nils-herrmann/clean_scirepeval huggingface dataset:
Human Detection by Quadratic Classification on Subspace of Extended Histogram of Gradients

This paper proposes a quadratic classification approach on the subspace of Extended Histogram of Gradients (ExHoG) for human detection. By investigating the limitations of Histogram of Gradients (HG) and Histogram of Oriented Gradients (HOG), ExHoG is proposed as a new feature for human detection. ExHoG alleviates the problem of discrimination between a dark object against a bright background and vice versa inherent in HG. It also resolves an issue of HOG whereby gradients of opposite directions in the same cell are mapped into the same histogram bin. We reduce the dimensionality of ExHoG using Asymmetric Principal Component Analysis (APCA) for improved quadratic classification. APCA also addresses the asymmetry issue in training sets of human detection where there are much fewer human samples than non-human samples. Our proposed approach is tested on three established benchmarking data sets - INRIA, Caltech, and Daimler - using a modified Minimum Mahalanobis distance classifier. Results indicate that the proposed approach outperforms current state-of-the-art human detection methods.
InGaAsP–InP Avalanche Photodiodes for Single Photon Detection

In this paper, we describe the design, characterization, and modeling of InGaAsP/InP avalanche diodes designed for single photon detection at wavelengths of 1.55 and 1.06 mum. Through experimental and theoretical work, we investigate critical performance parameters of these single photon avalanche diodes (SPADs), including dark count rate (DCR), photon detection efficiency (PDE), and afterpulsing. The models developed for the simulation of device performance provide good agreement with experimental results for all parameters studied. For 1.55-mum SPADs, we report the relationship between DCR and PDE for gated mode operation under a variety of operating conditions. We also describe in detail the dependence of afterpulsing effects on numerous operating conditions, and in particular, we demonstrate and explain a universal functional form that describes the dependence of DCR on hold-off time at any temperature. For 1.06-mum SPADs, we present the experimentally determined relationship between DCR and detection efficiency for free-running operation, as well as simulations complementing the experimental data.
Fingerprint quality and validity analysis

Discusses methods to estimate the quality as well as validity of a fingerprint image. Orientation certainty is used to certify the localized texture pattern of the fingerprint images while ridge to valley structure is analyzed to detect invalid images. Global uniformity and continuity ensures that the image is valid as a whole. 150 images with various qualities are evaluated using the proposed algorithm and quality benchmark we defined. A monotonic relationship is found indicating that the proposed algorithm is feasible in detecting low quality as well as invalid fingerprint images.
Enhance deep learning performance in face recognition

Deep convolutional neural networks (CNNs) based face recognition approaches have been dominating the field. The success of CNNs is attributed to their ability to learn rich image representations. But training CNNs relies on estimating millions of parameters and requires a very large number of annotated training images. A widely-used alternative is to fine-tune the CNN that has been pre-trained using a large set of labeled images. However, we show that fine-tuning pre-trained CNNs cannot provide satisfactory face recognition performance when training and testing datasets have large differences. To address this problem, we propose to improve the face recognition performance of CNNs by using non-CNN features. Extensive experiments are conducted on LFW and FRGC databases using the pre-trained CNN model, VGG-Face. Results show that the complementary information contained in non-CNN features greatly improves the face verification rate/accuracy of CNNs on LFW and FRGC databases. Furthermore, we show that non-CNN features are more effective in enhancing the performance of pre-trained CNNs than fine-tuning.
Verification of human faces using predicted eigenvalues

To alleviate the conventional problems of LDA and its variants, we propose a procedure of predicting eigenvalues using few reliable eigenvalues from the range space. Partitioning of entire eigenspace is performed using two control points, however, the effective low dimensional discriminative vectors are extracted from the whole eigenspace. This prediction strategy enables to perform discriminant evaluation in the full eigenspace. The proposed method is evaluated and compared with 8 popular subspace based methods for face verification task. Experimental results on popular face databases show that our method consistently outperforms others.
Modular Weighted Global Sparse Representation for Robust Face Recognition

This work proposes a novel framework of robust face recognition based on the sparse representation. Image is first divided into modules and each module is processed separately to determine its reliability. A reconstructed image from the modules weighted by their reliability is formed for the robust recognition. We propose to use the modular sparsity and residual jointly to determine the modular reliability. The proposed framework advances both the modular and global sparse representation approaches, especially in dealing with disguise, large illumination variations and expression changes. Compared with the related state-of-the-art methods, experimental results on benchmark face databases verify the advancement of the proposed method.
Dimensionality reduction in subspace face recognition

