nils-herrmann/clean_scirepeval huggingface dataset:
An Analysis of the Sensitivity of Active Shape Models to Initialization When Applied to Automatic Facial Landmarking

Active Shape Models (ASMs) have recently gained popularity for performing automatic facial landmark fitting. Their demonstrated ability to generalize and fit unseen faces make them ideal candidates for this task unlike the traditional Active Appearance Model (AAM)-based approaches, which have difficulty in accurately landmarking unseen images. Given a test image, a face detector is used to determine the locations, orientations and sizes of faces in the image. Facial landmarking algorithms, such as ASMs, are initialized based on these parameters. In this paper, we conduct a series of experiments to exhaustively evaluate the tolerance of three popular ASMs to initialization perturbations (translation, rotation, and scaling in size) of the face detected, a topic that has not been analyzed in depth to date. Our results are consistent across different databases, provide an understanding of the role initialization plays in the landmark fitting process and serve as a performance gauge that could be considered when comparing facial landmarking algorithms.
Analyzing Facial Images using Empirical Mode Decomposition for Illumination Artifact Removal and Improved Face Recognition

A popular modality of biometrics, facial recognition is effective when used in controlled environments as in those situations where factors such as camera position, facial expression, and illumination effects are either completely or partially controlled in a beneficial way. Regulation of such factors has an immediate effect on the performance of facial recognition algorithms, in particular illumination effects which can not be controlled by even the most cooperative of users, in this paper we describe a method to address illumination effects in the biometric modality of face recognition using the signal processing analysis tool of empirical mode decomposition (EMD) to decompose images into their intrinsic mode function that correspond to the dominant illumination factors. Using these illumination modes we reconstruct the facial image without these illumination distortion components to synthesize a more illumination neutral facial image. We then perform verification experiments using algorithms such as principal component analysis (PCA), Fisher linear discriminant analysis (FEDA), and advanced correlation filters (ACF's) to demonstrate the fundamental effectiveness of EMD as an illumination compensation method. Results are reported on the Carnegie Mellon University pose-illumination-expression (CMU PIE) database.
A facial aging approach to identification of identical twins

Biometric recognition based on the characteristics of human faces has attracted a great deal of attention over the past few years. However, the similarity in the facial appearance of identical twins has made the task difficult and has even compromised commercial face recognition systems. In this paper, we shed new light on the study of facial recognition of identical twins and propose a novel approach using twins group classification and facial aging features to tell them apart. Our experiments, conducted on the University of Notre Dame ND-twins database, that was acquired over two years (2009 and 2010), indicate that our proposed approach demonstrates good generalization ability and high identification rates.
Face Pose Correction With Eyeglasses and Occlusions Removal

This paper presents how to remove unwanted occlusions such as eyeglasses in face images. By choosing training images carefully, we can derive a set of basis vectors that can eliminate this impediment for processing face images. In order to handle different poses, we apply multi-view active appearance models (MVAAMs) for fitting and then convert non-frontal images into frontal neutral faces. A set of these frontal-neutral faces is chosen for the basis and used for reconstructing other face images with occlusions. In addition, we are able to correct missing features while converting into frontal faces. The corrected faces are used for inputting to a frontal based face recognition system which can handle non-frontal faces efficiently.
Simultaneous forgery identification and localization in paintings using advanced correlation filters

With the availability of high resolution digital technology, there has been increased interest in developing statistical and image processing techniques that can enhance the existing capabilities of analyzing works of art for authenticity. This work explores the merits of using advanced correlation filters in supplementing art experts efforts in identifying forgeries among disputed paintings. We show that by training the optimal trade-off synthetic discriminant function (OTSDF) filter on each section of a coarsely parceled image of an original painting, we are not only able to distinguish between a low-quality digitized representation of a painting and its forgery, but also specifically indicate where the differences occur and where the replica is particularly faithful to the original. This method is also valuable in determining whether an original painting has undergone any modifications, given that a representation of the initial version is available.
Investigating age invariant face recognition based on periocular biometrics

In this paper, we will present a novel framework of utilizing periocular region for age invariant face recognition. To obtain age invariant features, we first perform preprocessing schemes, such as pose correction, illumination and periocular region normalization. And then we apply robust Walsh-Hadamard transform encoded local binary patterns (WLBP) on preprocessed periocular region only. We find the WLBP feature on periocular region maintains consistency of the same individual across ages. Finally, we use unsupervised discriminant projection (UDP) to build subspaces on WLBP featured periocular images and gain 100% rank-1 identification rate and 98% verification rate at 0.1% false accept rate on the entire FG-NET database. Compared to published results, our proposed approach yields the best recognition and identification results.
Dynamic three-bin real AdaBoost using biased classifiers: An application in face detection

