nils-herrmann/clean_scirepeval huggingface dataset:
Personal identification utilizing finger surface features

In this paper we present a novel approach for personal identification, which utilizes finger surface features as a biometric identifier. Using dense range data images of the hand, we calculate the curvature-based surface representation, shape index, for the index, middle, and ring fingers. This representation is used for comparisons to determine subject similarity. Our experiments involve the use of a large data set of range images collected over time. We examine the performance of individual finger surfaces as a biometric identifier as well as the performance when using the three finger surfaces in conjunction. The results of our experiments are presented, which indicate that this approach performs well for a first-of-its-kind biometric technique.
A Comparison of 3D Biometric Modalities

Recently, researchers have focused on the use of threedimensional data as a source of distinguishing features for personal identification. In this paper, the recognition performance of three three-dimensional biometric modalities (face, ear, and finger surface) is compared. In addition, we combine the modalities in an attempt to improve overall recognition performance. The Iterative Closest Point (ICP) algorithm and root mean square (RMS) registration error are used to measure match quality in each case. The experimental results using a dataset of multi-modal biometric samples collected from a group of 85 individuals are presented.
Spoofing analysis of mobile device data as behavioral biometric modalities

While mobile devices are no longer a new technology, using the data generated from the use of these devices for security purposes has just recently been explored. Current methods, such as passwords, are quickly becoming antiquated, lacking the robustness, accuracy, and convenience desired to serve as reliable security measures. Since, researchers have resorted to alternative techniques, such as measurements obtained from keyboard interactions and movement, and behavioral interactions, such as application usage. However, practical implementations require further evaluation of circumvention. Thus, this work thoroughly analyzes various threats against mobile devices which use mobile device usage data as behavioral biometrics for authentication. Experimental results indicate that an outsider with a certain level of knowledge regarding the behavior of the device's owner poses a great security threat. Possible countermeasures to prevent such attacks are also provided.
On the vulnerability of ECG verification to online presentation attacks

Electrocardiogram (ECG) has long been regarded as a biometric modality which is impractical to copy, clone, or spoof. However, it was recently shown that an ECG signal can be replayed from arbitrary waveform generators, computer sound cards, or off-the-shelf audio players. In this paper, we develop a novel presentation attack where a short template of the victim's ECG is captured by an attacker and used to map the attacker's ECG into the victim's, which can then be provided to the sensor using one of the above sources. Our approach involves exploiting ECG models, characterizing the differences between ECG signals, and developing mapping functions that transform any ECG into one that closely matches an authentic user's ECG. Our proposed approach, which can operate online or on-the-fly, is compared with a more ideal offline scenario where the attacker has more time and resources. In our experiments, the offline approach achieves average success rates of 97.43% and 94.17% for non-fiducial and fiducial based ECG authentication. In the online scenario, the performance is de-graded by 5.65% for non-fiducial based authentication, but is nearly unaffected for fiducial authentication.
Adapting Starburst for Elliptical Iris Segmentation

Fitting an ellipse to the iris boundaries accounts for the projective distortions present in off-axis images of the eye and provides the contour fitting necessary for the dimensionless mapping used in leading iris recognition algorithms. Previous iris segmentation efforts have either focused on fitting circles to pupillary and limbic boundaries or assigning labels to image pixels. This paper approaches the iris segmentation problem by adapting the starburst algorithm to locate pupillary and limbic feature pixels used to fit a pair of ellipses. The approach is evaluated by comparing the fits to ground truth. Two metrics are used in the evaluation, the first based on the algebraic distance between ellipses, the second based on ellipse chamfer images. Results are compared to segmentations produced by ND_IRIS over randomly selected images from the iris challenge evaluation database. Statistical evidence shows significant improvement of starburst's elliptical fits over the circular fits on which ND_IRIS relies.
Hybrid GAs for Eigen-based facial recognition

In this paper, we have performed an evaluation of genetic-based feature selection and weighting on the PCA-based face recognition. This work highlights the first attempt of applying Genetic Algorithm (GA) based feature selection on the Eigenface method. The results show that genetic-based feature selection reduces the number of features needed by approximately 50% while improving the identification accuracy over the baseline. Genetic-based feature weighting significantly improves the accuracy from an 87.14% to a 92.5% correct recognition rate.
Using associative classification to authenticate mobile device users

