nils-herrmann/clean_scirepeval huggingface dataset:
Face Recognition Performance: Role of Demographic Information

This paper studies the influence of demographics on the performance of face recognition algorithms. The recognition accuracies of six different face recognition algorithms (three commercial, two nontrainable, and one trainable) are computed on a large scale gallery that is partitioned so that each partition consists entirely of specific demographic cohorts. Eight total cohorts are isolated based on gender (male and female), race/ethnicity (Black, White, and Hispanic), and age group (18-30, 30-50, and 50-70 years old). Experimental results demonstrate that both commercial and the nontrainable algorithms consistently have lower matching accuracies on the same cohorts (females, Blacks, and age group 18-30) than the remaining cohorts within their demographic. Additional experiments investigate the impact of the demographic distribution in the training set on the performance of a trainable face recognition algorithm. We show that the matching accuracy for race/ethnicity and age cohorts can be improved by training exclusively on that specific cohort. Operationally, this leads to a scenario, called dynamic face matcher selection, where multiple face recognition algorithms (each trained on different demographic cohorts) are available for a biometric system operator to select based on the demographic information extracted from a probe image. This procedure should lead to improved face recognition accuracy in many intelligence and law enforcement face recognition scenarios. Finally, we show that an alternative to dynamic face matcher selection is to train face recognition algorithms on datasets that are evenly distributed across demographics, as this approach offers consistently high accuracy across all cohorts.
Face recognition: Impostor-based measures of uniqueness and quality

We present a framework, called uniqueness-based nonmatch estimates (UNE), which demonstrates the ability to improve face recognition performance of any face matcher. The first aspect of the framework is a novel metric for measuring the uniqueness of a given individual, called the impostor-based uniqueness measure (IUM). The UNE the maps face match scores from any any face matcher into non-match probability estimates that are conditionally dependent on the probe image's IUM. Using this framework we demonstrate: (i) an improved generalization of matching thresholds (and, subsequently, improved matching accuracy), (ii) a score normalization technique that improves the interoperability for users of different face matchers, and (iii) the predictive ability of IUM towards face recognition accuracy. Studies are conducted on an operational dataset with 16,000 subjects using three different face matchers (two commercial, one proprietary) to demonstrate the effectiveness of the proposed framework.
Algorithms for feature selection: An evaluation

A large number of algorithms have been proposed for doing feature subset selection. The goal of this paper is to evaluate the quality of feature subsets generated by the various algorithms, and also compare their computational requirements. Our results show that the sequential forward floating selection (SFFS) algorithm, proposed by Pudil et al. (1994), dominates the other algorithms tested. This paper also illustrates the dangers of using feature selection in small sample size situations. It gives the results of applying feature selection to land use classification of SAR satellite images using four different texture models. Pooling features derived from different texture models, followed by a feature selection results in a substantial improvement in the classification accuracy. Application of feature selection to classification of handprinted characters illustrates the value of feature selection in reducing the number of features needed for classifier design.
Physical layer built-in security analysis and enhancement of CDMA systems

Historically developed for secure communication and military use, CDMA is now serving as one of the most widely used wireless airlink interface and has been identified as a major technique for 3G wireless communications. In addition to the wide bandwidth and low power spectrum density which make CDMA signals robust to narrow band jamming and easy to be concealed within the noise floor, the physical layer built-in information privacy of CDMA system is provided by pseudo-random scrambling. In this paper, first, the physical layer security weakness of the operational IS-95 CDMA airlink interface is analyzed. Secondly, based on the advanced encryption standard (AES), we propose to enhance the physical layer built-in security of CDMA systems through secure scrambling. Performance analysis demonstrates that while providing significantly improved information privacy, CDMA system with secure scrambling has comparable computational complexity and system performance with that of the IS-95 system. Moreover, it is shown that by scrambling the training sequence and the message sequence separately with two independent scrambling sequences, both information privacy and system performance can be further improved. The proposed scheme can readily be applied to 3G systems and IEEE 802.11b WLAN systems
A framework for multi-date multi-sensor image interpretation

This paper describes a methodological framework for multi-date multi-sensor remotely-sensed image interpretation. The framework was built based on an integrated multi-sensor classification scheme. The classification scheme considers a remotely-sensed input image to consist of homogeneous and textured regions. This approach allows flexibility in performing digital image mosaicking and data fusion for the purpose of cloud cover removal and a more consistent scene interpretation. The authors have determined that both in optical and radar images the land cover types can be grouped into homogeneous and textured regions. Using their classification scheme, the discrimination between land cover types in each group can be enhanced. It was found that better classification accuracy can be obtained by not assuming that the input image is fully homogeneous where a classifier based on image tone or spectral features is applied or fully textured where a classifier based on texture features is applied. Several results on digital image mosaicking and data fusion using the proposed classification scheme are presented.
Vehicle Segmentation and Classification Using Deformable Templates

