nils-herrmann/clean_scirepeval huggingface dataset:
Elliptical Higher-Order-Spectra Periocular Code

The periocular region has recently emerged as a standalone biometric trait, promising attractive tradeoff between the iris alone and the entire face, especially for cases where neither the iris nor a full facial image can be acquired. This advantage provides another dimension for implementing a robust biometric system performed in non-ideal conditions. Global features [local binary pattern (LBP), Histogram of Gradient (HOG)] and local features have been introduced; however, the performance of these features can deteriorate for images captured in unconstrained and less-cooperative conditions. A particular set of higher order spectral (HOS) features have been proved to be invariant to translation, scale, rotation, brightness level shift, and contrast change. These properties are desirable in the periocular recognition problem to deal with the non-ideal imaging conditions. This paper investigates the HOS features in different configurations for the periocular recognition problem under non-ideal conditions. Specifically, we introduce a new sampling approach for the periocular region based on an elliptical coordinate. This non-linear sampling approach is then combined with the robustness of the HOS features for encoding the periocular region. In addition, we also propose a new technique for combining left and right perioculars. The proposed feature-level fusion approach is based on the state-of-the-art bilinear pooling technique to allow efficient interaction between the features of both perioculars. We show the validity of the proposed approach in encoding discriminant features outperforming or comparing favorably with the state-of-the-art features on the two popular data sets: Face Recognition Grand Challenge and Japanese Female Facial Expression.
Investigating the impact of drug induced pupil dilation on automated iris recognition

Recent research has demonstrated the negative impact of pupil dilation on iris recognition performance. Apart from light intensity changes, several factors such as alcohol, drugs, age, disease and psychology, are known to affect the size of the pupil. This work (1) analyzes the impact of drugs on pupil dilation, (2) proposes the use of a biomechanical nonlinear iris normalization scheme along with key point-based feature matching for mitigating the impact of drug-induced pupil dilation on iris recognition, and (3) investigates differences between drug-induced and light-induced pupil dilation on iris recognition performance. Experimental results indicate performance improvement when nonlinear normalization is used along with key point-based feature encoding for matching iris images with large differences in pupil size.
Pattern Recognition Using Invariants Defined From Higher Order Spectra- One Dimensional Inputs

A new approach to pattern recognition using invariant parameters based on higher order spectra is presented. In particular, invariant parameters derived from the bispectrum are used to classify one-dimensional shapes. The bispectrum, which is translation invariant, is integrated along straight lines passing through the origin in bifrequency space. The phase of the integrated bispectrum is shown to be scale and amplification invariant, as well. A minimal set of these invariants is selected as the feature vector for pattern classification, and a minimum distance classifier using a statistical distance measure is used to classify test patterns. The classification technique is shown to distinguish two similar, but different bolts given their one-dimensional profiles. Pattern recognition using higher order spectral invariants is fast, suited for parallel implementation, and has high immunity to additive Gaussian noise. Simulation results show very high classification accuracy, even for low signal-to-noise ratios.
Pattern recognition using invariants defined from higher order spectra: 2-D image inputs

A new algorithm for extracting features from images for object recognition is described. The algorithm uses higher order spectra to provide desirable invariance properties, to provide noise immunity, and to incorporate nonlinearity into the feature extraction procedure thereby allowing the use of simple classifiers. An image can be reduced to a set of 1D functions via the Radon transform, or alternatively, the Fourier transform of each 1D projection can be obtained from a radial slice of the 2D Fourier transform of the image according to the Fourier slice theorem. A triple product of Fourier coefficients, referred to as the deterministic bispectrum, is computed for each 1D function and is integrated along radial lines in bifrequency space. Phases of the integrated bispectra are shown to be translation- and scale-invariant. Rotation invariance is achieved by a regrouping of these invariants at a constant radius followed by a second stage of invariant extraction. Rotation invariance is thus converted to translation invariance in the second step. Results using synthetic and actual images show that isolated, compact clusters are formed in feature space. These clusters are linearly separable, indicating that the nonlinearity required in the mapping from the input space to the classification space is incorporated well into the feature extraction stage. The use of higher order spectra results in good noise immunity, as verified with synthetic and real images. Classification of images using the higher order spectra-based algorithm compares favorably to classification using the method of moment invariants.
Importance coding of surveillance imagery for interpretability using quadtree dynamic importance maps