Because passwords and personal identification numbers are easily forgotten, stolen, or reused on multiple accounts, the current norm for mobile device security is quickly becoming inefficient and inconvenient. Thus, manufacturers have worked to make physiological biometrics accessible to mobile device owners as improved security measures. While behavioral biometrics has yet to receive commercial attention, researchers have continued to consider these approaches as well. However, studies of interactive data are limited, and efforts which are aimed at improving the performance of such techniques remain relevant. Thus, this paper provides a performance analysis of application, Bluetooth, and Wi-Fi data collected from 189 subjects on a mobile device for user verification. Results indicate that user authentication can be achieved with up to 91% accuracy, demonstrating the effectiveness of associative classification as a feature extraction technique.
Human and Machine Performance on Periocular Biometrics Under Near-Infrared Light and Visible Light

Periocular biometrics is the recognition of individuals based on the appearance of the region around the eye. Periocular recognition may be useful in applications where it is difficult to obtain a clear picture of an iris for iris biometrics, or a complete picture of a face for face biometrics. Previous periocular research has used either visible-light (VL) or near-infrared (NIR) light images, but no prior research has directly compared the two illuminations using images with similar resolution. We conducted an experiment in which volunteers were asked to compare pairs of periocular images. Some pairs showed images taken in VL, and some showed images taken in NIR light. Participants labeled each pair as belonging to the same person or to different people. Untrained participants with limited viewing times correctly classified VL image pairs with 88% accuracy, and NIR image pairs with 79% accuracy. For comparison, we presented pairs of iris images from the same subjects. In addition, we investigated differences between performance on light and dark eyes and relative helpfulness of various features in the periocular region under different illuminations. We calculated performance of three computer algorithms on the periocular images. Performance for humans and computers was similar.
Genetic-Based Type II Feature Extraction for Periocular Biometric Recognition: Less is More

Given an image from a biometric sensor, it is important for the feature extraction module to extract an original set of features that can be used for identity recognition. This form of feature extraction has been referred to as Type I feature extraction. For some biometric systems, Type I feature extraction is used exclusively. However, a second form of feature extraction does exist and is concerned with optimizing/minimizing the original feature set given by a Type I feature extraction method. This second form of feature extraction has been referred to as Type II feature extraction (feature selection). In this paper, we present a genetic-based Type II feature extraction system, referred to as GEFE (Genetic & Evolutionary Feature Extraction), for optimizing the feature sets returned by Loocal Binary Pattern Type I feature extraction for periocular biometric recognition. Our results show that not only does GEFE dramatically reduce the number of features needed but the evolved features sets also have higher recognition rates.
Periocular region appearance cues for biometric identification

We evaluate the utility of the periocular region appearance cues for biometric identification. Even though periocular region is considered to be a highly discriminative part of a face, its utility as an independent modality or as a soft biometric is still an open ended question. It is our goal to establish a performance metric for the periocular region features so that their potential use in conjunction with iris or face can be evaluated. In this approach, we employ the local appearance based feature representation, where the image is divided into spatially salient patches, and histograms of texture and color are computed for each patch. The images are matched by computing the distance between the corresponding feature representations using various distance metrics. We report recognition results on images captured in the visible and near-infrared (NIR) spectrum. For the color periocular region data consisting of about 410 subjects and the NIR images of 85 subjects, we obtain the Rank-1 recognition rate of 91% and 87% respectively. Furthermore, we also demonstrate that recognition performance of the periocular region images is comparable to that of face.
Performance evaluation of local appearance based periocular recognition

The human periocular region is known to be one of the most discriminative regions of a face image, and recent studies have indicated its potential as a biometric trait. However, the bulk of the previous work concerning the periocular region consists of feasibility studies that report recognition results on controlled data, and lacks rigorous performance evaluation, thus leaving various open questions regarding the effectiveness of periocular region as a biométrie modality. In this paper we present a performance evaluation of a local periocular texture based recognition approach. Specifically, the paper investigates the effect of input image quality on recognition performance, the uniqueness of texture between different color channels, and texture information present in different color channels. Recognition results of periocular texture features are compared to those of full face texture features and suggest that periocular texture features are robust to varying image quality.
Non-ideal iris segmentation using graph cuts

A non-ideal iris segmentation approach using graph cuts is presented. Unlike many existing algorithms for iris localization which extensively utilize eye geometry, the proposed approach is predominantly based on image intensities. In a step-wise procedure, first eyelashes are segmented from the input images using image texture, then the iris is segmented using grayscale information, followed by a post-processing step that utilizes eye geometry to refine the results. A preprocessing step removes specular reflections in the iris, and image gradients in a pixel neighborhood are used to compute texture. The image is modeled as a Markov random field, and a graph cut based energy minimization algorithm  is used to separate textured and untextured regions for eyelash segmentation, as well as to segment the pupil, iris, and background using pixel intensity values. The algorithm is automatic, unsupervised, and efficient at producing smooth segmentation regions on many non-ideal iris images. A comparison of the estimated iris region parameters with the ground truth data is provided.
Eyebrow shape-based features for biometric recognition and gender classification: A feasibility study