This paper proposes a segmentation algorithm using deformable template models to segment a vehicle of interest both from the stationary complex background and other moving vehicles in an image sequence. We define a polygonal template to characterize a general model of a vehicle and derive a prior probability density function to constrain the template to be deformed within a set of allowed shapes. We propose a likelihood probability density function which combines motion information and edge directionality to ensure that the deformable template is contained within the moving areas in the image and its boundary coincides with strong edges with the same orientation in the image. The segmentation problem is reduced to a minimization problem and solved by the Metropolis algorithm. The system was successfully tested on 405 image sequences containing multiple moving vehicles on a highway.
Generating Statistically Correlated Glint And RCS For Empirically Based RF Target Models

In this paper we study the problem of generating correlated random variables representing glint and scintillation (or equivalently the radar cross-section, RCS) for distributed targets. Earlier models of glint and RCS have not considered the correlation between these two variables. We propose a new model which incorporates the appropriate correlation whose parameters can be estimated from aspect dependent measurements of mean and variances of glint and RCS variables. An algorithm for implementing the proposed model for generating correlated glint and RCS random variables is presented.
On iris camera interoperability

With the advancements in iris matching and growing number of system deployments, a wide variety of iris cameras are now being manufactured. These cameras differ in manufacturing technology, including image acquisition spectrum and illumination settings. For large scale applications (e.g. UID system in India) where cameras from several vendors are likely to be used for iris enrollment and authentication, iris camera interoperability is an important consideration. The question we address here is: will the matching accuracy differ in matching iris images captured by two different cameras compared to images captured by the same camera? We propose an iris camera classification-based preprocessing framework to address iris interoperability. The camera classification output is used to perform selective iris image enhancement. Experimental results on the IIITD Multi-Sensor Iris database collected locally and the Notre Dame Cross Sensor database show a significant improvement in the cross-camera iris recognition accuracy using the proposed approach.
Finger ECG signal for user authentication: Usability and performance

Over the past few years, the evaluation of Electrocardio-graphic (ECG) signals as a prospective biometric modality has revealed promising results. Given the vital and continuous nature of this information source, ECG signals offer several advantages to the field of biometrics; yet, several challenges currently prevent the ECG from being adopted as a biometric modality in operational settings. These arise partially due to ECG signal's clinical tradition and intru-siveness, but also from the lack of evidence on the permanence of the ECG templates over time. The problem of in-trusiveness has been recently overcome with the “off-the-person” approach for capturing ECG signals. In this paper we provide an evaluation of the permanence of ECG signals collected at the fingers, with respect to the biometric authentication performance. Our experimental results on a small dataset suggest that further research is necessary to account for and understand sources of variability found in some subjects. Despite these limitations, “off-the-person” ECG appears to be a viable trait for multi-biometric or standalone biometrics, low user throughput, real-world scenarios.
An antenna for dual-wavelength radiometry at 21 and 32 GHz

Accurate multiwavelength remote sensing of the atmosphere requires antennas with the same beamwidth at the various frequencies of operation. A single offset antenna with a corrugated feed which meets this criterion at 20.6 and 31.65 GHz is described. The planar near-field (PNF) scanning facility at the National Bureau of Standards (NBS) was utilized to measure the near-field patterns of the overall antenna for various feed positions, and with an apodizer placed on the reflector. Comparison of the far-field patterns, calculated using PNF methods, yielded the optimum configuration. In addition, the facility was used as a far-field range to measure the radiation pattern of the feed. The antenna is presently installed at Stapleton International Airport, Denver, CO, in a dual-channel radiometric system which continuously remotely senses water vapor and liquid, and it is performing satisfactorily.
Path diversity in propagation of millimeter waves through rain

Cumulative distributions of attenuation due to rain are computed for the frequencies 11,18.5, and 30 GHz. The results are derived from a four-year sample of rainfall data obtained on a highly resolving areal rain gauge network in Bedfordshire, England. Lack of correlation of attenuation on both orthogonal and parallel paths is discussed and the effect of path diversity is demonstrated for the frequency 30 GHz. Examples of computer-generated maps of intense rain showers at the earth's surface are given.
Wet Reflectors in Millimeter-Wave Radiometry-Expenrment and Theory

