nils-herrmann/clean_scirepeval huggingface dataset:
Triangular similarity metric learning for face verification

We propose an efficient linear similarity metric learning method for face verification called Triangular Similarity Metric Learning (TSML). Compared with relevant state-of-the-art work, this method improves the efficiency of learning the cosine similarity while keeping effectiveness. Concretely, we present a geometrical interpretation based on the triangle inequality for developing a cost function and its efficient gradient function. We formulate the cost function as an optimization problem and solve it with the advanced L-BFGS optimization algorithm. We perform extensive experiments on the LFW data set using four descriptors: LBP, OCLBP, SIFT and Gabor wavelets. Moreover, for the optimization problem, we test two kinds of initialization: the identity matrix and the WCCN matrix. Experimental results demonstrate that both of the two initializations are efficient and that our method achieves the state-of-the-art performance on the problem of face verification.
On the coding performances of DCT based subband decomposition for low contrast images

In this study, we consider the DCT based subband decomposition and show the efficiency of this approach for low contrast images in comparison to 9-7 bi-orthogonal FIR filters generally used in such a scheme. First, we consider the theoretical coding gain for the two approaches. Afterwards, the distortion versus rate performances are analyzed and the perceptual qualities of the reconstructed images are discussed. Finally, an edge detection operator is applied on the reconstructed images in order to show the robustness of DCT based subband decomposition to post-processing.
Generalization of angular radial transform

Content based shape image retrieval is an important problem which gained the attention of the community. The challenge is to map the shape into compact and robust descriptor. This study presents a generalization of the angular radial transform (ART). The ART, recommended by the MPEG-7 standard, is only limited to binary images and is not robust to perspective deformations. We propose two generalizations of the ART allowing to apply it to color images and to make it robust to all possible rotations and to perspective deformations.
Learning an Efficient and Robust Graph Matching Procedure for Specific Object Recognition

We present a fast and robust graph matching approach for 2D specific object recognition in images. From a small number of training images, a model graph of the object to learn is automatically built. It contains its local key points as well as their spatial proximity relationships. Training is based on a selection of the most efficient subgraphs using the mutual information. The detection uses dynamic programming with a lattice and thus is very fast. Experiments demonstrate that the proposed method outperforms the specific object detectors of the state-of-the-art in realistic noise conditions.
Logistic similarity metric learning for face verification

This paper presents a new method for similarity metric learning, called Logistic Similarity Metric Learning (LSML), where the cost is formulated as the logistic loss function, which gives a probability estimation of a pair of faces being similar. Especially, we propose to shift the similarity decision boundary gaining significant performance improvement. We test the proposed method on the face verification problem using four single face descriptors: LBP, OCLBP, SIFT and Gabor wavelets. Extensive experimental results on the LFW-a data set demonstrate that the proposed method achieves competitive state-of-the-art performance on the problem of face verification.
A segmentation algorithm of 3D ultrasonic data based on tissue characterization

In a previous work (D. Boukerroui et al., "Texture based adaptive clustering algorithm for 3D breast lesion segmentation", ibid., p. 1389-92, 1997), a segmentation algorithm involves 3D adaptive K-Means clustering of the gray-scale and texture features images calculated from the envelope image. The segmentation problem was formulated as a Maximum A Posterior (MAP) estimation problem. The method was demonstrated successfully on in vivo breast data with texture features calculated on the cooccurrence matrices. A major difficulty in the proposed algorithm is the choice of the texture features which characterize the different tissues. In the case of ultrasonic data, two major classes of parameters exist, acoustical and textural parameters. In this work, both acoustic and texture characterization are taken into account in the segmentation process.
A multiparametric and multiresolution segmentation algorithm of 3D ultrasonic data

An algorithm devoted to the segmentation of 3-D ultrasonic data is proposed. The algorithm involves 3-D adaptive clustering based on multiparametric information: the gray-scale intensity of the echographic data, 3-D texture features calculated from the envelope data, and 3-D tissue characterization information calculated from the local frequency spectra of the radio-frequency signals. The segmentation problem is formulated as a maximum a posterior (MAP) estimation problem. A multi-resolution implementation of the algorithm is proposed. The approach is tested on simulated data and on in vivo echocardiographic 3-D data. The results presented in the paper illustrate the robustness and the accuracy of the proposed approach for the segmentation of ultrasonic data.
An image statistics approach towards efficient and robust refinement for landmarks on facial boundary