A wide variety of applications in forensic, government, and commercial fields require reliable personal identification. However, the recognition performance is severely affected when encountering non-ideal images caused by motion blur, poor contrast, various expressions, or illumination artifacts. In this paper, we investigated the use of shape-based eyebrow features under non-ideal imaging conditions for biometric recognition and gender classification. We extracted various shape-based features from the eyebrow images and compared three different classification methods: Minimum Distance Classifier (MD), Linear Discriminant Analysis Classifier (LDA) and Support Vector Machine Classifier (SVM). The methods were tested on images from two publicly available facial image databases: The Multiple Biometric Grand Challenge (MBGC) database and the Face Recognition Grand Challenge (FRGC) database. Obtained recognition rates of 90% using the MBGC database and 75% using the FRGC database as well as gender classification recognition rates of 96% and 97% for each database respectively, suggests the shape-based eyebrow features maybe be used for biometric recognition and soft biometric classification.
A comparison of GEC-based feature selection and weighting for multimodal biometric recognition

In this paper, we compare the performance of a Steady-State Genetic Algorithm (SSGA) and an Estimation of Distribution Algorithm (EDA) for multi-biometric feature selection and weighting. Our results show that when fusing face and periocular modalities, SSGA-based feature weighting (GEFeWSSGA) produces higher average recognition accuracies, while EDA-based feature selection (GEFeSEDA) performs better at reducing the number of features needed for recognition.
Combination of Video Change Detection Algorithms by Genetic Programming

Within the field of computer vision, change detection algorithms aim at automatically detecting significant changes occurring in a scene by analyzing the sequence of frames in a video stream. In this paper we investigate how state-of-the-art change detection algorithms can be combined and used to create a more robust algorithm leveraging their individual peculiarities. We exploited genetic programming (GP) to automatically select the best algorithms, combine them in different ways, and perform the most suitable post-processing operations on the outputs of the algorithms. In particular, algorithms’ combination and post-processing operations are achieved with unary, binary and  ${n}$ -ary functions embedded into the GP framework. Using different experimental settings for combining existing algorithms we obtained different GP solutions that we termed In Unity There Is Strength. These solutions are then compared against state-of-the-art change detection algorithms on the video sequences and ground truth annotations of the ChangeDetection.net 2014 challenge. Results demonstrate that using GP, our solutions are able to outperform all the considered single state-of-the-art change detection algorithms, as well as other combination strategies. The performance of our algorithm are significantly different from those of the other state-of-the-art algorithms. This fact is supported by the statistical significance analysis conducted with the Friedman test and Wilcoxon rank sum post-hoc tests.
Quantitative review of local descriptors for visual search

Local Visual detectors and descriptors have been studied for many years, but their applications, for example on visual search, for large volume, low cost, embedded systems have been limited or negligible to date. One reason is the lack of an industry standard applicable at worldwide level.  Descriptors for Visual Search (CDVS) working group is filling the gap by defining a highly performing extraction stage and the bitstream syntax at its output. As a contribution to such process, this work reviews existing methods for interest point detection and description. They are compared on several databases, exploiting the pairwise matching test environment devised by the MPEG CDVS standardization group.
Semantic 3D Face Mesh Simplification for Transmission and Visualization

Three-dimensional data generated from range scanners is usually composed of a huge amount of information. Simplification and compression techniques must be adopted in order to reduce transmission or processing time and to allow real-time visualization. In this paper we propose an approach to semantic simplification of triangular meshes representing faces. The algorithm is aimed to preserve facial features and can be used especially for face recognition systems purposes. In a first phase we detect salient regions using a 3D face detector based on curvature analysis and holistic classification. In a second phase, vertex decimation is applied to the mesh with different decimation parameters for salient and non salient-regions. We have tested our algorithm on a set of 150 acquisitions obtaining good visual quality meshes with approximately 90% or more of decimated vertexes
Color correction for digital photographs