Microwave radiometers are often required to operate under all weather conditions, including those under which an exposed surface of the antenna system becomes wetted by rain. Here we discuss the special case of a wetted flat reflector, with energy at 20.60 and 31.65 GHz linearly and orthogonally polarized at an incidence angle of 45°. The brightness temperatures produced by the wet reflector are computed from conventional theory, and are measured for various thicknesses of water layer. Both theory and experiment show that the brightness temperatures produced by the wet reflector have the same behavior throughout a range of water-layer thicknesses. The impact of these results on design of radiometers for research and operational application is discussed.
Learning structured behaviour models using variable length Markov models

In recent years there has been an increased interest in the modelling and recognition of human activities involving highly structured and semantically rich behaviour such as dance, aerobics, and sign language. A novel approach is presented for automatically acquiring stochastic models of the high-level structure of an activity without the assumption of any prior knowledge. The process involves temporal segmentation into plausible atomic behaviour components and the use of variable length Markov models for the efficient representation of behaviours. Experimental results are presented which demonstrate the generation of realistic sample behaviours and evaluate the performance of models for long-term temporal prediction.
Personalizing Human Video Pose Estimation

We propose a personalized ConvNet pose estimator that automatically adapts itself to the uniqueness of a person's appearance to improve pose estimation in long videos. We make the following contributions: (i) we show that given a few high-precision pose annotations, e.g. from a generic ConvNet pose estimator, additional annotations can be generated throughout the video using a combination of image-based matching for temporally distant frames, and dense optical flow for temporally local frames, (ii) we develop an occlusion aware self-evaluation model that is able to automatically select the high-quality and reject the erroneous additional annotations, and (iii) we demonstrate that these high-quality annotations can be used to fine-tune a ConvNet pose estimator and thereby personalize it to lock on to key discriminative features of the person's appearance. The outcome is a substantial improvement in the pose estimates for the target video using the personalized ConvNet compared to the original generic ConvNet. Our method outperforms the state of the art (including top ConvNet methods) by a large margin on three standard benchmarks, as well as on a new challenging YouTube video dataset. Furthermore, we show that training from the automatically generated annotations can be used to improve the performance of a generic ConvNet on other benchmarks.
The radiation pattern and impedance of offset and symmetrical near-field Cassegrainian and Gregorian antennas

Most Cassegrainian and Gregorian antennas have axial symmetry, in which case the subreflector and associated supporting members partially block the aperture. Consequently, relatively high sidelobes appear in the radiation pattern, and a reflection is produced in the transmission line of the feed. These undesirable effects can be largely eliminated using asymmetrical configurations. Here we compare axisymmetrical and offset near-field Cassegrainians and Gregorians; expressions for the reflection coefficient and increase in sidelobe level are given. The offset designs are found to have superior performance in both respects.
Efficient Non-iterative Domain Adaptation of Pedestrian Detectors to Video Scenes

Pedestrian detection is an essential step in many important applications of Computer Vision. Most detectors require manually annotated ground-truth to train, the collection of which is labor intensive and time-consuming. Generally, this training data is from representative views of pedestrians captured from a variety of scenes. Unsurprisingly, the performance of a detector on a new scene can be improved by tailoring the detector to the specific viewpoint, background and imaging conditions of the scene. Unfortunately, for many applications it is not practical to acquire this scene-specific training data by hand. In this paper, we propose a novel algorithm to automatically adapt and tune a generic pedestrian detector to specific scenes which may possess different data distributions than the original dataset from which the detector was trained. Most state-of-the-art approaches can be inefficient, require manually set number of iterations to converge and some form of human intervention. Our algorithm is a step towards overcoming these problems and although simple to implement, our algorithm exceeds state-of-the-art performance.
Learning Hierarchical Models of Complex Daily Activities from Annotated Videos

Effective recognition of complex long-term activities is becoming an increasingly important task in artificial intelligence. In this paper, we propose a novel approach for building models of complex long-term activities. First, we automatically learn the hierarchical structure of activities by learning about the 'parent-child' relation of activity components from a video using the variability in annotations acquired using multiple annotators. This variability allows for extracting the inherent hierarchical structure of the activity in a video. We consolidate hierarchical structures of the same activity from different videos into a unified stochastic grammar describing the overall activity. We then describe an inference mechanism to interpret new instances of activities. We use three datasets, which have been annotated by multiple annotators, of daily activity videos to demonstrate the effectiveness of our system.
Building qualitative event models automatically from visual input