In the real-world unconstrained face recognition scenarios, automatic facial landmarking scheme using the active shape model (ASM) usually gives non-ideal results, especially at the facial boundary. This is because the local subspace methods such as the principal component analysis (PCA) used in the ASM does not properly discern skin texture and background with very similar photometric and textual properties, thus fails to accurately locate the facial boundary. In this work, we have novelly developed a robust image statistics approach to efficiently refine the landmarks on facial boundary. Moreover, with the aid of banana wavelets to highlight the facial boundary, our proposed approach can deal with even more difficult task. This algorithm can dramatically increase the accuracy of landmarks on facial boundary for unconstrained facial images with minimum computational expense since this method is purely based on image statistics with no training stages involved at all. We have shown the effectiveness of our proposed methods on the GBU database where the refined landmarks yield much lower MSE from the ground truth.
How to Generate Spoofed Irises From an Iris Code Template

Biometrics has gained a lot of attention over recent years as a way to identify individuals. Of all biometrics-based techniques, the iris-pattern-based systems have recently shown very high accuracies in verifying an individual's identity. The premise here is that iris patterns are unique across people. Only the iris bit code template specific to an individual need be stored for future identity verification. It is generally accepted that this iris bit code is unidentifiable data. However, in this work, we explore methods to generate alternate iris textures for a given person for the purpose of bypassing a system based on this iris bit code. We show that, if this spoof texture is presented to an iris recognition system, it will generate the same score response as that of the original iris texture. Hence, this approach can bypass filter-based feature extraction systems (such as Daugman style systems) without using the actual texture of the target iris that we want to spoof, by obtaining a hamming distance match score that falls within the authentic score range. This approach assumes we know the feature extraction mechanism of the iris matching scheme. We embed features within a person's natural iris texture to spoof another person's iris. A very convincing preliminary investigation into how one can get by any iris recognition system by synthesizing various levels of “natural” looking irises is presented here and we hope to use this knowledge to build countermeasures into the feature extraction scheme of the recognition module.
Determination of pose angle of face using dynamic space warping

In this paper we consider the problem of estimating the angle of pose of a face image with respect to frontal face image. Although the pose of a person's face depend on the 3D nature of the images, we show that it is possible to derive the pose of a face even from a 2D face image. This is primarily because there are several features of the image which are constrained because of it is a face. Also the objective is only to extract the single parameter pose angle, and not to reconstruct the face image. We use the features extracted along vertical scan line of an image to derive the pose angle. Using these vertical face features the image at any given pose is matched with face features from face image with front view, which corresponds to the zero pose. The matching is accomplished using dynamic space warping which is similar to dynamic time warping (DTW) used in matching the speech spectrum of isolated word recognition. The warping path obtained using DSW can be calibrated so that from the path one can derive the pose angle. In order to obtain good/accurate estimation of the pose angle, it is useful to have the reference image at several known angles.
Estimating Mixing Factors Simultaneously in Multilinear Tensor Decomposition for Robust Face Recognition and Synthesis

Facial images change appearance due to multiple factors such as poses, lighting variations, facial expressions, etc. Tensor approach, an extension of conventional matrix, is appropriate to analyze facial factors since tensors make it possible to construct multilinear models using a multiple factor structure. We use higher-order tensors to model multiple factors of facial variations, but this provides some difficulty in use. First, it is difficult to decompose the multiple factors of a test image, especially when the factor parameters are unknown or are not in the training set. Second, for face recognition and face synthesis tasks, it is also difficult to construct reliable multilinear models which have more than two factors. In this paper, we propose a novel tensor factorization method to decompose mixing factors for unseen test images. We set up tensor factorization problem as a least squares problems with a quadratic equality constraint, and solve it using numerical optimization techniques. The novelty in our approach compared to previous work is that our tensor factorization method does not require any knowledge or assumption of test images; we can attain parameters of facial factors by our tensor factorization method. We have conducted several experiments to show the versatility of the method for both face recognition and synthesis tasks. Thus, we demonstrate the proposed method produces reliable results for trilinear models as well as bilinear models.
Individual Tensorface Subspaces for Efficient and Robust Face Recognition that do not Require Factorization

