13 accepted papers
Concept erasure aims to suppress sensitive content in diffusion models, but recent studies show that erased concepts can still be reawakened, revealing vulnerabilities in erasure methods. Existing reawakening methods mainly rely on prompt-level optimization to manipulate sampling trajectories, negle
We aim to solve unsupervised anomaly detection in a practical challenging environment where the normal dataset is both contaminated with defective regions and its product class distribution is tailed but unknown. We observe that existing models suffer from tail-versus-noise trade-off where if a mode…
Touch-stroke dynamics have garnered significant attention as a means of mobile user authentication. However, current research often assumes the availability of large datasets tailored to specific smartphone applications. In response to the scarcity of real-world data and the computational limitation…
"Understanding the vulnerability of face recognition systems to malicious attacks is of critical importance. Previous works have focused on reconstructing face images that can penetrate a targeted verification system. Even in the white-box scenario, however, naively reconstructed images misrepresent…
Periocular and face are complementary biometrics for identity management albeit with inherent limitations notably in scenarios involving occlusion due to sunglasses or masks. In response to these challenges we introduce Flexible Biometric Recognition (FBR) a novel framework designed to advance conve…
Conditional Multimodal Biometrics (CMB) presents a promising avenue for boosting periocular biometrics performance by conditioning facial information. This paper reframes CMB as a domain adaptation problem arising from distinct distribution gaps between facial and periocular modalities. Despite shar…
Palmprint biometrics garner heightened attention in palm-scanning payment and social security due to their distinctive attributes. However, prevailing methodologies singularly prioritize texture orientation, neglecting the significant texture scale dimension. We design an innovative network for conc…
Detecting out-of-distribution (OOD) samples are crucial for machine learning models deployed in open-world environments. Classifier-based scores are a standard approach for OOD detection due to their fine-grained detection capability. However, these scores often suffer from overconfidence issues, mi…
Very low-resolution face recognition (VLRFR) poses unique challenges, such as tiny regions of interest and poor resolution due to extreme standoff distance or wide viewing angle of the acquisition device. In this paper, we study principled approaches to elevate the recognizability of a face in the e…
A neural network trained on a classification dataset often exhibits a higher vector norm of hidden layer features for in-distribution (ID) samples, while producing relatively lower norm values on unseen instances from out-of-distribution (OOD). Despite this intriguing phenomenon being utilized in ma…
Low-light image enhancement, a pervasive but challenging problem, plays a central role in enhancing the visibility of an image captured in a poor illumination environment. Due to the fact that not all photons can pass the Bayer-Filter on the sensor of the color camera, in this work, we first present…
Gabor-based region covariance matrix (GRCM) has been demonstrated as a promising descriptor for face recognition. However, GRCM requires large number of filters to achieve satisfactory performance. Furthermore, complex-valued Gabor filters require double convolution operations for each filter that m…
The standard multi-scale, multi-orientation Gabor filter ensemble (SGFE) in the face recognition task reposits 40 filters localized in 8 orientations and 5 scales, with a real and an imaginary constituent. This paper devises a simple means of filter diversification, dubbed as multi-fold Gabor filter…