https://www.microsoft.com/en-us/research/wp-content/uploads/2010/01/main.pdf

Multi-PIE

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.

Written by leading authority Ramjee Prasad, this timely new work offers a complete understanding of OFDM technology and applications in wireless communications systems, placing emphasis on wireless LANs and PANs. OFDM is a key technology for beyond 3G communications, promising robust, high capacity, high speed wireless broadband multimedia networks. In this practical resource, established and new technologies are explained clearly and comprehensively, from OFDM basics to a detailed account of a new technique, hybrid OFDM CDMA slow frequency hopping. The book offers an in-depth treatment of the underlying technologies and applications of PANs and describes and evaluates a practical OFDM system concept. Invaluable for wireless engineers and researchers, this cutting edge new book is a must-have for anyone who needs to understand this critical technology and its implementation in WLANs and WPANs.

OFDM for Wireless Communications Systems

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

Template Matching Techniques in Computer Vision: Theory and Practice

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 .

/pdf/scface-surveillance-cameras-face-database-1ku7dgyloc.pdf

SCface --- surveillance cameras face database

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

https://www.vcl.fer.hr/papers_pdf/Independent%20Comparative%20Study%20of%20PCA,%20ICA,%20and%20LDA%20on%20the%20FERET%20Data%20Set.pdf

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

The aim of this paper is to examine a set of wavelet functions (wavelets) for implementation in a still image compression system and to highlight the benefit of this transform relating to today's methods. The paper discusses important features of wavelet transform in compression of still images, including the extent to which the quality of image is degraded by the process of wavelet compression and decompression. Image quality is measured objectively, using peak signal-to-noise ratio or picture quality scale, and subjectively, using perceived image quality. The effects of different wavelet functions, image contents and compression ratios are assessed. A comparison with a discrete-cosine-transform-based compression system is given. Our results provide a good reference for application developers to choose a good wavelet compression system for their application.

Performance analysis of image compression using wavelets

Due to the availability of many sophisticated image processing tools, a digital image forgery is nowadays very often used. One of the common forgery method is a copy-move forgery, where part of an image is copied to another location in the same image with the aim of hiding or adding some image content. Numerous algorithms have been proposed for a copy-move forgery detection (CMFD), but there exist only few benchmarking databases for algorithms evaluation. We developed new database for a CMFD that consist of 260 forged image sets. Every image set includes forged image, two masks and original image. Images are grouped in 5 categories according to applied manipulation: translation, rotation, scaling, combination and distortion. Also, postprocessing methods, such as JPEG compression, blurring, noise adding, color reduction etc., are applied at all forged and original images. In this paper we present database organization and content, creation of forged images, postprocessing methods, and database testing. CoMoFoD database is available at http://www.vcl.fer.hr/comofod.

CoMoFoD — New database for copy-move forgery detection

The increasing availability of large digitized fine art collections opens new research perspectives in the intersection of artificial intelligence and art history. Motivated by the successful performance of Convolutional Neural Networks (CNN) for a wide variety of computer vision tasks, in this paper we explore their applicability for art-related image classification tasks. We perform extensive CNN fine-tuning experiments and consolidate in one place the results for five different art-related classification tasks on three large fine art datasets. Along with addressing the previously explored tasks of artist, genre, style and time period classification, we introduce a novel task of classifying artworks based on their association with a specific national artistic context. We present state-of-the-art classification results of the addressed tasks, signifying the impact of our method on computational analysis of art, as well as other image classification related research areas. Furthermore, in order to question transferability of deep representations across various source and target domains, we systematically compare the effects of domain-specific weight initialization by evaluating networks pre-trained for different tasks, varying from object and scene recognition to sentiment and memorability labelling. We show that fine-tuning networks pre-trained for scene recognition and sentiment prediction yields better results than fine-tuning networks pre-trained for object recognition. This novel outcome of our work suggests that the semantic correlation between different domains could be inherent in the CNN weights. Additionally, we address the practical applicability of our results by analysing different aspects of image similarity. We show that features derived from fine-tuned networks can be employed to retrieve images similar in either style or content, which can be used to enhance capabilities of search systems in different online art collections.

Sonja Grgic

Papers

SCface --- surveillance cameras face database

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

Performance analysis of image compression using wavelets

CoMoFoD — New database for copy-move forgery detection

Fine-tuning Convolutional Neural Networks for fine art classification