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Gait energy images (GEIs) and its variants form the basis of many recent appearance-based gait recognition systems. The GEI combines good recognition performance with a simple implementation, though it suffers problems inherent to appearance-based approaches, such as being highly view dependent. In this paper, we extend the concept of the GEI to 3D, to create what we call the gait energy volume, or GEV. A basic GEV implementation is tested on the CMU MoBo database, showing improvements over both the GEI baseline and a fused multi-view GEI approach. We also demonstrate the efficacy of this approach on partial volume reconstructions created from frontal depth images, which can be more practically acquired, for example, in biometric portals implemented with stereo cameras, or other depth acquisition systems. Experiments on frontal depth images are evaluated on an in-house developed database captured using the Microsoft Kinect, and demonstrate the validity of the proposed approach.

The beta-convergence model has been widely used to predict the convergence rates of European regions to their steady states. However, this model has two major limitations. First, it does not take into account the spatial interactions of the economies and, second, it does not include their structural differences. Many authors have overcome the first problem by sing spatial econometric techniques such as spatial lag or spatial error, but the latter problem has remained unresolved. In our model we use the spatial filtering technique to manage both spatial dependence and structural differences of the economies. Our results show that European regions have a wide range of convergence rates and in some cases regions with similar structural conditions are clustered. The spatial filtering technique is also able to highlight the scale (local, regional or global) of the phenomena that influence growth. This information is very useful for policy makers.

Automatically verifying the identity of a person by means of biometrics (e.g. face and fingerprint) is an important application in our to-day activities such as accessing banking services and security control in airports. To increase the system reliability, several biometric devices are often used. Such a combined system is known as a multimodal biometric system. This paper reports a benchmarking study carried out within the framework of the BioSecure DS2 (Access Control) evaluation campaign organized by the University of Surrey, involving face, fingerprint, and iris biometrics for person authentication, targeting the application of physical access control in a medium-size establishment with some 500 persons.

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-01793-3_73, Person recognition using facial features, e.g., mug-shot images, has long been used in identity documents. However, due to the widespread use of web-cams and mobile devices embedded with a camera, it is now possible to realise facial video recognition, rather than resorting to just still images. In fact, facial video recognition offers many advantages over still image recognition; these include the potential of boosting the system accuracy and deterring spoof attacks. This paper presents the first known benchmarking effort of person identity verification using facial video data. The evaluation involves 18 systems submitted by seven academic institutes.

Contains fulltext : 64581.pdf (publisher's version ) (Open Access)

In this paper we describe two related approaches to estimating the sample sizes required to statistically compare the performance of two classifiers: acceptable failure rates (AFR) and the area under the receiver operating characteristic (ROC) curve (AUC). In particular, we consider rare event detection problems, where the prior class probabilities are highly skewed, and measure performance at a specific operating point and for the whole ROC curve. It is shown that the use of AUC as a performance measure is preferable to AFR as it requires a smaller data set to demonstrate superiority of one classifier over another.

When combining outputs from multiple classifiers, many combination rules are available. Although easy to implement, fixed combination rules are optimal only in restrictive conditions. We discuss and evaluate their performance when the optimality conditions are not fulfilled. Fixed combination rules are then compared with trainable combination rules on real data in the context of face-based identity verification. The face images are classified by combining the outputs of five different face verification experts. It is demonstrated that a reduction in the error rates of up to 50% over the best single expert is achieved on the XM2VTS database, using either fixed or trainable combination rules. (C) 2004 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

This paper details the results of a face verification competition [2] held in conjunction with the First International Conference on Biometric Authentication. The contest was held on the publically available BANCA database [1] according to a defined protocol [6]. Six different verification algorithms from 4 academic and commercial institutions submitted results. Also, a standard set of face recognition software from the internet [3] was used to provide a baseline performance measure.

In this paper we study two aspects of decision fusion for enhancing face authentication. First, sequential fusion of scores obtained on successive video frames of a user's face is used to reduce the error rate. Secondly, the opinions of several face authentication algorithms are combined so that the combined decision is more accurate than the best algorithm alone. The experiments performed on a realistic database demonstrate that the fully automatic multi-frame - multi-experts system proposed in this work allows a significant improvement over the static single-expert system.

Segmentation of individual actions from a stream of human motion is an open problem in computer vision. This paper approaches the problem of segmenting higher-level activities into their component sub-actions using Hidden Markov Models modified to handle missing data in the observation vector. By controlling the use of missing data, action labels can be inferred from the observation vector during inferencing, thus performing segmentation and classification simultaneously. The approach is able to segment both prominent and subtle actions, even when subtle actions are grouped together. The advantage of this method over sliding windows and Viterbi state sequence interrogation is that segmentation is performed as a trainable task, and the temporal relationship between actions is encoded in the model and used as evidence for action labelling.

In the year 2000 a competition was organised to collect face verification results on an identical, publicly available data set using a standard evaluation protocol. The database used was the Xm2vts database along with the Lausanne protocol [14]. Four different institutions submitted results on the database which were subsequently published in [13]. Three years later, a second contest using the same dataset and protocol was organised as part of AVBPA 2003. This time round seven seperate institutions submitted results to the competition. This paper presents the results of the competition and shows that verification results on this protocol have increased in performance by a factor of 3.

We investigate the Behavior Knowledge Space [4] and Decision Templates [7] methods of classifier fusion in the context of personal identity verification involving six intramodal experts exploiting frontal face biometrics. The results of extensive experiments on the XM2VTS database show the Behavioural Knowledge Space fusion strategy achieves consistently better results than the Decision Templates method. Most importantly, it exhibits quasi monotonic behaviour as the number of experts combined increases.

Annotating images with a description of the content can facilitate the organization, storage and retrieval of image databases. It can also be useful in processing images, by taking into account the scene depicted, in intelligent scanners, digital cameras, photocopiers, and printers. We present here our experimentation on indoor/outdoor/close-up content-based image classification. More specifically, we show that it is possible to relate low-level visual features to semantic photo categories, such as indoor, outdoor and close-up, using tree classifiers. We have designed and experimentally compared several classification strategies, producing a classifier that can provide a reasonably good performance on a generic photograph database