This paper presents a novel approach to one-class classifier fusion using locally adaptive learning with dynamic ℓ p-norm constraints. Our framework dynamically adjusts fusion weights based on local data characteristics, addressing key challenges in ensemble-based anomaly detection. By incorporating an interior-point optimization technique, our method significantly improves computational efficiency over traditional Frank-Wolfe approaches, achieving up to 19× speed gains in complex scenarios. We evaluate the framework on UCI benchmark datasets and robotics-related temporal sequence datasets, demonstrating superior performance across diverse anomaly types. Statistical validation via Skillings-Mack tests confirms significant advantages over existing methods, consistently achieving top rankings in both pure and non-pure learning scenarios. The framework’s ability to adapt to local data patterns while remaining computationally efficient makes it particularly valuable for real-time anomaly detection applications.

Using a variable ℓ p ≥ 1 -norm penalty on the slacks, the recently introduced ℓ p -norm Support Vector Data Description ( ℓ p -SVDD) method has improved the performance in novelty detection over the baseline approach, sometimes remarkably. This work extends this modelling formalism in multiple aspects. First, a large-margin extension of the ℓ p -SVDD method is formulated to enhance generalisation capability by maximising the margin between the positive and negative samples. Second, based on the Frank–Wolfe algorithm, an efficient yet effective method with predictable accuracy is presented to optimise the convex objective function in the proposed method. Finally, it is illustrated that the proposed approach can effectively benefit from a multiple kernel learning scheme to achieve state-of-the-art performance. The proposed method is theoretically analysed using Rademacher complexities to link its classification error probability to the margin and experimentally evaluated on several datasets to demonstrate its merits against existing methods.

The paper studies face spoofing, a.k.a. presentation attack detection (PAD) in the demanding scenarios of unknown attacks. While earlier studies have revealed the benefits of ensemble methods, and in particular, a multiple kernel learning (MKL) approach to the problem, one limitation of such techniques is that they treat the entire observation space similarly and ignore any variability and local structure inherent to the data. This work studies this aspect of face presentation attack detection with regards to one-class multiple kernel learning to benefit from the intrinsic local structure in bona fide samples to adaptively weight each representation in the composite kernel. More concretely, drawing on the one-class Fisher null formalism, we formulate a convex localised multiple kernel learning algorithm by regularising the collection of local kernel weights via a joint matrix-norm constraint and infer locally adaptive kernel weights for zero-shot one-class unseen attack detection. We present a theoretical study of the proposed localised MKL algorithm using Rademacher complexities to characterise its generalisation capability and demonstrate its advantages over some other options. An assessment of the proposed approach on general object image datasets illustrates its efficacy for anomaly and novelty detection while the results of the experiments on face PAD datasets verify its potential in detecting unknown/unseen face presentation attacks.

We address the one-class classification (OCC) problem and advocate a one-class MKL (multiple kernel learning) approach for this purpose. To this aim, based on the Fisher null-space OCC principle, we present a multiple kernel learning algorithm where an $\ell _{p}$ -norm regularisation ( $p \geq 1$ ) is considered for kernel weight learning. We cast the proposed one-class MKL problem as a min-max saddle point Lagrangian optimisation task and propose an efficient approach to optimise it. An extension of the proposed approach is also considered where several related one-class MKL tasks are learned concurrently by constraining them to share common weights for kernels. An extensive evaluation of the proposed MKL approach on a range of data sets from different application domains confirms its merits against the baseline and several other algorithms.

We address the face presentation attack detection problem in the challenging conditions of an unseen attack scenario where the system is exposed to novel presentation attacks that were not available in the training stage. To this aim, we pose the unseen face presentation attack detection (PAD) problem as the one-class kernel Fisher null-space regression and present a new face PAD approach that only uses bona fide (genuine) samples for training. Drawing on the proposed kernel Fisher null-space face PAD method and motivated by the availability of multiple information sources, next, we propose a multiple kernel fusion anomaly detection approach to combine the complementary information provided by different views of the problem for improved detection performance. And the last but not the least, we introduce a sparse variant of our multiple kernel Fisher null-space face PAD approach to improve inference speed at the operational phase without compromising much on the detection performance. The results of an experimental evaluation on the OULU-NPU, Replay-Mobile, Replay-Attack and MSU-MFSD datasets illustrate that the proposed method outperforms other methods operating in an unseen attack detection scenario while achieving very competitive performance to multi-class methods (that benefit from presentation attack data for training) despite using only bona fide samples in the training stage.