This paper presents an importance coding technique to improve the interpretability versus bit-rate performance for an image compression system. The term interpretability is a subjective image quality measure of the content recognition performance by trained image analysts. A quadtree dynamics importance map is used to aid the prioritisation of the image encoded bit-stream according to its importance for interpretability. The importance map is generated using several bottom-up, context-free features for the identification of regions-of-interest in surveillance imagery that correlate well with visual attention processes in humans. Traditional PSNR progressive coders rely on descending wavelet coefficient magnitudes to prioritise the encoded bit-stream. The importance coder discussed in the paper is designed such that the JPEG2000 standard can implement the importance prioritisation schema (without the need to send the importance map information). Subjective evaluations indicate that the proposed schema is better than traditional PSNR progressive coders for the purpose of very low bitrate content recognition.
Face recognition from super-resolved images

Surveillance imagery is usually lower in resolution than the capabilities of hardware owing to demands on storage and processing. Additionally, a large field of view reduces the number of pixels covered by a face even further. As this number decreases, it has been observed that face recognition performance eventually degrades considerably. Super-resolution helps overcome this by fusing complimentary information from multiples frames of video to produce higher resolution images. As many existing techniques assume rigidity of objects and simple global motion between frames, their performance suffers when applied to human faces. Optical flow can be used solve this problem by generating a dense motion field to track the inter-frame motion. This paper presents a novel optical flow based super-resolution face recognition system. Results from preliminary experiments show consistent improvement in recognition performance using super-resolved images, making the system viable for use in a surveillance environment.
Face recognition using fractal codes

In this paper we propose a new method for face recognition using fractal codes. Fractal codes represent local contractive, affine transformations which when iteratively applied to range-domain pairs in an arbitrary initial image result in a fixed point close to a given image. The transformation parameters such as brightness offset, contrast factor, orientation and the address of the corresponding domain for each range are used directly as features in our method. Features of an unknown face image are compared with those pre-computed for images in a database. There is no need to iterate, use fractal neighbor distances or fractal dimensions for comparison in the proposed method. This method is robust to scale change, frame size change and rotations as well as to some noise, facial expressions and blur distortion in the image.
Techniques for improving stereo depth maps of faces

This paper presents an improved technique for determining the 3D structure of the human face form stereo images. The approach targets specific regions of the face individually. An application of the approach to estimate the 3D structure of the nose is presented. Stereo matching is extended and applied to colour in addition to grayscale, which improves matching in a low texture image like parts of the skin of a human face. Left-right consistency check is used to determine erroneous disparity estimates. Prior knowledge of the nose structure is used in conjunction to improve the quality of the 3D nose estimates.
Evaluation of Texture and Geometry for Dimensional Facial Expression Recognition

Facial expression recognition (FER) algorithms mainly focus on classification into a small discrete set of emotions or representation of emotions using facial action units (AUs). Dimensional representation of emotions as continuous values in an arousal-valence space is relatively less investigated. It is not fully known whether fusion of geometric and texture features will result in better dimensional representation of spontaneous emotions. Moreover, the performance of many previously proposed approaches to dimensional representation has not been evaluated thoroughly on publicly available databases. To address these limitations, this paper presents an evaluation framework for dimensional representation of spontaneous facial expressions using texture and geometric features. SIFT, Gabor and LBP features are extracted around facial fiducial points and fused with FAP distance features. The CFS algorithm is adopted for discriminative texture feature selection. Experimental results evaluated on the publicly accessible NVIE database demonstrate that fusion of texture and geometry does not lead to a much better performance than using texture alone, but does result in a significant performance improvement over geometry alone. LBP features perform the best when fused with geometric features. Distributions of arousal and valence for different emotions obtained via the feature extraction process are compared with those obtained from subjective ground truth values assigned by viewers. Predicted valence is found to have a more similar distribution to ground truth than arousal in terms of covariance or Bhattacharya distance, but it shows a greater distance between the means.
Shape discrimination using invariants defined from higher-order spectra

An approach to pattern recognition using invariant parameters based on higher-order spectra is presented. In particular, bispectral invariants are used to classify one-dimensional shapes. The bispectrum, which is translation invariant, is integrated along straight lines passing through the origin in bifrequency space. The phase of the integrated bispectrum is shown to be scale- and amplification-invariant. A minimal set of these invariants is selected as the feature vector for pattern classification. Pattern recognition using higher-order spectral invariants is fast, suited for parallel implementation, and works for signals corrupted by Gaussian noise. The classification technique is shown to distinguish two similar but different bolts given their one-dimensional profiles.<<ETX>>
Automatic classification of physical exercises from wearable sensors using small dataset from non-laboratory settings

