Slobodan Ribaric

A biometric identification system based on eigenpalm and eigenfinger features

This paper presents a multimodal biometric identification system based on the features of the human hand. We describe a new biometric approach to personal identification using eigenfinger and eigenpalm features, with fusion applied at the matching-score level. The identification process can be divided into the following phases: capturing the image; preprocessing; extracting and normalizing the palm and strip-like finger subimages; extracting the eigenpalm and eigenfinger features based on the K-L transform; matching and fusion; and, finally, a decision based on the (k, l)-NN classifier and thresholding. The system was tested on a database of 237 people (1,820 hand images). The experimental results showed the effectiveness of the system in terms of the recognition rate (100 percent), the equal error rate (EER = 0.58 percent), and the total error rate (TER = 0.72 percent).

Determining the absolute orientation in a corridor using projective geometry and active vision

The capability of a mobile robot to determine its position in the environment (self-localization) is a prerequisite for achieving autonomous navigation. An approach is proposed for determining the absolute orientation of an autonomous robot in a system of corridors, based on the projective geometry and active computer vision. In the proposed approach, the common direction of longitudinal corridor edges is inferred by detecting the vanishing point of the corresponding straight line segments in the image. It is assumed that the knowledge about the vertical direction in the scene is available, so that the image coordinates of these vanishing points are considerably constrained. However, longitudinal corridor edges are not visible in images acquired for many viewing directions, so that the processing in a localization procedure has to be performed on a sequence of images acquired from the given position, for regularly arranged orientations of the camera. Extensive experimentation was performed on real scenes and the obtained results are provided.

A biometric verification system based on the fusion of palmprint and face features

This paper presents a bimodal biometric verification system for physical access control based on the features of the palmprint and the face. The system tries to improve the verification results of unimodal biometric systems based on palmprint or facial features by integrating them using fusion at the matching-score level. The verification process consists of image acquisition using a scanner and a camera, palmprint recognition based on the principal lines, face recognition with eigenfaces, fusion of the unimodal results at the matching-score level, and finally, a decision based on thresholding. The experimental results show that fusion improves the equal error rate by 0.74% and the minimum total error rate by 1.72%.

Thermal and Visual Image Registration in Hough Parameter Space

This paper introduces a new approach in image registration, that is a multisensor registration in Hough parameter space. Visual and thermal images of building fronts were aimed to be aligned in order to inspect thermal properties of buildings. Some preprocessing of visible images was necessary to be comparable to their thermal counterparts, namely downsampling and color space conversion from RGB to grayscale intensity. For each image pair, edges were detected with Canny edge detector and, as a result, binary edge images were obtained. These images were further processed by Hough transform which extracted all linear image segments. We decided for linear segments, because they are the most frequent feature appearing in the images of buildings. In the Hough parameter space the rotation and translation of the linear segments can be recovered using the line correspondence analysis. The method was verified first on synthetic images with only translation, only rotation, and also both the rotation and translation together. Finally, a verification on real images was done. The method was able to correctly register both type of images, synthetic and the real ones. In general, our algorithm can cope with rotated and translated images if only a few linear segments are detectible.

A model of fuzzy spatio-temporal knowledge representation and reasoning based on high-level Petri nets

In many application areas there is a need to represent human-like knowledge related to spatio-temporal relations among multiple moving objects. This type of knowledge is usually imprecise, vague and fuzzy, while the reasoning about spatio-temporal relations is intuitive. In this paper we present a model of fuzzy spatio-temporal knowledge representation and reasoning based on high-level Petri nets. The model should be suitable for the design of a knowledge base for real-time, multi-agent-based intelligent systems that include expert or user human-like knowledge. The central part of the model is the knowledge representation scheme called FuSpaT, which supports the representation and reasoning for domains that include imprecise and fuzzy spatial, temporal and spatio-temporal relationships. The scheme is based on the high-level Petri nets called Petri nets with fuzzy spatio-temporal tokens (PeNeFuST). The FuSpaT scheme integrates the theory of the PeNeFuST and 117 spatio-temporal relations. The reasoning in the proposed model is a spatio-temporal data-driven process based on the dynamical properties of the scheme, i.e., the execution of the Petri nets with fuzzy spatio-temporal tokens. An illustrative example of the spatio-temporal reasoning for two agents in a simplified robot-soccer scene is given.

Real-time active visual tracking system

The paper describes implementation of a real-time visual tracking system equipped with an active camera. The system is intended for indoor human motion tracking. Real-time tracking is achieved using simple and fast motion detection procedures based on frame differencing and camera motion compensation. Results of on-line person tracking are presented.

Gray level thresholding using the Havrda and Charvat entropy

Investigating the Kapur et al. (1985) image thresholding method, we found, that taking the sum of the Havrda and Charvat entropies as a criterion for threshold selection instead of the Shannon entropies, can result in a better image segmentation in the sense of greater uniformity of the partitioned segments, as well as greater contrast among segments.

Experimental Evaluation of Matching-Score Normalization Techniques on Different Multimodal Biometric Systems

The goal of this work is the experimental evaluation of matching-score normalization techniques for the following three multimodal biometric systems: a verification system based on the fusion of face and palmprint principal line features, an identification system based on eigenfingers and finger-geometry, and an identification system based on eigenpalm and eigenfinger features. The following normalization techniques are tested: Bayes-based normalization and several heuristic techniques (min-max, z-score, median-MAD, double-sigmoid, tanh, and piecewise-linear). The results of evaluation are represented by means of system performance (expressed by ROC, EER and minTER) and F-statistics

De-identification for privacy protection in multimedia content

Privacy is one of the most important social and political issues in our information society, characterized by a growing range of enabling and supporting technologies and services. Amongst these are communications, multimedia, biometrics, big data, cloud computing, data mining, internet, social networks, and audio-video surveillance. Each of these can potentially provide the means for privacy intrusion. De-identification is one of the main approaches to privacy protection in multimedia contents (text, still images, audio and video sequences and their combinations). It is a process for concealing or removing personal identifiers, or replacing them by surrogate personal identifiers in personal information in order to prevent the disclosure and use of data for purposes unrelated to the purpose for which the information was originally obtained. Based on the proposed taxonomy inspired by the Safe Harbour approach, the personal identifiers, i.e., the personal identifiable information, are classified as non-biometric, physiological and behavioural biometric, and soft biometric identifiers. In order to protect the privacy of an individual, all of the above identifiers will have to be de-identified in multimedia content. This paper presents a review of the concepts of privacy and the linkage among privacy, privacy protection, and the methods and technologies designed specifically for privacy protection in multimedia contents. The study provides an overview of de-identification approaches for non-biometric identifiers (text, hairstyle, dressing style, license plates), as well as for the physiological (face, fingerprint, iris, ear), behavioural (voice, gait, gesture) and soft-biometric (body silhouette, gender, age, race, tattoo) identifiers in multimedia documents. Privacy protection in multimedia.Taxonomy of the personal identifiers in multimedia contents.De-identification of non-biometrical identifiers.De-identification of physiological, behavioural and soft biometric identifiers.

Restoration of images blurred by circular motion

The problem of restoring images blurred by circular motion is considered. In order to simplify the process of deblurring, the original image in the (x,y) plane is transformed into polar plane (p,/spl phi/) . This simplifies the blur model, resulting in one-dimensional integration.