Abstract

Automatic fingerprint recognition systems are currently under the constant threat of presentation attacks (PAs). Existing fingerprint presentation attack detection (FPAD) solutions improve cross-sensor and cross-material generalization by utilizing style-transfer-based augmentation wrappers over a two-class PAD classifier. These solutions synthesize data by learning the style as a single entity, containing both sensor and material characteristics. However, these strategies necessitate learning the entire style upon adding a new sensor for an already known material or vice versa. We propose a bilinear decomposition-based wrapper called OSMT to improve cross-sensor and cross-material FPAD. OSMT uses one PA fingerprint to learn the corresponding sensor and material representations by disentanglement. Our approach also reduces the computational complexity by generating compact representations and utilizing lesser combinations of sensors and materials to produce several styles. We present the improvement in PAD performance using our technique on the publicly available LivDet datasets (2015, 2017, 2019 and 2021

Abstract

Biometric noise is often discarded in many biometric template protection systems. However, the noise ratio be- tween two templates encodes specific correlational proper- ties that template protection schemes can exploit. Biometric authentication usually occurs between mutually distrusting parties, which calls for privacy-preserving techniques. In this paper, we propose a novel biometric authentication pro- tocol, SIAN(Secure Iris Authentication using Noise), adapt- ing secure two-party computation and incorporating un- certainty constraints from biometric noise for security. We evaluate it on three iris datasets: MMU v1, Ubiris v1, and IITD v1, and observe a low EER degradation. The pro- posed protocol has information-theoretic security and low computational complexity, making it suitable for practical real-time applications.

Abstract

Active Learning aims to solve the problem of alleviating labelling costs for large-scale datasets by selecting a subset of data to effectively train on. Deep Active Learning (DAL) techniques typically involve repeated training of a model for sample acquisition over the entire subset of labelled data available in each round. This can be prohibitively expensive to run in real-world scenarios with large and constantly growing data. Some work has been done to address this – notably, SelectionVia-Proxy (SVP) proposed the use of a separate, smaller “proxy” model for acquisition. We explore further optimizations to the standard DAL setup and propose CLActive: an optimization procedure that brings significant speedups which maintains a constant training time for the selection model across rounds and retains information from past rounds using Experience Replay. We demonstrate large improvements in total train-time compared to the fully-trained baselines and SVP. We achieve up to 89×, 7×, 61× speedups over the fully-trained baseline at 50% of dataset collection in CIFAR, Imagenet and Amazon Review datasets, respectively, with little accuracy loss. We also show that CLActive is robust against catastrophic forgetting in a challenging class-incremental active-learning setting. Overall, we believe that CLActive can effectively enable rapid prototyping and deployment of deep AL algorithms in real-world use cases across a variety of settings.

Abstract

A method of calibrating cameras used to collect images to form an omni - stereo image is disclosed . The method may comprise determining intrinsic and extrinsic camera param eters for each of a plurality of left eye cameras and right eye cameras arranged along a viewing circle or ellipse and angled tangentially with respect to the viewing circle or ellipse ; categorizing left - right pairs of the plurality of left eye cameras and the plurality of right eye cameras into at least a first category , a second category or a third category ; aligning the left - right pairs of cameras that fall into the first category ; aligning the left - right pairs of cameras that fall into the second category ; and aligning the left - right pairs of cameras that fall into the third category by using extrinsic parameters of the left - right pairs that fall into the first category , and of the left - right pairs that fall into the second category .

Abstract

Existing approaches for 3D garment reconstruction either assume a predefined template for the garment geometry (restricting them to fixed clothing styles) or yield vertex colored meshes (lacking high-frequency textural details). Our novel framework co-learns geometric and semantic information of garment surface from the input monocular image for template-free textured 3D garment digitization. More specifically, we propose to extend PeeledHuman representation to predict the pixel-aligned, layered depth and semantic maps to extract 3D garments. The layered representation is further exploited to UV parametrize the arbitrary surface of the extracted garment without any human intervention to form a UV atlas. The texture is then imparted on the UV atlas in a hybrid fashion by first projecting pixels from the input image to UV space for the visible region, followed by inpainting the occluded regions

