nils-herrmann/clean_scirepeval huggingface dataset:
Longitudinal Study of Automatic Face Recognition

The two underlying premises of automatic face recognition are uniqueness and permanence. This paper investigates the permanence property by addressing the following: Does face recognition ability of state-of-the-art systems degrade with elapsed time between enrolled and query face images? If so, what is the rate of decline w.r.t. the elapsed time? While previous studies have reported degradations in accuracy, no formal statistical analysis of large-scale longitudinal data has been conducted. We conduct such an analysis on two mugshot databases, which are the largest facial aging databases studied to date in terms of number of subjects, images per subject, and elapsed times. Mixed-effects regression models are applied to genuine similarity scores from state-of-the-art COTS face matchers to quantify the population-mean rate of change in genuine scores over time, subject-specific variability, and the influence of age, sex, race, and face image quality. Longitudinal analysis shows that despite decreasing genuine scores, 99% of subjects can still be recognized at 0.01% FAR up to approximately 6 years elapsed time, and that age, sex, and race only marginally influence these trends. The methodology presented here should be periodically repeated to determine age-invariant properties of face recognition as state-of-the-art evolves to better address facial aging.
A unified transform architecture

A common architecture, which is based on the Cooley-Tukey[1] algorithm, is developed for the Fourier, Hadamard, and forward and inverse Cosine transforms. The theory to implement the first three transforms in this architecture is well known. However, the existing algorithms for the inverse Cosine transform (IDCT) would disqualify it from the unified architecture and this led us to develop a new IDCT algorithm which falls within the unified architecture if we allow ourselves the luxury of double length processing. Details of the unified architecture and the new algorithm for the IDCT will be given in this paper.
Unconstrained face recognition: Establishing baseline human performance via crowdsourcing

Research focus in face recognition has shifted towards recognition of faces “in the wild” for both still images and videos which are captured in unconstrained imaging environments and without user cooperation. Due to confounding factors of pose, illumination, and expression, as well as occlusion and low resolution, current face recognition systems deployed in forensic and security applications operate in a semi-automatic manner; an operator typically reviews the top results from the face recognition system to manually determine the final match. For this reason, it is important to analyze the accuracies achieved by both the matching algorithms (machines) and humans on unconstrained face recognition tasks. In this paper, we report human accuracy on unconstrained faces in still images and videos via crowd-sourcing on Amazon Mechanical Turk. In particular, we report the first human performance on the YouTube Faces database and show that humans are superior to machines, especially when videos contain contextual cues in addition to the face image. We investigate the accuracy of humans from two different countries (United States and India) and find that humans from the United States are more accurate, possibly due to their familiarity with the faces of the public figures in the YouTube Faces database. A fusion of recognitions made by humans and a commercial-off-the-shelf face matcher improves performance over humans alone.
Clustering Millions of Faces by Identity

Given a large collection of unlabeled face images, we address the problem of clustering faces into an unknown number of identities. This problem is of interest in social media, law enforcement, and other applications, where the number of faces can be of the order of hundreds of million, while the number of identities (clusters) can range from a few thousand to millions. To address the challenges of run-time complexity and cluster quality, we present an approximate Rank-Order clustering algorithm that performs better than popular clustering algorithms (k-Means and Spectral). Our experiments include clustering up to 123 million face images into over 10 million clusters. Clustering results are analyzed in terms of external (known face labels) and internal (unknown face labels) quality measures, and run-time. Our algorithm achieves an F-measure of 0.87 on the LFW benchmark (13 K faces of 5,749 individuals), which drops to 0.27 on the largest dataset considered (13 K faces in LFW + 123M distractor images). Additionally, we show that frames in the YouTube benchmark can be clustered with an F-measure of 0.71. An internal per-cluster quality measure is developed to rank individual clusters for manual exploration of high quality clusters that are compact and isolated.
Automatic multi-view face recognition via 3D model based pose regularization

One of the major challenges encountered by face recognition lies in the difficulty of handling arbitrary poses variations. While different approaches have been developed for face recognition across pose variations, many methods either require manual landmark annotations or assume the face poses to be known. These constraints prevent many face recognition systems from working automatically. In this paper, we propose a fully automatic method for multiview face recognition. We first build a 3D model from each frontal target face image, which is used to generate synthetic target face images. The pose of a query face image is also estimated using a multi-view face detector so that the synthetic target face images can be generated to resemble the pose variation of a query face image. Procrustes analysis is then applied to align the synthetic target images and the query image, and block based MLBP features are extracted for face matching. Experimental results on two public-domain databases (Color FERET and PubFig), and a Mobile face database collected using mobile phones show that the proposed approach outperforms two state-of-the-art face matchers (FaceVACS and MKD-SRC) in automatic multi-view face recognition. The proposed approach can also be easily extended to leverage existing face recognition systems for automatic multi-view face recognition.
Ridge-Based Fingerprint Matching Using Hough Transform

