| 期刊全称 | Advanced Studies in Biometrics | | 期刊简称 | Summer School on Bio | | 影响因子2023 | Massimo Tistarelli,Josef Bigun,Enrico Grosso | | 视频video | | | 发行地址 | Includes supplementary material: | | 学科分类 | Lecture Notes in Computer Science | | 图书封面 |  | | 影响因子 | .Automatic person authentication, the identification and verification of an individual as such, has increasingly been acknowledged as a significant aspect of various security applications. Various recognition and identification systems have been based on biometrics utilizing biometric features such as fingerprint, face, retina scans, iris patterns, hand geometry, DNA traces, gait, and others...This book originates from an international summer school on biometrics, held in Alghero, Italy, in June 2003. The seven revised tutorial lectures by leading researchers introduce the reader to biometrics-based person authentication, fingerprint recognition, gait recognition, various aspects of face recognition and face detection, topologies for biometric recognition, and hand detection. Also included are the four best selected student papers, all dealing with face recognition.. | | Pindex | Textbook 2005 |
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Front Matter |
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Abstract
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| 2 |
Combining Biometric Evidence for Person Authentication |
J. Bigun,J. Fierrez-Aguilar,J. Ortega-Garcia,J. Gonzalez-Rodriguez |
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Abstract
Humans are excellent experts in person recognition and yet they do not perform excessively well in recognizing others only based on one modality such as single facial image. Experimental evidence of this fact is reported concluding that even human authentication relies on multimodal signal analysis. The elements of automatic multimodal authentication along with system models are then presented. These include the machine experts as well as machine supervisors. In particular, fingerprint and speech based systems will serve as illustration. A signal adaptive supervisor based on the input biometric signal quality is evaluated. Experimental results on data collected from a mobile telephone prototype application are reported demonstrating the benefits of the reported scheme.
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Biometric Gait Recognition |
Jeffrey E. Boyd,James J. Little |
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Abstract
Psychological studies indicate that people have a small but statistically significant ability to recognize the gaits of individuals that they know. Recently, there has been much interest in machine vision systems that can duplicate and improve upon this human ability for application to biometric identification. While gait has several attractive properties as a biometric (it is unobtrusive and can be done with simple instrumentation), there are several confounding factors such as variations due to footwear, terrain, fatigue, injury, and passage of time. This paper gives an overview of the factors that affect both human and machine recognition of gaits, data used in gait and motion analysis, evaluation methods, existing gait and quasi gait recognition systems, and uses of gait analysis beyond biometric identification. We compare the reported recognition rates as a function of sample size for several published gait recognition systems.
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A Tutorial on Fingerprint Recognition |
Davide Maltoni |
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Abstract
This tutorial introduces fingerprint recognition systems and their main components: sensing, feature extraction and matching. The basic technologies are surveyed and some state-of-the-art algorithms are discussed. Due to the extent of this topic it is not possible to provide here all the details and to cover a number of interesting issues such as classification, indexing and multi-modal systems. Interested readers can find in [21] a complete and comprehensive guide to fingerprint recognition.
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Spiral Topologies for Biometric Recognition |
Massimo Tistarelli,Enrico Grosso,Andrea Lagorio |
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Abstract
Biometric recognition has attracted the attention of scientists, investors, government agencies as well as the media for the great potential in many application domains. It turns out that there are still a number of intrinsic drawbacks in all biometric techniques. In this paper we postulate the need for a proper data representation which may simplify and augment the discrimination among different instances or biometric samples of different subjects. Considering the design of many natural systems it turns out that spiral (circular) topologies are the best suited to economically store and process data. Among the many developed techniques for biometric recognition, face analysis seems to be the most promising and interesting modality. The ability of the human visual system of analyzing unknown faces, is an example of the amount of information which can be extracted from face images. Nonetheless, there are still many open problems which need to be ”faced” as well. The choice of optimal resolution of the face within the image, face registration and facial feature extraction are still open issues. This not only requires to devise new algorithms but to determine the real potential and lim
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Statistical Learning Approaches with Application to Face Detection |
Emanuele Franceschi,Francesca Odone,Alessandro Verri |
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Abstract
We present a concise tutorial on statistical learning, the theoretical ground on which the learning from examples paradigm is based. We also discuss the problem of face detection as a case study illustrating the solutions proposed in this framework. Finally, we describe some new results we obtained by means of an object detection method based on statistical hypothesis tests which makes use of positive examples only.