The paper describes a reliable and rapid method for detecting and removing a color cast (i.e. a superimposed dominant color) in a digital image without any a priori knowledge of its semantic content. A multi-step algorithm classifies the input images as having no cast, evident cast, ambiguous cast, or intrinsic cast (images presenting a cast due to a predominant color that must be preserved). If an evident or ambiguous cast is found, a cast remover step, a modified version of the white balance algorithm, is then applied in the two cases of evident or ambiguous casts. The method we propose has been tuned and tested with positive results on a data set of over 650 images.
Computational color constancy

Computational color constancy aims to estimate the actual color in an acquired scene disregarding its illuminant. Many illuminant estimation solutions have been proposed in the last few years, although it is known that the problem addressed is actually ill-posed as its solution lacks uniqueness and stability. To cope with this problem, different solutions usually exploit some assumptions about the statistical properties of the expected illuminants and/or of the object reflectances in the scene. In this keynote we briefly review state of the art methods and illustrate our recent research on classification-based color constancy, where automatically extracted low level features are used to drive the selection and combination of the best algorithm(s) for each image. We describe how the problem of illuminant estimation and correction is deeply intertwined with the one of color space transformation. Finally, we also highlight research trends in these fields.
Color constancy using faces

In this work, we investigate how illuminant estimation can be performed exploiting the color statistics extracted from the faces automatically detected in the image. The proposed method is based on two observations: first, skin colors tend to form a cluster in the color space, making it a cue to estimate the illuminant in the scene; second, many photographic images are portraits or contain people. The proposed method has been tested on a public dataset of images in RAW format, using both a manual and a real face detector. Experimental results demonstrate the effectiveness of our approach. The proposed method can be directly used in many digital still camera processing pipelines with an embedded face detector working on gray level images.
Multiresolution wavelet transform and supervised learning for content-based image retrieval

We focus on the definition of an effective strategy that allows the user to pose a visual query and retrieve a set of images from a database that satisfy his criteria of pictorial similarity without requiring any semantic expression of them. The strategy exploits a multiresolution wavelet transform to effectively describe image content. The salient features of the images are coded in signatures of predefined lengths which are compared in the retrieval phase by applying a similarity measure the system has pre-learned, using a regression model for ordinal responses, from a learning set of "very similar", "rather-similar", "not-very-similar", and "different" pairs of images. Some experimental results demonstrating the effectiveness of this approach are reported.
Adaptive Color Constancy Using Faces

In this work we design an adaptive color constancy algorithm that, exploiting the skin regions found in faces, is able to estimate and correct the scene illumination. The algorithm automatically switches from global to spatially varying color correction on the basis of the illuminant estimations on the different faces detected in the image. An extensive comparison with both global and local color constancy algorithms is carried out to validate the effectiveness of the proposed algorithm in terms of both statistical and perceptual significance on a large heterogeneous data set of RAW images containing faces.
Adaptive Skin Classification Using Face and Body Detection

In this paper, we propose a skin classification method exploiting faces and bodies automatically detected in the image, to adaptively initialize individual ad hoc skin classifiers. Each classifier is initialized by a face and body couple or by a single face, if no reliable body is detected. Thus, the proposed method builds an ad hoc skin classifier for each person in the image, resulting in a classifier less dependent from changes in skin color due to tan levels, races, genders, and illumination conditions. The experimental results on a heterogeneous data set of labeled images show that our proposal outperforms the state-of-the-art methods, and that this improvement is statistically significant.
Adaptive edge enhancement using a neurodynamical model of visual attention

A new approach for selective edge enhancement using unsharp masking is presented. This is based on the premise that biological vision and image reproduction share common principles. In the traditional approach the high frequency components of the image are emphasized, adding to the signal a constant fraction of its high-pass filtered version. The presence of a linear high-pass filter makes the system extremely sensitive to noise. In our approach, the high frequencies added to input image are weighted by a topographic map corresponding to visually salient regions, obtained by a neurodynamical model of visual attention. In this way, the unsharp masking algorithm becomes local and adaptive, enhancing differently the edges according to human perception.
Supervised and unsupervised classification post-processing for visual video summaries

Representation of the video content using a set of key frames is one of the most common techniques for video summarization. Summaries composed of key frames allow users to grasp the overall content of a video, and access specific sequences. The post-processing algorithm presented in this paper makes it possible to create visual video summaries that are exhaustive, but not redundant, in three steps the method removes meaningless key frames, groups the key frames into clusters to allow multi-level summary presentation, and determines the default summary level. The algorithm utilizes both supervised and unsupervised classification strategies to perform these tasks. It requires no previous knowledge about the video contents, nor is any assumption made about the input data.