Despite the efficacy of minutia-based fingerprint matching techniques for good-quality images captured by optical sensors, minutia-based techniques do not often perform so well on poor-quality images or fingerprint images captured by small solid-state sensors. Solid-state fingerprint sensors are being increasingly deployed in a wide range of applications for user authentication purposes. Therefore, it is necessary to develop new fingerprint-matching techniques that utilize other features to deal with fingerprint images captured by solid-state sensors. This paper presents a new fingerprint matching technique based on fingerprint ridge features. This technique was assessed on the MSU-VERIDICOM database, which consists of fingerprint impressions obtained from 160 users (4 impressions per finger) using a solid-state sensor. The combination of ridge-based matching scores computed by the proposed ridge-based technique with minutia-based matching scores leads to a reduction of the false non-match rate by approximately 1.7% at a false match rate of 0.1%.
A 550 Mhz Alpha Microprocessor Targeted At PC Applications

A SS0MHz Alpha microprocessor targeted ut PC applications i s described. The chip has been fabricated in Mitsubishi's 0.3um twin-well four-layer-metal CMOS process technology. It includes 3.SM transistors i n a I .43cm2 die and is estimated t o deliver 14.3 SPECint9.5 and 17.0 SPECfp9S at S33MHz. The chip employs pipelined Synchronous Burst SRAM interface and supports MVI (Motion Video Instructions) for MPEG-2 encodeldecode. I t i s estimated to deliver 30framesls DID play-back with stereo quality audio.  This multi-media enhanced, quad-issue, 550MHz Alpha architecture microprocessor, the Alpha 21 164PCII 1, i s designed for PC applications. The design i s an extension of an existing RISC microprocessor, the Alpha 21 164[2,3]. 'The design objectives of the 21164PC were t o achieve leadership performance on a die size of less than 1.5cm2 and to realize low system cost. This would facilitate in bringing Alpha's performance to the midrange desktop PC market. In order to minimize time to market, the part of the 21 164PC design was leveraged from the 21 164. Table 1 shows the chip statistics. The availability and affordable pricing of the high performance SRAMs for building the offFigure 1 . The 21 I64PC photomicrograph C o s t and Performance Tradeoffs chip level-two (L2) cache enabled removal of on-chip 96KB L2 cache. This change reduced the chip's die area by almost origillal 21 164, the 21164PC i s able to use the Same core logic chipsets.  1 shows a photomicrograph of the Module and microprocessor costs are important factors reduce the cost of the 21 164, the design team first focused o n the die area, one of the key variable i n production cost. 30%. By keeping the bus interface wi th the for Alpha chip to penetrate volume PC market. In order to
Evidence-Based Recognition of 3-D Objects

An evidence-based recognition technique is defined that identifies 3-D objects by looking for their notable features. This technique makes use of an evidence rule base, which is a set of salient or evidence conditions with corresponding evidence weights for various objects in the database. A measure of similarity between the set of observed features and the set of evidence conditions for a given object in the database is used to determine the identity of an object in the scene or reject the object(s) in the scene as unknown. This procedure has polynomial time complexity and correctly identifies a variety of objects in both synthetic and real range images. A technique for automatically deriving the evidence rule base from training views of objects is shown to generate evidence conditions that successfully identify new views of those objects.
1.38 cm/sup 2/ 550 MHz microprocessor with multimedia extensions

This multi-media enhanced, quad-issue, 550MHz RISC microprocessor includes hardware for MPEG-II encode/decode. It delivers 30 frame/s DVD playback with stereo-quality audio. Extensions to the instruction set for motion video estimation provide an order of magnitude improvement in computational capability, enabling video teleconferencing at 30 frame/s. An on-chip cache controller supports module caches using pipelined burst SRAMs to deliver up to 3.2GB/s data. The chip supports built-in-self-test (BIST) and repair of on-chip caches to reduce test cost.
Robust Ensemble Clustering by Matrix Completion