Facial images change appearance due to multiple factors such as poses, lighting variations, facial expressions, etc. Tensor approach, an extension of the conventional 2D matrix, is appropriate to analyze facial factors since tensors make it possible to construct multilinear models using multiple factor structures. However, tensor algebra provides some difficulties in practical usage. First, it is difficult to decompose the multiple factors (e.g. pose, illumination, expression) of a test image, especially when the factor parameters are unknown or are not in the training set. Second, for face recognition, as the number of factors is larger, it becomes more difficult to construct reliable multilinear models and it requires more memory and computation to build a global model. In this paper, we propose a novel Individual TensorFaces which does not require tensor factorization, a step which was necessary in previous tensorface research for face recognition. Another advantage of this individual subspace approach is that it makes the face recognition tasks computationally and analytically simpler. Based on various experiments, we demonstrate the proposed Individual TensorFaces bring better discriminant power for classification.
Minimizing the number of bits needed for iris recognition via Bit Inconsistency and GRIT

In this paper, we demonstrate how the concepts of Bit Inconsistency and Genetic Search can be used to minimize the number of iris code bits needed for iris recognition. In addition, we compare two systems: GRIT-I (Genetically Refined Iris Templates I) and GRIT-II. Our results show that GRIT-I (by evolving the bit mask of iris templates) was able to reduce the number of iris code bits needed by approximately 30% on average. GRIT-II by contrast optimizes the bit mask as well as the iris code bits that have 100% consistency and 100% coverage with respect to the training set. GRIT-II was able to reduce the number of iris code bits needed by approximately 89%.
Evaluating Active Shape Models for Eye-Shape Classification

This paper explores the goal of applying Active Shape Models (ASMs) on the eye images to classify eye shapes and identify whether the images belong from left or right irises. In many applications, particular to data collected from single eye capture devices (such as the PIER mobile iris image acquisition device), it is of importance to be able to sort and correct mislabeled collected data. ASMs have traditionally been applied for classification or identification of a wide variety of objects ranging from faces, assembly line objects to biomedical objects such as bone structures (like the spine etc). In this paper we apply and evaluate ASM models to fit on the eye shape to determine if the image belongs to a left or right eye. The approach we employ is based on building 2 ASM models, one for the left eye and one for right eye. The best fit model is chosen as the result. Our preliminary evaluation using vanilla ASM shows that preprocessing techniques like illumination compensation, shape normalization, and accurate Iris detection are key steps required to improve the classification performance.
Second-degree correlation surface features from Optimal Trade-off Synthetic Discriminant Function filters for subject identification using radio frequency cardiosynchronous waveforms

Radio Frequency Impedance Interrogation (RFII) measures hemodynamic function via resonance frequency coupling to a hydrophilic protein molecule. The RFII device generates a cardiosynchronous waveform from the identification of blood movement in the time, frequency, and voltage domains. This paper examines RFII signals with the end goal of allowing confirmation of the identity of a subject in an operational setting. An Optimal Trade-off Synthetic Discriminant Function (OT-SDF) was applied to filter the data stream for subject identification. Preliminary results using the OT-SDF Filters demonstrate 63.3% successful single-heartbeat subject identification. However, each individual's correlation surfaces appear to have a unique waveform morphology that is visually distinct from the other individuals in the data set. Improved identification was seen with second-degree correlation suggesting that a second-degree correlation may hold great potential as a biometric feature extraction identifier. We show that using correlation plane outputs as features actually provide a robust biometric identifier and significant higher identification accuracy.
Generalized Low Dimensional Feature Subspace for Robust Face Recognition on Unseen datasets using Kernel Correlation Feature Analysis

In this paper we analyze and demonstrate the subspace generalization power of the kernel correlation feature analysis (KCFA) method for producing compact low dimensional subspace that has good representation ability to work on unseen, untrained datasets. Examining the portability of an algorithm across different datasets is an important practical aspect of face recognition applications where the technology cannot be dataset-dependant in real-world practical applications. In most face recognition literature, algorithms are demonstrated on datasets by training on some part of the dataset and testing on the remainder. In general, the training and testing data have the same people but different capture sessions so essentially, some of the expected variation and people are modeled in the training set. In this paper we describe how we efficiently build a compact feature space using kernel correlation filter analysis on the generic training set of the FRGC dataset, and test the built subspace on other well-known face datasets. We show that the feature subspace produced by KCFA has good representation and discrimination to unseen datasets and produces good verification and identification rates compared to other subspace methods such as PCA. Its efficiency, lower dimensionality (the KCFA is only a 222 dimensional subspace) and discriminative power make it more practical and powerful than PCA as a powerful lower dimensionality reduction method for modeling faces and facial variations.
A novel energy based filter for cross-blink eye detection