We describe an implemented technique for generating event models automatically based on qualitative reasoning and a statistical analysis of video input. Using an existing tracking program which generates labelled contours for objects in every frame, the view from a fixed camera is partitioned into semantically relevant regions based on the paths followed by moving objects. The paths are indexed with temporal information so objects moving along the same path at different speeds can be distinguished. Using a notion of proximity based on the speed of the moving objects and qualitative spatial reasoning techniques, event models describing the behaviour of pairs of objects can be built, again using statistical methods. The system has been tested on a traffic domain and learns various event models expressed in the qualitative calculus which represent human observable events. The system can then be used to recognise subsequent selected event occurrences or unusual behaviours.
On the feasibility of using a cognitive model to filter surveillance data

This paper describes a novel approach to the problem of automated visual surveillance. The authors have extended an existing algorithm which uses a cognitive model of navigation to explain behaviour in a surveillance setting. We then take this cognitive model and apply it to the problem of filtering surveillance data: typically, a surveillance or CCTV installation will have a limited number of operatives monitoring a large number of cameras. The proposed system filters upon inexplicability scores, on the grounds that those trajectories which we can explain in terms of simple goals are exactly those trajectories which are uninteresting: it is only those we cannot simply explain which are worth attending to. Initial results are promising, with over 50% of uninteresting trajectories being excluded.
The acquisition and use of interaction behaviour models

Providing a machine with the ability to learn and use models of natural interaction is a challenging and largely unaddressed problem. A framework is developed enabling both the acquisition of interaction behaviours from the observation of humans, and the use of the acquired behaviour models to simulate a plausible partner during interaction. Statistically based interaction behaviour models are acquired automatically from the observation of interacting humans. Interaction with a virtual human is achieved using the model together with a stochastic tracking algorithm. Experimental results demonstrate the generation and use of the model for a simple human interaction.
Focusing Of 52-GHz Beams By Cylindrical Mirrors (Short papers)

The possibility of using inexpensive commercial glass mirrors for refocusing and redirecting millimeter-wave beams has already been investigated; such beams would be useful for distributing large quantities of information in cities. Interference is expected to be minimal as a result of the close confinement of the beams. We report preliminary experiments made in the 50-55-GHz band, using a swept backward-wave oscillator (BWO) as a source.
Fast Direct Methods for Gaussian Processes

A number of problems in probability and statistics can be addressed using the multivariate normal (Gaussian) distribution. In the one-dimensional case, computing the probability for a given mean and variance simply requires the evaluation of the corresponding Gaussian density. In the n-dimensional setting, however, it requires the inversion of an n x n covariance matrix, C, as well as the evaluation of its determinant, det(C). In many cases, such as regression using Gaussian processes, the covariance matrix is of the form C = σ2I + K, where K is computed using a specified covariance kernel which depends on the data and additional parameters (hyperparameters). The matrix C is typically dense, causing standard direct methods for inversion and determinant evaluation to require O(n3) work. This cost is prohibitive for large-scale modeling. Here, we show that for the most commonly used covariance functions, the matrix C can be hierarchically factored into a product of block low-rank updates of the identity matrix, yielding an O(n log2 n) algorithm for inversion. More importantly, we show that this factorization enables the evaluation of the determinant det(C), permitting the direct calculation of probabilities in high dimensions under fairly broad assumptions on the kernel defining K. Our fast algorithm brings many problems in marginalization and the adaptation of hyperparameters within practical reach using a single CPU core. The combination of nearly optimal scaling in terms of problem size with high-performance computing resources will permit the modeling of previously intractable problems. We illustrate the performance of the scheme on standard covariance kernels.
A Dual-Channel Microwave Radiometer for Measurement of Precipitable Water Vapor and Liquid

The design and performance of a two-channel ground-based microwave radiometer (20.6 and 31.6 GHz) for measurement of total integrated water vapor and cloud liquid in a vertical column are discussed. Unique features of this instrument are a single antenna producing equal beamwidth for the two frequencies, and incorporation of two stable reference loads in a three-way Dicke switching sequence. The latter allows an automatic gain control (AGC) to be applied in a minicomputer. Linear statistical inversion is used to retrieve the precipitable water vapor and liquid quantities which are provided in real time. The instrument has been in reliable operation, continuously and unattended, at the National Weather Service Forecast Office, Denver, CO, for a six-month period. The radiometrically derived precipitable water vapor compares favorably with that obtained from radiosondes.