subjects with motor disabilities and were analyzed using both parametric and nonparametric regression methods as well as a proposed hybrid regression method able to handle small sample sizes. Validation results showed that in most cases the proposed regression analysis can produce valid estimations on the variability of each disability parameter.

Protrusion Fields for 3D Model Search and Retrieval Based on Range Image Queries

This paper presents a novel framework for 3D object search and retrieval based on a query-by-range-image approach. Initially, salient features are extracted for both the query range image and the 3D target model that is followed by the estimation of the protrusion field generated by the extracted salient points of the 3D objects. Then, based on the concept that for a 3D object and a corresponding query range image, there should be a virtual camera with such intrinsic and extrinsic parameters that would generate an optimum range image, in terms of minimizing an error function that takes into account the protrusion field of the objects, when compared to other parameter sets or other target 3D models, matching is performed via estimating dissimilarity within the protrusion field. Experimental results illustrate the efficiency of the proposed approach even in the presence of noise or occlusion.

Corrections to “Gait Recognition Using Geometric Features and Soft Biometrics” [Apr 10 367-370]

In the above letter (ibid., vol. 17, no. 4, pp. 367-370, Apr. 10), equation (5) requires modification. The revised equation is presented here.