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Hand Detection by Direct Convexity Estimation |
Dganit Maimon,Yehezkel Yeshurun |
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Abstract
We suggest a novel attentional mechanism for detection of smooth convex and concave objects based on direct processing of intensity values. The operator detects the region of the forearm in images, enabling location of the hand. The operator is robust to variation in illumination, scale, pose, and hand orientation. This method uses the geometrical structure of the forearm, which is common to all people; therefore no limitation of the hand pose and no personal adjustments are required.
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Template-Based Hand Detection and Tracking |
R. Cipolla,B. Stenger,A. Thayananthan,P. H. S. Torr |
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Abstract
Within this paper a technique for model-based 3D hand tracking is presented. A hand model is built from a set of truncated quadrics, approximating the anatomy of a real hand with few parameters. Given that the projection of a quadric onto the image plane is a conic, the contours can be generated efficiently. These model contours are used as shape templates to evaluate possible matches in the current frame. The evaluation is done within a hierarchical Bayesian filtering framework, where the posterior distribution is computed efficiently using a tree of templates. We demonstrate the effectiveness of the technique by using it for tracking 3D articulated and non-rigid hand motion from monocular video sequences in front of a cluttered background.
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3D Face Recognition Using Stereoscopic Vision |
U. Castellani,M. Bicego,G. Iacono,V. Murino |
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Abstract
In this paper a new complete system for 3D face recognition is presented. 3D face recognition presents several advantages against 2D face recognition, as, for example, invariance to illumination conditions. The proposed system makes use of a stereo methodology, that does not require any expensive range sensors. The 3D image of the face is modelled using Multilevel B-Splines coefficients, that are classified using Support Vector Machines. Preliminary experimental evaluation has produced encouraging results, making the proposed system a promising low cost 3D face recognition system.
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Selection of Location, Frequency, and Orientation Parameters of 2D Gabor Wavelets for Face Recogniti |
Berk Gökberk,M. Okan Irfanoglu,Lale Akarun,Ethem Alpaydın |
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Abstract
In this paper, a two–level supervised feature selection algorithm for local feature–based face recognition is presented. In the first part, a genetic algorithm is used to determine the useful locations of the face region for recognition. 2D Gabor wavelet–based feature extractors are used for local image descriptors at these locations. In the second part, the most useful frequencies and orientations of Gabor kernels are determined using a floating feature selection algorithm. Our major aim in this study is to examine the relevance of the two common assumptions in the local feature based face recognition literature: first, that the contribution of a specific feature to the recognition performance is independent of others, and secondly, that feature extractors should be placed over the visually salient points. In this paper, we show that one can obtain better recognition accuracy by relaxing these two assumptions.
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A Face Recognition System Based on Local Feature Characterization |
Paola Campadelli,Raffaella Lanzarotti |
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Abstract
A completely automatic face recognition system is presented. The method works on color and gray level images: after having localized the face and the facial features, it determines 16 facial fiducial points, and characterizes them applying a bank of filters which extract the peculiar texture around them (.). Recognition is realized measuring the similarity between the different .. The system is inspired by the elastic bunch graph method, but the fiducial point localization does not require any manual setting.
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Influence of Location over Several Classifiers in 2D and 3D Face Verification |
Susana Mata,Cristina Conde,Araceli Sánchez,Enrique Cabello |
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Abstract
In this paper two methods for human face recognition and the influence of location mistakes are shown. First one, Principal Components Analysis (PCA), has been one of the most applied methods to perform face verification in 2D. In our experiments three classifiers have been considered to test influence of location errors in face verification using PCA. An initial set of ”correct located faces” has been used for PCA matrix computation and to train all classifiers. An initial test set was built considering a ”correct located faces” set (based on different images than training ones) and then a new test set was obtained by applying a small displacement in both axis (20 pixels) to the initial set. Second method is based on geometrical characteristics constructed with facial and cranial points that come from a 3D representation. Data are acquired by a calibrated stereo system. Classifiers considered for both methods are k-nearest neighbours (KNN), artificial neural networks: radial basis function (RBF) and Support Vector Machine (SVM). Given our data set, results show that SVM is capable to classify correctly in the presence of small location errors. RBF has an acceptable correct rate bu
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Back Matter |
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Abstract
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发表于 2025-3-21 18:22:51
