Kresimir Delac

Bio: Kresimir Delac is an academic researcher from University of Zagreb. The author has contributed to research in topics: Facial recognition system & JPEG. The author has an hindex of 16, co-authored 32 publications receiving 1886 citations.

SCface --- surveillance cameras face database

Abstract: In this paper we describe a database of static images of human faces. Images were taken in uncontrolled indoor environment using five video surveillance cameras of various qualities. Database contains 4,160 static images (in visible and infrared spectrum) of 130 subjects. Images from different quality cameras should mimic real-world conditions and enable robust face recognition algorithms testing, emphasizing different law enforcement and surveillance use case scenarios. In addition to database description, this paper also elaborates on possible uses of the database and proposes a testing protocol. A baseline Principal Component Analysis (PCA) face recognition algorithm was tested following the proposed protocol. Other researchers can use these test results as a control algorithm performance score when testing their own algorithms on this dataset. Database is available to research community through the procedure described at www.scface.org .

A survey of biometric recognition methods

Abstract: Biometric recognition refers to an automatic recognition of individuals based on a feature vector (s) derived from their physiological and/or behavioral characteristic. Biometric recognition systems should provide a reliable personal recognition schemes to either confirm or determine the identity of an individual. Applications of such a system include computer systems security, secure electronic banking, mobile phones, credit cards, secure access to buildings, health and social services. By using biometrics a person could be identified based on "who she/he is" rather then "what she/he has" (card, token, key) or "what she/he knows" (password, PIN). In this paper, a brief overview of biometric methods, both unimodal and multimodal, and their advantages and disadvantages, will be presented.

Independent Comparative Study of PCA, ICA, and LDA on the FERET Data Set

Abstract: Face recognition is one of the most successful applica- tions of image analysis and understanding and has gained much attention in recent years. Various algorithms were proposed and research groups across the world reported different and often contra- dictory results when comparing them. The aim of this paper is to present an independent, comparative study of three most popular appearance-based face recognition projection methods (PCA, ICA, and LDA) in completely equal working conditions regarding prepro- cessing and algorithm implementation. We are motivated by the lack of direct and detailed independent comparisons of all possible algo- rithm implementations (e.g., all projection-metric combinations) in available literature. For consistency with other studies, FERET data set is used with its standard tests (gallery and probe sets). Our results show that no particular projection-metric combination is the best across all standard FERET tests and the choice of appropriate projec- tion-metric combination can only be made for a specific task. Our results are compared to other available studies and some discrepan- cies are pointed out. As an additional contribution, we also introduce our new idea of hypothesis testing across all ranks when comparing

Overview of the DICOM standard

Abstract: Digital technology has in the last few decades entered almost every aspect of medicine. There has been a huge development in noninvasive medical imaging equipment. Because there are many medical equipment manufacturers, a standard for storage and exchange of medical images needed to be developed. DICOM (Digital Imaging and Communication in Medicine) makes medical image exchange more easy and independent of the imaging equipment manufacturer. Besides the image data, DICOM file format supports other information useful to describe the image. This makes DICOM easy to use and the data exchange fast and safe while avoiding possible confusion caused by multiple files for the same study.

Sub-Image Homomorphic Filtering Technique for Improving Facial Identification under Difficult Illumination Conditions

Abstract: In this paper we will propose a simple modification of standard homomorphic filtering technique and thus significantly improve face recognition performance on images with difficult illumination conditions. We will also give a detailed theoretical description of a homomorphic filter and compare our proposed method to common illumination compensation techniques used in face recognition literature. The comparisons will be performed on standard grayscale FERET database and this will, in addition, be the first evaluation of homomorphic filter on this database. Results will show that our method yields significantly better identification results than standard illumination compensation methods currently used in face recognition.

Multi-PIE

Abstract: A close relationship exists between the advancement of face recognition algorithms and the availability of face databases varying factors that affect facial appearance in a controlled manner. The CMU PIE database has been very influential in advancing research in face recognition across pose and illumination. Despite its success the PIE database has several shortcomings: a limited number of subjects, a single recording session and only few expressions captured. To address these issues we collected the CMU Multi-PIE database. It contains 337 subjects, imaged under 15 view points and 19 illumination conditions in up to four recording sessions. In this paper we introduce the database and describe the recording procedure. We furthermore present results from baseline experiments using PCA and LDA classifiers to highlight similarities and differences between PIE and Multi-PIE.

Template Matching Techniques in Computer Vision: Theory and Practice

Abstract: The detection and recognition of objects in images is a key research topic in the computer vision community Within this area, face recognition and interpretation has attracted increasing attention owing to the possibility of unveiling human perception mechanisms, and for the development of practical biometric systems This book and the accompanying website, focus on template matching, a subset of object recognition techniques of wide applicability, which has proved to be particularly effective for face recognition applications Using examples from face processing tasks throughout the book to illustrate more general object recognition approaches, Roberto Brunelli: examines the basics of digital image formation, highlighting points critical to the task of template matching; presents basic and advanced template matching techniques, targeting grey-level images, shapes and point sets; discusses recent pattern classification paradigms from a template matching perspective; illustrates the development of a real face recognition system; explores the use of advanced computer graphics techniques in the development of computer vision algorithms Template Matching Techniques in Computer Vision is primarily aimed at practitioners working on the development of systems for effective object recognition such as biometrics, robot navigation, multimedia retrieval and landmark detection It is also of interest to graduate students undertaking studies in these areas

On the effectiveness of local binary patterns in face anti-spoofing

Abstract: Spoofing attacks are one of the security traits that biometric recognition systems are proven to be vulnerable to. When spoofed, a biometric recognition system is bypassed by presenting a copy of the biometric evidence of a valid user. Among all biometric modalities, spoofing a face recognition system is particularly easy to perform: all that is needed is a simple photograph of the user. In this paper, we address the problem of detecting face spoofing attacks. In particular, we inspect the potential of texture features based on Local Binary Patterns (LBP) and their variations on three types of attacks: printed photographs, and photos and videos displayed on electronic screens of different sizes. For this purpose, we introduce REPLAY-ATTACK, a novel publicly available face spoofing database which contains all the mentioned types of attacks. We conclude that LBP, with ∼15% Half Total Error Rate, show moderate discriminability when confronted with a wide set of attack types.

A review of feature selection methods with applications

Abstract: Feature selection (FS) methods can be used in data pre-processing to achieve efficient data reduction. This is useful for finding accurate data models. Since exhaustive search for optimal feature subset is infeasible in most cases, many search strategies have been proposed in literature. The usual applications of FS are in classification, clustering, and regression tasks. This review considers most of the commonly used FS techniques. Particular emphasis is on the application aspects. In addition to standard filter, wrapper, and embedded methods, we also provide insight into FS for recent hybrid approaches and other advanced topics.