Abstract

A system and method of automatically reconstructing a three - dimensional ( 3D ) model of an object using a machine learning model is provided . The method includes ( i ) obtain ing , using an image capturing device , a color image of an object , ( ii ) generating , using an encoder , a feature map by converting the color image that is represented in the 3D array to n - dimensional array , ( iii ) generating , using the machine learning model , a set of peeled depth maps and a set of RGB maps from the feature map , ( iv ) determining one or more 3D surface points of the object by back projecting the set of peeled depth maps and the set of RGB maps to 3D space , ( v ) reconstructing , using the machine learning model , a 3D model of the object by performing surface reconstruc tion using the one or more 3D surface points of the object

Abstract

An active area of research in cognitive science is characterizing the relationship between brain structure and the observed functional activations. Recent graph diffusion models have had great success in mapping whole-brain, resting-state dy- namics measured using functional Magnetic Resonance Imaging (fMRI) to the brain structure derived using diffusion and T1 brain imaging. Here we test the application of one such graph diffusion method called the Multiple Kernel Learning (MKL) model. MKL model, formulated as a reaction-diffusion system using Wilson-Cowan equations, combines multiple diffusion ker- nels at different scales to predict functional connectome (FC) arising from a fixed structural connectome (SC). Our simulation results demonstrate that the MKL model successfully mapped the relationship between SC and FC from five different Electroen- cephalogram (EEG) bands (delta, theta, alpha, beta, and gamma). We used simultaneously acquired EEG-fMRI and NODDI dataset of 17 participants. The correlation between predicted FC and ground truth FC was higher for EEG bands than for fMRI data. The prediction accuracy peaked for the alpha band, and the highest frequency band, gamma had the lowest prediction accuracy. To the best of our knowledge, this is the first such end- to-end application of multiple kernel graph diffusion framework for modeling EEG data. One of the important features of MKL model is its ability to incorporate structural connectivity features into the generative model that predicts the EEG functional connectivity.

Abstract

Over the last decade, there has been growing interest in learning the mapping from structural connectivity (SC) to functional connectivity (FC) of the brain. The spontaneous brain activity fluctuations during the resting-state as captured by func- tional MRI (rsfMRI) contain rich non-stationary dynamics over a relatively fixed structural connectome. Among the modeling approaches, graph diffusion-based methods with single and mul- tiple diffusion kernels approximating static or dynamic functional connectivity have shown promise in predicting the FC given the SC. However, these methods are computationally expensive, not scalable, and fail to capture the complex dynamics underlying the whole process. Recently, deep learning methods such as GraphHeat networks along with graph diffusion have been shown to handle complex relational structures while preserving global information. In this paper, we propose multiple GraphHeat networks (M-GHN), a novel approach for mapping SC-FC. M- GHN enables us to model multiple heat kernel diffusion over the brain graph for approximating the complex Reaction Diffusion phenomenon. We argue that the proposed deep learning method overcomes the scalability and computational inefficiency issues but can still learn the SC-FC mapping successfully. Training and testing were done using the rsfMRI data of 100 participants from the human connectome project (HCP), and the results establish the viability of the proposed model. On the HCP dataset of 100 participants, the M-GHN achieves a high Pearson correlation

Abstract

We present a novel self-supervised framework for learning the discretization-agnostic surface parameterization of arbitrary 3D objects with both bounded and unbounded surfaces. Our framework leverages diffusion enabled global-to-local shape context for each vertex to first partition the unbounded surface into multiple patches using proposed self-supervised PatchNet and subsequently perform independent UV parameterization of these patches by learning forward and backward UV mapping for individual patches. Thus, our framework enables learning a discretization-agnostic parameterization at a lower resolution and then directly infer the parameterization for a higher resolution mesh without retraining. We evaluate our framework on multiple 3D objects from publicly available SHREC [Lian et al. 2011] dataset and report superior/faster UV parameterization over conventional methods.

Abstract

Kathakali is one of the major forms of Classical Indian Dance. The dance form is distinguished by the elaborately colourful makeup, costumes and face masks. In this work, we present (a) a framework to analyze the facial expressions of the actors and (b) novel visualization techniques for the same. Due to extensive makeup, costumes and masks, the general face analysis techniques fail on Kathakali videos. We present a dataset with manually annotated Kathakali sequences for four downstream tasks, i.e. face detection, background subtraction, landmark detection and face segmentation. We rely on transfer learning and fine-tune deep learning models and present qualitative and quantitative results for these tasks. Finally, we present a novel application of style-transfer of Kathakali video onto a cartoonized face. The comprehensive framework presented in the paper paves the way for better understanding, analysis, pedagogy and visualization of Kathakali videos.