The functionality of face biometric systems is severely challenged by presentation attacks (PA’s), and especially those attacks that have not been available during the training phase of a PA detection (PAD) subsystem. Among other alternatives, the one-class classification (OCC) paradigm is an applicable strategy that has been observed to provide good generalisation against unseen attacks. Following an OCC approach for the unseen face PAD from RGB images, this work advocates a matrix-regularised multiple kernel learning algorithm to make use of several sources of information each constituting a different view of the face PAD problem. In particular, drawing on the one-class null Fisher classification principle, we characterise different deep CNN representations as kernels and propose a multiple kernel learning (MKL) algorithm subject to an (r, p)-norm (1 ≤ r, p) matrix regularisation constraint. The propose MKL algorithm is formulated as a saddle point Lagrangian optimisation task for which we present an effective optimisation algorithm with guaranteed convergence. An evaluation of the proposed one-class MKL algorithm on both general object images in an OCC setting as well as on different face PAD datasets in an unseen zero-shot attack detection setting illustrates the merits of the proposed method compared to other one-class multiple kernel and deep end-to-end CNN-based methods.

The kernel null-space technique is known to be an effective one-class classification (OCC) technique. Nevertheless, the applicability of this method is limited due to its susceptibility to possible training data corruption and the inability to rank training observations according to their conformity with the model. This article addresses these shortcomings by regularizing the solution of the null-space kernel Fisher methodology in the context of its regression-based formulation. In this respect, first, the effect of the Tikhonov regularization in the Hilbert space is analyzed, where the one-class learning problem in the presence of contamination in the training set is posed as a sensitivity analysis problem. Next, the effect of the sparsity of the solution is studied. For both alternative regularization schemes, iterative algorithms are proposed which recursively update label confidences. Through extensive experiments, the proposed methodology is found to enhance robustness against contamination in the training set compared with the baseline kernel null-space method, as well as other existing approaches in the OCC paradigm, while providing the functionality to rank training samples effectively.

Spoofing attacks on biometric systems can seriously compromise their practical utility. In this paper we focus on face spoofing detection. The majority of papers on spoofing attack detection formulate the problem as a two or multiclass learning task, attempting to separate normal accesses from samples of different types of spoofing attacks. In this paper we adopt the anomaly detection approach proposed in [1], where the detector is trained on genuine accesses only using one-class classifiers and investigate the merit of subject specific solutions. We show experimentally that subject specific models are superior to the commonly used client independent method. We also demonstrate that the proposed approach is more robust than multiclass formulations to unseen attacks.

Face anti-spoofing problem can be quite challenging due to various factors including diversity of face spoofing attacks, any new means of spoofing, the problem of imaging sensor interoperability and other environmental factors in addition to the small sample size. Taking into account these observations, in this work, first, a new evaluation protocol called “innovative attack evaluation protocol” to study the effect of occurrence of unseen attack types is proposed which better reflects the realistic conditions in spoofing attacks. Second, a new formulation of the problem based on the anomaly detection concept is proposed where the training data comes from the positive class only. The test data, of course, may come from the positive or negative class. Finally, a thorough evaluation and comparison of 20 different one-class and two-class systems is performed and demonstrated that the anomaly-based formulation is not inferior as compared with the conventional two-class approach.

Robustness to a diverse range of image transformations and distortions has been an everlasting goal of visual pattern recognition. While there have been a huge number of efforts to advance the state‐of‐the art in this direction over the last decades, two prominent outstanding schemes, among others, are deep multilayer architectures and graphical models, providing some degree of robustness to undesired image perturbations. In this study, the authors aim at shedding some light on the underlying concepts, mechanisms, strengths and potentials of each methodology while discussing their relative merits from a practical point of view. In particular, they discuss the underlying motivations for the construction of deep multilayer architectures and undirected graphical models, also known as Markov random fields. The principles in the construction of each architecture, how invariance properties are achieved in each approach, the efficiency of each approach in terms of computations required during train and test as well as the degree of human labour required in each approach are discussed. Finally, an experimental comparison of the performances of the two frameworks is performed on a challenging problem of face recognition in unconstrained settings in the presence of a wide range of undesirable visual perturbations.

In this study, a new method for single sample face recognition problem based on local dual‐tree complex wavelet transform (DT‐CWT) representation is proposed. The proposed system provides a number of countermeasures to neutralise the unwanted imaging conditions in face classification. First of all, two novel pre‐processing steps, geometric and photometric normalisation are used to normalise the facial images. The employed geometric normalisation method is more consistent with the geometry and the shape of the face image, removing redundant information from classification. This step is followed by a new illumination normalisation approach in which useful information for the extraction of DT‐CWT features is kept almost unaltered. Taking a local feature‐based approach, DT‐CWT features are then extracted regionally. By virtue of the effective normalisation stages and the employed multi‐scale and multi‐orientation DT‐CWT features, the proposed locally selected DT‐CWT method offers invariance to moderate real world image variations, such as illumination, expression, head pose, shift and in‐plane rotation. The experimental evaluation of the method is performed on the widely used FERET and YALE databases, in an identification scenario, achieving promising performance.