Effective classification of physical exercises allows individuals to assess their levels of physical activity and functional ability for maintaining physical fitness and help reduce risks of chronic diseases. This paper investigates and compares classification techniques for detecting physical exercise in real-world contexts that often only supports a small training dataset. The system combines heart rate with other exercise-related features, such as distance, duration, calories, etc. The experiment uses a dataset of 40 realistic (uncontrolled) sessions from 22 individuals wearing wearable sensors while performing different exercises, including walking, aerobics, running, indoor cycling, and weight training. Based on a 5-fold cross validation approach, AdaBoost demonstrated the highest (87.25%) classification accuracy compared to other classifiers, including support vector machine, neural network, and binary decision tree when used individually. When fused together at the decision level using majority-voting techniques, these classifiers achieved higher accuracy (89.25%) than that of individual applications.
Time-varying bispectral analysis of auditory evoked multi-channel scalp EEG

Time-varying bispectra, computed using a classical sliding window short-time Fourier approach, are analyzed for scalp EEG potentials evoked by an auditory stimulus and new observations are presented. A single, short duration tone is presented from the left or the right, direction unknown to the test subject. The subject responds by moving the eyes to the direction of the sound. EEG epochs sampled at 200 Hz for repeated trials are processed between -70 ms and +1200 ms with reference to the stimulus. It is observed that for an ensemble of correctly recognized cases, the best matching time-varying bispectra at (8 Hz, 8Hz) are for PZ-FZ channels and this is also largely the case for grand averages but not for power spectra at 8 Hz. Out of 11 subjects, the only exception for time-varying bispectral match was a subject with family history of Alzheimer's disease and the difference was in bicoherence, not biphase.
A Virtual Test Assessment Tool for Second Year Signal Processing Students

The development and use of a virtual assessment tool for a signal processing unit is described. It allows students to take a test from anywhere using a Web browser to connect to the university server that hosts the test. While student responses are of the multiple choice type, they have to work out problems to arrive at the answer to be entered. CGI programming is used to verify student identification information and record their scores as well as provide immediate feedback after the test is complete. The tool has been used at QUT for the past 3 years and student feedback is discussed. The virtual assessment tool is an efficient alternative to marking written assignment reports that can often take more hours than actual lecture hall contact from a lecturer or tutor. It is especially attractive for very large classes that are now the norm at many universities in the first two years.
Automatic segmentation of HEp-2 cell Fluorescence microscope images using level set method via geometric active contours

A method for segmenting HEp-2 cells in Indirect Immuno Fluorescence microscope (IIF) images is implemented and evaluated. Challenges to accurate segmentation include the complexity of the data acquired at multiple wavelengths, overlapping cells, and variations in staining. Level set methods via geometric active contours are used to solve this problem. Level set methods use morphological operations to estimate an initial cell boundary and are fully automated. Geometric active contours are able to adapt to the curve topology of the cell boundary. Segmentation performance is evaluated using six indices: boundary displacement error, global consistency error, variation of information, Jaccard distance error, rand index and F-index.
Coal seam thickness estimation using GPR and higher order statistics - the near-surface case

A novel pattern recognition-based approach to detect near-surface interfaces using ground penetrating radar (GPR) has been reported in . The approach was used to successfully detect interfaces within 5 cm of the ground surface. This technique has been adapted for the important task of layer thickness estimation in the near-surface range. This is inherently a difficult problem to solve in practice because the radar echo is often dominated by unwanted components such as antenna crosstalk and ring-down, ground reflection effects and clutter. Features derived from the bispectrum and a nearest-neighbour classifier have been utilized for this processing task. It is shown that unlike traditional second order correlation based methods such as matched filtering which can fail in known conditions, layer thickness estimation using this approach can be reliably extended to the near-surface region.
Tracking people in 3D using position, size and shape

This paper presents a prototype tracking system for tracking people in enclosed indoor environments where there is a high rate of occlusions. The system uses a stereo camera for acquisition, and is capable of disambiguating occlusions using a combination of depth map analysis, a two step ellipse fitting people detection process, the use of motion models and Kalman filters and a novel fit metric, based on computationally simple object statistics. Testing shows that our fit metric outperforms commonly used position based metrics and histogram based metrics, resulting in more accurate tracking of people.
The Role of Motion Models in Super-Resolving Surveillance Video for Face Recognition

