Search Results for author:
Found 8 papers, 0 papers with code
Lp-Norm Constrained One-Class Classifier Combination
The vector-norm constraint enables the model to adapt to the intrinsic uniformity/sparsity of the ensemble in the space of base learners and acts as a (soft) classifier selection mechanism by shaping the relative magnitudes of fusion weights.
Unknown Face Presentation Attack Detection via Localised Learning of Multiple Kernels
The paper studies face spoofing, a. k. a.
$\ell_p$-Norm Multiple Kernel One-Class Fisher Null-Space
The paper addresses the multiple kernel learning (MKL) problem for one-class classification (OCC).
Unseen Face Presentation Attack Detection Using Class-Specific Sparse One-Class Multiple Kernel Fusion Regression
The paper addresses face presentation attack detection in the challenging conditions of an unseen attack scenario where the system is exposed to novel presentation attacks that were not present in the training step.
Multi-Task Kernel Null-Space for One-Class Classification
The non-linear structure learning method is then extended to a sparse setting where different tasks compete in an output composition mechanism, leading to a sparse non-linear structure among multiple problems.
Robust One-Class Kernel Spectral Regression
This work addresses these shortcomings by studying the effect of regularising the solution of the null-space kernel Fisher methodology in the context of its regression-based formulation (OC-KSR).
One-Class Kernel Spectral Regression
The paper introduces a new efficient nonlinear one-class classifier formulated as the Rayleigh quotient criterion optimisation.
Client-Specific Anomaly Detection for Face Presentation Attack Detection
In addition, it is demonstrated that the same set of deep convolutional features used for the recognition purposes is effective for face presentation attack detection in the class-specific one-class anomaly detection paradigm.