In this paper, we briefly review AdaBoost and expand on the Discrete version by building weak classifiers from a pair of biased classifiers which enable the weak classifier to abstain from classifying some samples. We show that this approach turns into a 3-bin Real AdaBoost approach where the bin sizes and positions are set by the bias parameters selected by the user and dynamically change with every iteration which make it different from the traditional Real AdaBoost. We apply this method to face detection more specifically the Viola-Jones approach to detecting faces with Haar-like features and empirically show that our method can help improving the generalization ability by reducing the testing error of the final classifier. We benchmark the results on the MIT+CMU database.
Towards a deep learning framework for unconstrained face detection

Robust face detection is one of the most important preprocessing steps to support facial expression analysis, facial landmarking, face recognition, pose estimation, building of 3D facial models, etc. Although this topic has been intensely studied for decades, it is still challenging due to numerous variants of face images in real-world scenarios. In this paper, we present a novel approach named Multiple Scale Faster Region-based Convolutional Neural Network (MS-FRCNN) to robustly detect human facial regions from images collected under various challenging conditions, e.g. large occlusions, extremely low resolutions, facial expressions, strong illumination variations, etc. The proposed approach is benchmarked on two challenging face detection databases, i.e. the Wider Face database and the Face Detection Dataset and Benchmark (FDDB), and compared against recent other face detection methods, e.g. Two-stage CNN, Multi-scale Cascade CNN, Faceness, Aggregate Chanel Features, HeadHunter, Multi-view Face Detection, Cascade CNN, etc. The experimental results show that our proposed approach consistently achieves highly competitive results with the state-of-the-art performance against other recent face detection methods.
Gaussian Mixture Models based on the Phase Spectra for Illumination Invariant Face Identification on the Yale Database

The appearance of a face is severely altered by illumination conditions that makes automatic face recognition a challenging task. In , we introduced an illumination- invariant face identification method based on Gaussian Mixture Models (GMM) and the phase spectra of the Fourier Transform of images. In this paper we explore the application of this identification scheme on the Yale database that contains images with a greater degree of illumination variations. The novelty of our approach is that the model is able to capture the illumination variations so aptly that it yields satisfactory results without an illumination normalization unlike most existing methods. Identification based on a MAP estimate achieves misclassitication error rate of 3.5% and a low verification rate of 0.4% on this database with 10 people and 64 different illumination conditions. Both these sets of results are significantly better than those obtained from traditional PCA and LDA classifiers. We next show that upon illumination normalization, our method succeeds in attaining near-perfect results using the reconstructed images. A rigorous comparison with existing state-of-the-art approaches demonstrates that our proposed technique outperforms all of those. Furthermore, some statistical analyses pertaining to Bayesian model selection and large-scale performance evaluation based on random effects model are included.
Eigenphases vs eigenfaces

In this paper, we present a novel method for performing robust illumination-tolerant and partial face recognition that is based on modeling the phase spectrum of face images. We perform principal component analysis in the frequency domain on the phase spectrum of the face images and we show that this improves the recognition performance in the presence of illumination variations dramatically compared to normal eigenface method and other competing face recognition methods such as the illumination subspace method and fisherfaces. We show that this method is robustly even when presented with partial views of the test faces, without performing any pre-processing and without needing any a-priori knowledge of the type or part of face that is occluded or missing. We show comparative results using the illumination subset of CMU-PIE database consisting of 65 people showing the performance gain of our proposed method using a variety of training scenarios using as little as three training images per person. We also present partial face recognition results that obtained by synthetically blocking parts of the face of the test faces (even though training was performed on the full face images) showing gain in recognition accuracy of our proposed method.
Weakly Supervised Facial Analysis with Dense Hyper-Column Features

Weakly supervised methods have recently become one of the most popular machine learning methods since they are able to be used on large-scale datasets without the critical requirement of richly annotated data. In this paper, we present a novel, self-taught, discriminative facial feature analysis approach in the weakly supervised framework. Our method can find regions which are discriminative across classes yet consistent within a class and can solve many face related problems. The proposed method first trains a deep face model with high discriminative capability to extract facial features. The hypercolumn features are then used to give pixel level representation for better classification performance along with discriminative region detection. In addition, calibration approaches are proposed to enable the system to deal with multi-class and mixed-class problems. The system is also able to detect multiple discriminative regions from one image. Our uniform method is able to achieve competitive results in various face analysis applications, such as occlusion detection, face recognition, gender classification, twins verification and facial attractiveness analysis.
Robust Hand Detection and Classification in Vehicles and in the Wild