Data clustering is an important task and has found applications in numerous real-world problems. Since no single clustering algorithm is able to identify all different types of cluster shapes and structures, ensemble clustering was proposed to combine different partitions of the same data generated by multiple clustering algorithms. The key idea of most ensemble clustering algorithms is to find a partition that is consistent with most of the available partitions of the input data. One problem with these algorithms is their inability to handle uncertain data pairs, i.e. data pairs for which about half of the partitions put them into the same cluster and the other half do the opposite. When the number of uncertain data pairs is large, they can mislead the ensemble clustering algorithm in generating the final partition. To overcome this limitation, we propose an ensemble clustering approach based on the technique of matrix completion. The proposed algorithm constructs a partially observed similarity matrix based on the data pairs whose cluster memberships are agreed upon by most of the clustering algorithms in the ensemble. It then deploys the matrix completion algorithm to complete the similarity matrix. The final data partition is computed by applying an efficient spectral clustering algorithm to the completed matrix. Our empirical studies with multiple real-world datasets show that the proposed algorithm performs significantly better than the state-of-the-art algorithms for ensemble clustering.
3D Whole Hand Targets: Evaluating Slap and Contactless Fingerprint Readers

We design and fabricate wearable whole hand 3D targets complete with four fingerprints and one thumb print for evaluating multi-finger capture devices, e.g., slap and contactless readers. We project 2D calibration patterns onto 3D finger surfaces pertaining to each of the four fingers and the thumb to synthetically generate electronic 3D whole hand targets. A state-of-the-art 3D printer is then used to fabricate physical 3D hand targets with printing materials that are similar in hardness and elasticity to the human skin and are optically compatible for imaging with a variety of fingerprint readers. We demonstrate that the physical 3D whole hand targets can be imaged using three commercial (500/1000 ppi) Appendix F certified slap fingerprint readers and a contactless reader. We further show that the features present in the 2D calibration patterns (e.g. ridge structure) are replicated with high fidelity on both the electronically generated and physically fabricated 3D hand targets. Results of evaluation experiments for the three slap readers and the contactless reader using the generated whole hand targets are also presented.
Annotating Unconstrained Face Imagery: A scalable approach

As unconstrained face recognition datasets progress from containing faces that can be automatically detected by commodity face detectors to face imagery with full pose variations that must instead be manually localized, a significant amount of annotation effort is required for developing benchmark datasets. In this work we describe a systematic approach for annotating fully unconstrained face imagery using crowdsourced labor. For such data preparation, a cascade of crowdsourced tasks are performed, which begins with bounding box annotations on all faces contained in images and videos, followed by identification of the labelled person of interest in such imagery, and, finally, landmark annotation of key facial fiducial points. In order to allow such annotations to scale to large volumes of imagery, a software system architecture is provided which achieves a sustained rate of 30,000 annotations per hour (or 500 manual annotations per minute). While previous crowdsourcing guidance described in the literature generally involved multiple choice questions or text input, our tasks required annotators to provide geometric primitives (rectangles and points) in images. As such, algorithms are provided for combining multiple annotations of an image into a single result, and automatically measuring the quality of a given annotation. Finally, other guidance is provided for improving the accuracy and scalability of crowdsourced image annotation for face detection and recognition.
Image data compression: A review

With the continuing growth of modern communications technology, demand for image transmission and storage is increasing rapidly. Advances in computer technology for mass storage and digital processing have paved the way for implementing advanced data compression techniques to improve the efficiency of transmission and storage of images. In this paper a large variety of algorithms for image data compression are considered. Starting with simple techniques of sampling and pulse code modulation (PCM), state of the art algorithms for two-dimensional data transmission are reviewed. Topics covered include differential PCM (DPCM) and predictive coding, transform coding, hybrid coding, interframe coding, adaptive techniques, and applications. Effects of channel errors and other miscellaneous related topics are also considered. While most of the examples and image models have been specialized for visual images, the techniques discussed here could be easily adapted more generally for multidimensional data compression. Our emphasis here is on fundamentals of the various techniques. A comprehensive bibliography with comments is included for a reader interested in further details of the theoretical and experimental results discussed here.
Iris recognition under alcohol influence: A preliminary study

Iris recognition has been used mainly to recognize cooperative subjects in controlled environments. With the continuing improvements in iris matching performance and reduction in the cost of iris scanners, the technology will witness broader applications and may be confronted with newer challenges. In this research, we have investigated one such challenge, namely matching iris images captured before and after alcohol consumption. Due to alcohol consumption, the pupil dilates/constricts which causes deformation in iris pattern, possibly affecting iris recognition performance. The experiments performed on the “IIITD Iris Under Alcohol Influence” database show that in matching pre and post alcohol consumption images, the overlap between genuine and impostor match score distributions increases by approximately 20%. These results on a relatively small database suggest that about one in five subjects under alcohol influence may be able to evade identification by iris recognition.
Analysis of consensus partition in cluster ensemble