Based on the fact that eye regions may be considered as a sequence of consecutive low and high spatial frequency regions, we propose a novel and efficient filter based on Isotropic Gaussian Energy which can be used for eye detection. When applied to facial images, the designed filter highlights the eye region with a prominent pattern, which can then be localized by template matching technique such as MACE correlation filter. The proposed filter is proved useful to detect both open and closed eyes. We demonstrate the effectiveness of the proposed filter by conducting experiments on both FERET and MBGC face databases.
Boosted multi image features for improved face detection

In this paper, we present novel approaches of automatically detecting human faces in images which is extremely important for any face recognition system. This paper expands on the traditional Viola-Jones approach by proposing to boost a plethora of mixed feature sets for face detection; we do this by adding non-Haar-like elements to a large pool of mixed features in an Adaboost framework. We show how to generate discriminative support vector machine (SVM) type features and Gabor-type features (in various orientations and frequencies and central locations) and use this whole pool as possible discriminative candidate feature sets in modeling the patterns of a frontal view human face. This general and large-diversity pool of features is used to build a boosted strong classifier and we show we can improve the generalization performance of the AdaBoost approach, and as a result improving the robustness of the face detector. We report performance on the MIT+CMU face database and compare the result with other published face detection algorithms. We also discuss processing times and speeding up methods to offset the increase in complexity in order to achieve face detection in real time.
Kernel Fukunaga-Koontz Transform Subspaces For Enhanced Face Recognition

Traditional linear Fukunaga-Koontz transform (FKT) (F. Fukunaga and W. Koontz, 1970) is a powerful discriminative subspaces building approach. Previous work has successfully extended FKT to be able to deal with small-sample-size. In this paper, we extend traditional linear FKT to enable it to work in multi-class problem and also in higher dimensional (kernel) subspaces and therefore provide enhanced discrimination ability. We verify the effectiveness of the proposed kernel Fukunaga-Koontz transform by demonstrating its effectiveness in face recognition applications; however the proposed non-linear generalization can be applied to any other domain specific problems.
IRIS super-resolution via nonparametric over-complete dictionary learning

This paper presents a novel iris super-resolution approach using a powerful nonparametric Bayesian modeling technique in the framework of sparse representation and over-complete dictionary. Far apart from previous iris super-resolution methods, our proposed approach has ability to automatically discover optimal parameter sets and optimally adapt from a given training data. Particularly, the Beta Process will be employed to build a nonparametric discriminative over-complete dictionary to represent and discriminate input samples simultaneously. Our proposed method will be evaluated on Casia iris database and compared with the linear interpolation super resolution. The result shows that our approach improves the performance of iris recognition.
Pose estimation using Spectral and Singular Value recomposition

In face recognition tasks, the changing pose of the face can cause enough information to be lost to cause the recognition to fail so being able to determine the pose of the face beforehand can allow for some better recognition performance. Many methods used for pose estimation tasks rely on finding some underlying structure of the data given to create a classifier. We propose an alternative method in which the training data itself is the underlying structure of a classifier. This is accomplished through the use of matrix decomposition equations. However, instead of decomposing a matrix, one is created by carefully selecting the terms in the decomposition equation such that the resulting matrix has the desired properties for classification. We show two recomposition methods using the Spectral Decomposition and Singular Value Decomposition equations. We show this method can perform pose estimation with a high accuracy of 85.21% and an accuracy of 98.42% when allowing a ±15° tolerance on the pose estimate on the CUbiC FacePix dataset. We also show results on both yaw and pitch estimation on the Pointing'04 dataset with our methods achieving 77.01% accuracy on yaw estimation.
Fastfood dictionary learning for periocular-based full face hallucination

The kernel trick becomes a burden for some machine learning tasks such as dictionary learning, where a huge amount of training samples are needed, making the kernel matrix gigantic and infeasible to store or process. In this work, we propose to alleviate this problem and achieve Gaussian RBF kernel expansion explicitly for dictionary learning using Fastfood transform, which is an approximation of full kernel expansion. We have shown, in the context of missing data recovery through joint dictionary learning i.e. periocular-based full face hallucination, that the approximated kernel expansion using Fastfood transform for joint dictionary learning yields much better results than its image space counterparts. Also, explicit kernel expansion through Fastfood allows us to de-kernelize the reconstructed image in the feature space back to the image space, enabling applications that require reconstructive dictionaries such as cross-domain reconstruction, image super-resolution, missing data recovery, etc.