Numerous face recognition algorithms use principal component analysis (PCA) as the first step for dimensionality reduction (DR) followed by linear discriminant analysis (LDA). PCA is applied in the beginning because it performs the DR in the minimum square error sense and achieves the most compact representation of data. However, they lack discrimination ability. To optimize classification, LDA and its variants are applied to the PCA reduced subspace so that the transformed data achieves minimum within-class variation and maximum between-class variations. In this paper, we study total, within-class and between-class scatter matrices and their roles in DR or feature extraction with good discrimination ability. The number of dimensions retained in DR plays a very crucial role for subsequent discriminant analysis. We reveal some important aspect of how recognition rate varies using different scatter matrices and their stepwise DR. Experimental results on popular face databases are provided to support our findings.
Two-Dimensional Polar Harmonic Transforms for Invariant Image Representation

This paper introduces a set of 2D transforms, based on a set of orthogonal projection bases, to generate a set of features which are invariant to rotation. We call these transforms Polar Harmonic Transforms (PHTs). Unlike the well-known Zernike and pseudo-Zernike moments, the kernel computation of PHTs is extremely simple and has no numerical stability issue whatsoever. This implies that PHTs encompass the orthogonality and invariance advantages of Zernike and pseudo-Zernike moments, but are free from their inherent limitations. This also means that PHTs are well suited for application where maximal discriminant information is needed. Furthermore, PHTs make available a large set of features for further feature selection in the process of seeking for the best discriminative or representative features for a particular application.
A reduced multivariate polynomial model for multimodal biometrics and classifiers fusion

The multivariate polynomial model provides an effective way to describe complex nonlinear input-output relationships since it is tractable for optimization, sensitivity analysis, and prediction of confidence intervals. However, for high-dimensional and high-order problems, multivariate polynomial regression becomes impractical due to its huge number of product terms. This is especially true for the case of a full interaction model. In this paper, we propose a reduced multivariate polynomial model to circumvent the dimensionality problem with some compromise in its approximation capability. In multimodal biometrics and many classifiers fusion applications, as individual classifiers to be combined would have attained a certain level of classification accuracy, this reduced multivariate polynomial model can be used to combine these classifiers in the next level of classification taking their outputs as the inputs to the reduced multivariate polynomial model. The model is first applied to a well-known pattern classification problem to illustrate its classification capability. The reduced multivariate polynomial model is then applied to combine two biometric verification systems with improved receiver operating characteristics performance as compared to an optimal weighing method and a few commonly used classifiers.
Fast eye localization based on pixel differences

A novel fast eye localization algorithm based on pixel differences is presented, which is suitable for face recognition system on mobile device. It is based on the fact that eyeball is dark and round. A binary eye map is obtained by choosing those pixels darker than surrounding; then it is filtered by a rank order filter; connected regions in the eye map are then labeled by their geometric centers; best suitable eyeball pair is selected based on a set of geometric constraints. If no eyeball pair is detected, the algorithm is repeated iteratively until one pair is found. The algorithm is fast since it converts the gray level image to a binary eye map at the beginning. The algorithm is tested on our own face database, which consists of 4095 images of size 250×200. Detection rate is 93.04% when the tolerance is 0.7 times of eyeball width.
Laplace gradient based Discriminative and Contrast Invertible descriptor

The performance of local descriptors such as SIFT drops under severe illumination changes. In this paper, we propose a Discriminative and Contrast Invertible (DCI) local feature descriptor. In order to increase the discriminative ability of the descriptor under illumination changes, a Laplace gradient based histogram is proposed. Moreover, a robust contrast flipping estimate is proposed based on the divergence of a local region. Experiments on fine-grained object recognition and retrieval applications demonstrate the superior performance of the DCI descriptor to others.
Deep Coupled ResNet for Low-Resolution Face Recognition

Face images captured by surveillance cameras are often of low resolution (LR), which adversely affects the performance of their matching with high-resolution (HR) gallery images. Existing methods including super resolution, coupled mappings (CMs), multidimensional scaling, and convolutional neural network yield only modest performance. In this letter, we propose the deep coupled ResNet (DCR) model. It consists of one trunk network and two branch networks. The trunk network, trained by face images of three significantly different resolutions, is used to extract discriminative features robust to the resolution change. Two branch networks, trained by HR images and images of the targeted LR, work as resolution-specific CMs to transform HR and corresponding LR features to a space where their difference is minimized. Model parameters of branch networks are optimized using our proposed CM loss function, which considers not only the discriminability of HR and LR features, but also the similarity between them. In order to deal with various possible resolutions of probe images, we train multiple pairs of small branch networks while using the same trunk network. Thorough evaluation on LFW and SCface databases shows that the proposed DCR model achieves consistently and considerably better performance than the state of the arts.
Pulsed Gating With Balanced InGaAs/InP Single Photon Avalanche Diodes