Robust hand detection and classification is one of the most crucial pre-processing steps to support human computer interaction, driver behavior monitoring, virtual reality, etc. This problem, however, is very challenging due to numerous variations of hand images in real-world scenarios. This work presents a novel approach named Multiple Scale Region-based Fully Convolutional Networks (MSRFCN) to robustly detect and classify human hand regions under various challenging conditions, e.g. occlusions, illumination, low-resolutions. In this approach, the whole image is passed through the proposed fully convolutional network to compute score maps. Those score maps with their position-sensitive properties can help to efficiently address a dilemma between translation-invariance in classification and detection. The method is evaluated on the challenging hand databases, i.e. the Vision for Intelligent Vehicles and Applications (VIVA) Challenge, Oxford hand dataset and compared against various recent hand detection methods. The experimental results show that our proposed MS-FRCN approach consistently achieves the state-of-the-art hand detection results, i.e. Average Precision (AP) / Average Recall (AR) of 95.1% / 94.5% at level 1 and 86.0% / 83.4% at level 2, on the VIVA challenge. In addition, the proposed method achieves the state-of-the-art results for left/right hand and driver/passenger classification tasks on the VIVA database with a significant improvement on AP/AR of ~7% and ~13% for both classification tasks, respectively. The hand detection performance of MS-RFCN reaches to 75.1% of AP and 77.8% of AR on Oxford database.
DeepGender: Occlusion and Low Resolution Robust Facial Gender Classification via Progressively Trained Convolutional Neural Networks with Attention

In this work, we have undertaken the task of occlusion and low-resolution robust facial gender classification. Inspired by the trainable attention model via deep architecture, and the fact that the periocular region is proven to be the most salient region for gender classification purposes, we are able to design a progressive convolutional neural network training paradigm to enforce the attention shift during the learning process. The hope is to enable the network to attend to particular high-profile regions (e.g. the periocular region) without the need to change the network architecture itself. The network benefits from this attention shift and becomes more robust towards occlusions and low-resolution degradations. With the progressively trained CNN models, we have achieved better gender classification results on the large-scale PCSO mugshot database with 400K images under occlusion and low-resolution settings, compared to the one undergone traditional training. In addition, our progressively trained network is sufficiently generalized so that it can be robust to occlusions of arbitrary types and at arbitrary locations, as well as low resolution.
Rapid 3D face modeling using a frontal face and a profile face for accurate 2D pose synthesis

This paper proposes an efficient way of modeling 3D faces by using only two — a frontal and a profile — images. Although it is desirable to utilize only one single image for 3D face modeling, more accurate depth information can be obtained if we use a profile face image additionally. Despite this seemly straightforward task, however, no standard solutions for 3D face modeling with two images have yet been reported. To tackle this problem, in our work, we first extract facial shape information from each image and then align these two shapes in order to obtain a sparse 3D face. Then, the observed sparse 3D face is combined into generic dense depth information. By doing so, we reflect both the observed 3D sparse depth information and smooth depth changes around facial areas in our reconstructed 3D shape. Finally, the intensity of the frontal image is texture-mapped onto the reconstructed 3D shape for realistic 3D modeling. Unlike other 3D modeling methods, our proposed work is extremely fast (within a few seconds) and does not require any complex hardware settings or calibration. We illustrate our 3D modeling results by using the MPIE-database and demonstrate the effectiveness of the proposed approach.
Biometric identification of cardiosynchronous waveforms utilizing person specific continuous and discrete wavelet transform features

In this paper we explore how a Radio Frequency Impedance Interrogation (RFII) signal may be used as a biometric feature. This could allow the identification of subjects in operational and potentially hostile environments. Features extracted from the continuous and discrete wavelet decompositions of the signal are investigated for biometric identification. In the former case, the most discriminative features in the wavelet space were extracted using a Fisher ratio metric. Comparisons in the wavelet space were done using the Euclidean distance measure. In the latter case, the signal was decomposed at various levels using different wavelet bases, in order to extract both low frequency and high frequency components. Comparisons at each decomposition level were performed using the same distance measure as before. The data set used consists of four subjects, each with a 15 minute RFII recording. The various data samples for our experiments, corresponding to a single heart beat duration, were extracted from these recordings. We achieve identification rates of up to 99% using the CWT approach and rates of up to 100% using the DWT approach. While the small size of the dataset limits the interpretation of these results, further work with larger datasets is expected to develop better algorithms for subject identification.
Tear-duct detector for identifying left versus right iris images