Face recognition has been the focus of attention for the past couple of decades and, as a result, a significant progress has been made in this area. However, the problem of spoofing attacks can challenge face biometric systems in practical applications. In this paper, an effective countermeasure against face spoofing attacks based on a kernel discriminant analysis approach is presented. Its success derives from different innovations. First, it is shown that the recently proposed multiscale dynamic texture descriptor based on binarized statistical image features on three orthogonal planes (MBSIF-TOP) is effective in detecting spoofing attacks, showing promising performance compared with existing alternatives. Next, by combining MBSIF-TOP with a blur-tolerant descriptor, namely, the dynamic multiscale local phase quantization (MLPQ-TOP) representation, the robustness of the spoofing attack detector can be further improved. The fusion of the information provided by MBSIF-TOP and MLPQ-TOP is realized via a kernel fusion approach based on a fast kernel discriminant analysis (KDA) technique. It avoids the costly eigen-analysis computations by solving the KDA problem via spectral regression. The experimental evaluation of the proposed system on different databases demonstrates its advantages in detecting spoofing attacks in various imaging conditions, compared with the existing methods.

This paper addresses face verification in unconstrained settings. For this purpose, first, a nonlinear binary class-specific kernel discriminant analysis classifier (CS-KDA) based on spectral regression kernel discriminant analysis is proposed. By virtue of the two-class formulation, the proposed CS-KDA approach offers a number of desirable properties such as specificity of the transformation for each subject, computational efficiency, simplicity of training, isolation of the enrolment of each client from others and increased speed in probe testing. Using the proposed CS-KDA approach, a regional discriminative face image representation based on a multiscale variant of the binarized statistical image features is proposed next. The proposed component-based representation when coupled with the dense pixel-wise alignments provided by a symmetric MRF matching model reduces the sensitivity to misalignments and pose variations, gauging the similarity more effectively. Finally, the discriminative representation is combined with two other effective image descriptors, namely the multiscale local binary patterns and the multiscale local phase quantization histograms via a kernel fusion approach to further enhance system accuracy. The experimental evaluation of the proposed methodology on challenging databases demonstrates its advantage over other methods.

A spatio-temporal descriptor for representation and recognition of time-varying textures is proposed [binarized statistical image features on three orthogonal planes (BSIF-TOP)] in this paper. The descriptor, similar in spirit to the well known local binary patterns on three orthogonal planes approach, estimates histograms of binary coded image sequences on three orthogonal planes corresponding to spatial/spatio-temporal dimensions. However, unlike some other methods which generate the code in a heuristic fashion, binary code generation in the BSIF-TOP approach is realized by filtering operations on different regions of spatial/spatio-temporal support and by binarizing the filter responses. The filters are learnt via independent component analysis on each of three planes after preprocessing using a whitening transformation. By extending the BSIF-TOP descriptor to a multiresolution scheme, the descriptor is able to capture the spatio-temporal content of an image sequence at multiple scales, improving its representation capacity. In the evaluations on the UCLA, Dyntex, and Dyntex++ dynamic texture databases, the proposed method achieves very good performance compared to existing approaches.

A pose-invariant face recognition system based on an image matching method formulated on MRFs is presented. The method uses the energy of the established match between a pair of images as a measure of goodness-of-match. The method can tolerate moderate global spatial transformations between the gallery and the test images and alleviate the need for geometric preprocessing of facial images by encapsulating a registration step as part of the system. It requires no training on non-frontal face images. A number of innovations, such as a dynamic block size and block shape adaptation, as well as label pruning and error pre-whitening measures have been introduced to increase the effectiveness of the approach. The experimental evaluation of the method is performed on two publicly available databases. First, the method is tested on the rotation shots of the XM2VTS data set in a verification scenario. Next, the evaluation is conducted in an identification scenario on the CMU-PIE database. The method compares favorably with the existing 2D or 3D generative model-based methods on both databases in both identification and verification scenarios.

The paper addresses the problem of face recognition under arbitrary pose. A hi-erarchical MRF-based image matching method for finding pixel-wise correspondences between facial images viewed from different angles is proposed and used to densely reg-ister a pair of facial images. The goodness-of-match between two faces is then measured in terms of the normalized energy of the match which is a combination of both structural differences between faces as well as their texture distinctiveness. The method needs no training on non-frontal images and circumvents the need for geometrical normalization of facial images. It is also robust to moderate scale changes between images. The proposed approach is evaluated on the CMU PIE database and promising results are obtained. 1