We demonstrate balanced InGaAs/InP single photon avalanche diodes operated in gated mode for a data transmission rate of up to 20 MHz. The common-mode signal cancellation realized with the balanced configuration enabled detection of small avalanche pulses. For a laser repletion rate of 20 MHz at 240 K, the dark count probability for a photon detection efficiency of 13% is <formula formulatype="inline"><tex Notation="TeX">$1.9\times 10^{-5}$</tex></formula>. The afterpulse probability is 0.3% for a 2 ns pulse width, hold-off time of 20 ns, and 10% PDE, at 240 K.
Quantized fuzzy LBP for face recognition

Face recognition under large illumination variations is challenging. Local binary pattern (LBP) is robust to illumination variation, but sensitive to noise. Fuzzy LBP (FLBP) partially solves the noise-sensitivity problem by incorporating fuzzy logic in the representation of local binary patterns. The fuzzy membership function is determined by both sign and magnitude of the pixel difference. However, the magnitude is easily altered by noise, hence could be unreliable. Thus, we propose to determine the fuzzy membership function by its sign only. We name the proposed approach as Quantized Fuzzy LBP (QFLBP). On two challenging face recognition datasets, it is shown more robust to noise, and demonstrates a superior performance to FLBP and many other LBP variants.
Generating secure cancelable fingerprint templates using local and global features

Biometric template protection is an important issue to be resolved so that practical biometric authentication systems can be deployed. In this paper, we propose a non-invertible transform to perpendicularly project the distances between a pair of minutiae to a circle to generate the features. To achieve good performance, other local features such as relative angles between the minutiae pair, and global features such as orientation, ridge frequency and total number of minutiae of the randomly sampled blocks around each minutia are also utilized. The cancelable templates are eventually generated by the proposed “Bin-based Quantization (BQ)”. Both feature extraction and cancelable template generation are governed by a secret key to guarantee revocability and security. Experimental results conducted on FVC 2002 data set demonstrate the reasonable good performance of the proposed scheme.
Quantifying image distortions using SVD

This paper describes a novel, yet effective model for automatically evaluating or quantifying the visual quality of a distorted image against its reference, based on the image statistical information. Image statistical information is embedded into the proposed model via a set of singular values, extracted using SVD, each of which characterizes the dynamics of image energy. Alterations of singular values (image energy) provide an effective indicator to quantify the degradation of image quality subjected to a wide variety of distortions. The proposed metric assesses image quality locally as well as globally and provides two different measures as outputs, i.e., graphical and numerical measures. The graphical measure, referred as distortion map, describes on how the image is deviated from its reference, through the local distribution of distortion over the whole image domain. The numerical measure is a scalar value obtained from the distortion map, which quantifies the global quality of distorted image. The proposed metric has been used to quantify the quality of natural images of various distortion types from LIVE image database, and experimental results shown justify the effectiveness and reliability of the proposed metric over the exiting SVD-based metrics.
QSSB mixer design for MB-OFDM UWB frequency synthesizer

Two Quadrature Single Side Band Mixers based on Gilbert-Cell are designed for multi-band OFDM (Orthogonal Frequency Division Multilplexing) Ultra Wide Band(UWB) applications. The sideband suppression is about -56 dBc for 3.432 GHz and -48 dBc for 3.96 GHz Hz and -43 dBc for 4.488 GHz in the upper QSSB Mixer while -50 dBc for 792 MHz in the lower QSSB with 16 mA from a 1.8 V power supply, utilizing JAZZ 0.18 mum CMOS RF process. The performance of the QSSB Mixers meets the requirement of the system specifications. The QSSB Mixers are under-test.
InP-Based Single-Photon Detectors and Geiger-Mode APD Arrays for Quantum Communications Applications

To meet the increasing demand from quantum communications and other photon starved applications, we have developed various InP-based single-photon detectors, including discrete single-photon avalanche diodes (SPADs), negative feedback avalanche diodes (NFADs), and Geiger-mode avalanche photodiode (GmAPD) arrays. A large quantity of InP SPADs have been fabricated. Out of 1000 devices with a 25-μm active area diameter, operated under gated mode at temperature of 233 K, with a pulse repetition rate of 1 MHz and pulse width of 1 ns, the average dark count rate and afterpulsing probability are 30 kHz and 8 × 10-5, respectively. Smaller (16-μm active area diameter) and larger (40-μm active area diameter) discrete devices have been fabricated as well, and their performances are presented along with the 25-μm diameter devices. NFAD devices can operate in free running mode and photon detection efficiency of 10-15% can be achieved without applying any hold-off time externally. When the temperature decreases from 240 to 160 K, the noise equivalent power (NEP) decreasesfrom1.9 × 10-16 to 1.8 × 10-18WHz-1/2, with the activation energy being 0.2 eV. The very low NEP at 160 K makes NFAD devices an ideal choice for long distance, entanglement-based quantum key distributions. GmAPD arrays provide an enabling technology for many active optical applications, such as 3-D laser detection and ranging (LADAR) and photon starved optical communications. Both 32 × 32 and 128 × 32 GmAPD arrays have been fabricated with high performance and good uniformity. GmAPD focal plane arrays (FPAs) with framed readout mode have enabled very high-performance flash LADAR systems. GmAPD FPAs with asynchronous readout mode will enable high rate quantum key distributions and other quantum communications applications.
Sound-Event Classification Using Robust Texture Features for Robot Hearing