In this paper, we present different pattern recognition approaches for automatically detecting tear ducts in iris acquired eye images for enhancing iris recognition and detecting mislabeling in datasets. Detecting the tear duct in an image will tell an iris recognition system whether the presented eye image is that of a left or a right eye. This will enable the iris matcher to match the enrolled image against images in the database belonging to the same side, thus reducing the error rates by eliminating the chance of matching a left iris to a right iris or vice-versa. This is a major problem in many single iris imaging acquisition devices currently deployed in the field where the data recorded is mislabeled due to human error. We present several techniques of detecting tear ducts, including boosted Haar features, support vector machines (SVM), and more traditional approaches like PCA and LDA. Finally, we show that tear duct detection improves the detection of left/right iris recognition over previous approaches.
A Process-Tolerant Ring Modulator Based on Multi-Mode Waveguides

We present a novel 3.3-μm radius ring modulator design with dramatically reduced resonance wavelength variations. By implementing a multi-mode waveguide design for the ring waveguides, phase errors from processing non-idealities in waveguide width and etching depth are significantly reduced. Measured resonance wavelengths from four 200-mm wafers fabricated in a commercial 130-nm CMOS foundry showed a total of ~5-nm peak-to-peak variation, which is only about 1/6 of the free spectral range of the ring design. With this tighter control over the absolute resonance positions, the tuning range and tuning power requirements of the ring modulators can be significantly reduced, which in turn enhances their functionality for applications in high density, energy-efficient high-performance computing systems.
Symbol-by-symbol rate adapting wireless communication systems: architecture

In this paper, we present a symbol-based (SB), dynamic spreading gain control (DSGC) loop for multi-user CDMA wireless systems. Our SB-DSGC loop provides two advantages over other design. First, the transmitter has the flexibility to adjust spreading gain virtually at any time to maintain the fidelity of symbol error rate (SER) or bit error rate (BER) in time-varying channels. Secondary, our DSGC can have very frequent feedback the channel's state information; e.g., at (CSI) every few symbols. Moreover, it allows the station to dynamically adjust spreading gain along with other adaptive transmission resource schemes with seamless effort. One usage of our DSGC is to maintain individual link's BER in the wireless CDMA system operating in time-varying fading channels.
Can a routing protocol be truly optimal? dynamic load-balancing in wireless sensor networks

We consider the use of a novel load balancingaware routing algorithm for multipath wireless sensor networks. Focusing on solutions that minimize end-to-end delay, we optimize resource usage in mobile nodes. An analytical model is provided for flow-level multipath routing design; we then proceed to simplify the model for more practicality while retaining much of the original functionality. The practical system incorporates a load balancing-aware feedback control system based on the analytical model, allowing us to optimize traffic flow through the system. The proposed network design results in significant improvement in system delay and offered load.
Failure prediction and diagnosis for satellite monitoring systems using Bayesian networks

Predicting failure in complex systems, such as satellite network systems, is a challenging problem. A satellite earth terminal contains many components, including high-powered amplifiers, signal converters, modems, routers, and generators, any of which may cause system failure. The ability to estimate accurately the probability of failure of any of these components, given the current state of the system, may help reduce the cost of operation. Probabilistic graphical models, in particular Bayesian networks, provide a consistent framework in which to address problems containing uncertainty and complexity. Building a Bayesian network for failure prediction in a complex system such as a satellite earth terminal requires a large quantity of data. Software monitoring systems have the potential to provide vast amounts of data related to the operating state of the satellite earth terminal. Measurable nodes of the Bayesian network correspond to states of measurable parameters in the system and unmeasurable nodes represent failure of various components. Nodes for environmental factors are also included. A description of Bayesian networks will be provided and a demonstration of inference on the Bayesian network, such as the calculation of the marginal probability of failure nodes given measurements and the maximum probability state of the system for failure diagnosis will be given. Using the data to learn local probabilities of the network will be covered. An interface between MaxView monitoring and control software and a Bayesian network API will also be described.
Some simple task assignment problems for distributed networked agents

This paper formulates, in the abstract, a problem of assigning subtasks to distributed agents (processors) so that they can perform a certain global task efficiently. We model the group of networked agents by a graph that describes its communication capabilities, which is referred to as the organizational graph. In addition, we assume that the desired task-performing activity defines another graph describing the required communication. We then formulate a few variants of the problem with potentially conflicting objectives (or constraints) of load balancing and communication costs. For some of these variants, we provide algorithms that solve the assignment problem, while some others are left open.