Although the use of super-resolution techniques has demonstrated the ability to improve face recognition accuracy when compared to traditional upsampling techniques, they are difficult to implement for real-time use due to their complexity and high computational demand. As a large portion of processing time is dedicated to registering the lowresolution images, many have adopted global motion models in order to improve efficiency. The drawback of such global models is that they can not accommodate for complex local motions, such as multiple objects moving independently across and static or dynamic background as frequently occurs in a surveillance environment. Local methods like optical flow can compensate for these situations, although it is achieved at the expense of computation time. In this paper, experiments have been carried out to investigate how motion models of different super-resolution reconstruction algorithms affect reconstruction error and face recognition rates in a surveillance environment. Results show that lower reconstruction error doesn't necessarily imply better recognition rates and the use of local motion models yields better recognition rates than global motion models.
Position, rotation, and scale invariant recognition of images using higher-order spectra

A new approach to recognition of images using invariant features based on higher-order spectra is presented. Higher-order spectra are translation invariant because translation produces linear phase shifts which cancel. Scale and amplification invariance are satisfied by the phase of the integral of a higher-order spectrum along a radial line in higher-order frequency space because the contour of integration maps onto itself and both the real and imaginary parts are affected equally by the transformation. Rotation invariance is introduced by deriving invariants from the Radon transform of the image and using the cyclic-shift invariance property of the discrete Fourier transform magnitude. Results on synthetic and actual images show isolated, compact clusters in feature space and high classification accuracies.<<ETX>>
Multispectral Periocular Classification With Multimodal Compact Multi-Linear Pooling

Feature-level fusion approaches for multispectral biometrics are mainly grouped into two categories: 1) concatenation and 2) elementwise multiplication. While concatenation of feature vectors has benefits in allowing all elements to interact, it is difficult to learn output classification. Differently, elementwise multiplication has the benefits in enabling multiplicative interaction, but it is difficult to learn input embedding. In this paper, we propose a novel approach to combine the benefits of both categories based on a compact representation of two feature vectors’ outer product, which is called the multimodal compact multi-linear pooling technique. We first propose to expand the bilinear pooling technique for two inputs to a multi-linear technique to accommodate for multiple inputs (multiple inputs from multiple spectra are frequent in the multispectral biometric context). This fusion approach not only allows all elements to interact and enables multiplicative interaction, but also uses a small number of parameters and low computation complexity. Based on this fusion proposal, we subsequently propose a complete multispectral periocular recognition system. Employing higher order spectra features with an elliptical sampling approach proposed by Algashaam et al., our proposed system achieves the state-of-the-art performance in both our own and the IIIT multispectral periocular data sets. The proposed approach can also be extended to other biometric modalities.
Detection of mines in acoustic images using higher order spectral features

A new pattern-recognition algorithm detects approximately 90% of the mines hidden in the Coastal Systems Station Sonar0, 1, and 3 databases of cluttered acoustic images, with about 10% false alarms. Similar to other approaches, the algorithm presented here includes processing the images with an adaptive Wiener filter (the degree of smoothing depends on the signal strength in a local neighborhood) to remove noise without destroying the structural information in the mine shapes, followed by a two-dimensional FIR filter designed to suppress noise and clutter, while enhancing the target signature. A double peak pattern is produced as the FIR filter passes over mine highlight and shadow regions. Although the location, size, and orientation of this pattern within a region of the image can vary, features derived from higher order spectra (HOS) are invariant to translation, rotation, and scaling, while capturing the spatial correlations of mine-like objects. Classification accuracy is improved by combining features based on geometrical properties of the filter output with features based on HOS. The highest accuracy is obtained by fusing classification based on bispectral features with classification based on trispectral features.
Sequential fusion of decisions with favourable dependence for controlled verification errors

Statistical dependence between classifier decisions is often shown to improve performance over statistically independent decisions. Though the solution for favourable dependence between two classifier decisions has been derived, the theoretical analysis for the general case of `n' client and impostor decision fusion has not been presented before. This paper presents the expressions developed for favourable dependence of multi-instance and multi-sample fusion schemes that employ `AND' and `OR' rules. The expressions are experimentally evaluated by considering the proposed architecture for text-dependent speaker verification using HMM based digit dependent speaker models. The improvement in fusion performance is found to be higher when digit combinations with favourable client and impostor decisions are used for speaker verification. The total error rate of 20% for fusion of independent decisions is reduced to 2.1% for fusion of decisions that are favourable for both client and impostors. The expressions developed here are also applicable to other biometric modalities, such as finger prints and handwriting samples, for reliable identity verification.