Sound-event classification often utilizes time–frequency analysis, which produces an image-like spectrogram. Recent approaches such as spectrogram image features and subband power distribution image features extract the image local statistics such as mean and variance from the spectrogram. They have demonstrated good performance. However, we argue that such simple image statistics cannot well capture the complex texture details of the spectrogram. Thus, we propose to extract the local binary pattern (LBP) from the logarithm of the Gammatone-like spectrogram. However, the LBP feature is sensitive to noise. After analyzing the spectrograms of sound events and the audio noise, we find that the magnitude of pixel differences, which is discarded by the LBP feature, carries important information for sound-event classification. We thus propose a multichannel LBP feature via pixel difference quantization to improve the robustness to the audio noise. In view of the differences between spectrograms and natural images, and the reliability issues of LBP features, we propose two projection-based LBP features to better capture the texture information of the spectrogram. To validate the proposed multichannel projection-based LBP features for robot hearing, we have built a new sound-event classification database, the NTU-SEC database, in the context of social interaction between human and robot. It is publicly available to promote research on sound-event classification in a social context. The proposed approaches are compared with the state of the art on the RWCP database and the NTU-SEC database. They consistently demonstrate superior performance under various noise conditions.
Improved Bayesian Approach for Face Recognition

In subspace face recognition, PCA, LDA and Bayesian are the most commonly used methods. Each of them has their own advantages and disadvantages in recognizing human faces. Their recognition rates depend much on the methodologies used in selecting/transforming the eigenvectors using the eigenvalues obtained from the face subspaces. In this paper we compare all these three methods and propose a new methodology for selecting the eigenvalues in the face subspace which can be used for measuring the residual reconstruction error in the partial Karhunen-Loeve transformation (KLT) for Bayesian face recognition. We compare the recognition performances of all these methods on FERET image database on various image sizes. Experimental results using a large set of faces-2388 images drawn from 1194 subjects separated into training, gallery and probe datasets show that our proposed method consistently improves the performance over the Bayesian, LDA and PCA approaches
Relaxed local ternary pattern for face recognition

Local binary pattern (LBP) is sensitive to noise. Local ternary pattern (LTP) partially solves this problem by encoding the small pixel difference into a third state. The small pixel difference may be easily overwhelmed by noise. Thus, it is difficult to precisely determine its sign and magnitude. In this paper, we propose the concept of uncertain state to encode the small pixel difference. We do not care its sign and magnitude, and encode it as both 0 and 1 with equal probability. The proposed Relaxed LTP is tested on the CMU-PIE database, the extended Yale B database and the O2FN mobile face database. Superior performance is demonstrated compared with LBP and LTP.
A novel rank order LoG filter for interest point detection

This paper proposes a novel non-linear filter, named rank order LoG (ROLG) filter, and a new interest point detector, named ROLG detector. The ROLG filter is a weighted rank order filter. It is used to detect image structures whose significant majority of pixels are brighter (or darker) than the significant majority of pixels in their corresponding surroundings. The ROLG detector is built on this filter. Compared to linear filter based detectors, the proposed rank order filter based detector is more robust to abrupt variations of images. Experiments on the benchmark databases demonstrate that the ROLG detector achieves superior performance compared to four state-of-the-art detectors. Evaluation experiments are also conducted on face recognition. The results further demonstrate that the ROLG detector has better performance compared to other detectors.
Iterative Truncated Arithmetic Mean Filter and Its Properties

The arithmetic mean and the order statistical median are two fundamental operations in signal and image processing. They have their own merits and limitations in noise attenuation and image structure preservation. This paper proposes an iterative algorithm that truncates the extreme values of samples in the filter window to a dynamic threshold. The resulting nonlinear filter shows some merits of both the fundamental operations. Some dynamic truncation thresholds are proposed that guarantee the filter output, starting from the mean, to approach the median of the input samples. As a by-product, this paper unveils some statistics of a finite data set as the upper bounds of the deviation of the median from the mean. Some stopping criteria are suggested to facilitate edge preservation and noise attenuation for both the long- and short-tailed distributions. Although the proposed iterative truncated mean (ITM) algorithm is not aimed at the median, it offers a way to estimate the median by simple arithmetic computing. Some properties of the ITM filters are analyzed and experimentally verified on synthetic data and real images.