In combination of multiple partitions, one is usually interested in deriving a consensus solution with a quality better than that of given partitions. Several recent studies have empirically demonstrated improved accuracy of clustering ensembles on a number of artificial and real-world data sets. Unlike certain multiple supervised classifier systems, convergence properties of unsupervised clustering ensembles remain unknown for conventional combination schemes. In this paper, we present formal arguments on the effectiveness of cluster ensemble from two perspectives. The first is based on a stochastic partition generation model related to re-labeling and consensus function with plurality voting. The second is to study the property of the "mean" partition of an ensemble with respect to a metric on the space of all possible partitions. In both the cases, the consensus solution can be shown to converge to a true underlying clustering solution as the number of partitions in the ensemble increases. This paper provides a rigorous justification for the use of cluster ensemble.
Artificial neural network for nonlinear projection of multivariate data

The authors propose a learning algorithm to train a multilayer feedforward neural network to perform the well-known Sammon nonlinear projection. The learning algorithm is an extension of the backpropagation algorithm. A significant advantage of the network-based projection over the original Sammon algorithm is that the trained network is able to project new patterns. Experimental results indicate that the projection network has good generalization capability when an appropriately sized training set and network are utilized. A lower bound for the number of free parameters required to achieve the same representation power as Shannon's algorithm is derived. This lower bound, together with the generalization capability, provides some guidelines about the size of the network that should be used.<<ETX>>
A note on the effective number of parameters in nonlinear learning systems

Moody's notion of effective number of parameters in a nonlinear learning system has been used for studying the generalization ability of feedforward neural networks. It is more meaningful than the number of free parameters in a nonlinear learning system because the former explains explicitly how the generalization error is related to the expected training set error. In this paper, we extend Moody's model to include a more general noise model. We show that the addition of noise in both sampling points in test data and observations increases the deviation of the expected test set mean-squared-error (MSE) from the expected training set MSE, and also increases the effective number of parameters. Our extension makes less restrictive assumptions about the data generation process than in the original Moody's notion. Monte Carlo experiments have been conducted to verify our extension, and to demonstrate the role of the weight-decay regularization in improving the generalization ability of feedforward networks.
Integrated segmentation and classification for automotive airbag suppression

The use of airbags into automobiles has significantly improved the safety of the occupants. Unfortunately, when airbags are deployed in the case of a crash, they can also cause fatal injuries if the occupant is a child smaller (in weight) than a typical 6 year old. In response to this, The National Highway Transportation and Safety Administration (NHTSA) has mandated that, starting in the 2006 model year, all automobiles be equipped with an automatic suppression system. These systems are supposed to suppress the airbag if a child or an infant is occupying the front passenger seat. We are investigating the use of machine vision to classify front-seat passenger occupants into four classes: (i) adult, (ii) empty, (iii) RFIS (rear facing infant seat), and child. The design and integration of such a vision system into automobiles is very difficult due to (i) occupant variability (e.g., different types of infant seats and children clothing), and (ii) extreme lighting variability (e.g., bright sunny days, and night time operation). Our approach is based on recognition-driven segmentation. Preliminary results show that by integrating the segmentation and classification stages of processing, we are able to more reliably recognize the various occupant classes.
Fingerprint Deformation Models Using Minutiae Locations and Orientations

Nonlinear deformations in fingerprint images, arising from the elasticity of the skin as well as the pressure and movement of the finger during image acquisition, lead to difficulties in establishing a match between multiple impressions acquired from the same finger. One solution to this problem is to estimate and remove the relative deformations prior to the matching stage. In this paper, these relative deformations are represented as an average deformation model based on minutiae locations and orientations using 2-D thin plate splines (TPS). The estimated average deformation is used to pre-distort a template prior to matching it with a query image in a verification task. Experimental results show that the use of minutiae locations and orientations to estimate the deformation leads to a more representative deformation model than using minutiae locations only. An index of deformation based on the bending energy is also proposed to select templates with the least variability in the deformations. The EER goes down by ap 1.1% when we incorporate minutiae orientation information and use the template selection strategy.
Segmentation and grouping of object boundaries

A method is presented to detect curvilinear structures in 2D images. The approach is based on a generalization of the Hough transform that is different from its traditional, template-based generalizations. With this method, global interaction of edge elements is reduced through the use of the Delaunay graph in utilizing the Hough transform. The algorithm as it is currently implemented needs to be extended. One suggested extension is to perform a connected component analysis of the groups obtained from current algorithm and use the distribution of the set of bins in Hough space to further group these larger components. Another extension would be to use the Hough space information to perform contour completion for occluded objects.<<ETX>>