Lastly, we show that our end-to-end thermal-to-visible face verification system provides strong performance on the MILAB-VTF(B) dataset.
We show the efficacy of PASS to reduce gender and skintone information in descriptors from SOTA face recognition networks like Arcface.
Therefore, we present a novel `Adversarial Gender De-biasing algorithm (AGENDA)' to reduce the gender information present in face descriptors obtained from previously trained face recognition networks.
Therefore, distributed and sparse codes co-exist in the network units to represent different face attributes.
We discuss data driven factors (e. g., image quality, image population statistics, and algorithm architecture), and scenario modeling factors that consider the role of the "user" of the algorithm (e. g., threshold decisions and demographic constraints).
In the final fully connected layer of the networks, we found the order of expressivity for facial attributes to be Age > Sex > Yaw.
In this paper, we propose the Uncertainty-Gated Graph (UGG), which conducts graph-based identity propagation between tracklets, which are represented by nodes in a graph.
Subjects were tested subsequently on their ability to recognize those identities in low-resolution videos depicting the drivers operating a motor vehicle.
Deep convolutional neural networks (DCNNs) also create generalizable face representations, but with cascades of simulated neurons.
In this work, we consider challenging scenarios for unconstrained video-based face recognition from multiple-shot videos and surveillance videos with low-quality frames.
In this paper, we present a modular system for spatio-temporal action detection in untrimmed security videos.
We provide evaluation results of the proposed face detector on challenging unconstrained face detection datasets.
In this paper, we comprehensively study two covariate related problems for unconstrained face verification: first, how covariates affect the performance of deep neural networks on the large-scale unconstrained face verification problem; second, how to utilize covariates to improve verification performance.
We show that integrating this simple step in the training pipeline significantly improves the performance of face verification and recognition systems.
In particular, we show that learning features in a closed and bounded space improves the robustness of the network.
Domain Adaptation is an actively researched problem in Computer Vision.
Ranked #15 on Domain Adaptation on Office-31
In recent years, the performance of face verification systems has significantly improved using deep convolutional neural networks (DCNNs).
Ranked #4 on Face Verification on IJB-A
The proposed method employs a multi-task learning framework that regularizes the shared parameters of CNN and builds a synergy among different domains and tasks.
Ranked #9 on Face Verification on IJB-A
Over the last five years, methods based on Deep Convolutional Neural Networks (DCNNs) have shown impressive performance improvements for object detection and recognition problems.
In this paper, we present a brief history of developments in computer vision and artificial neural networks over the last forty years for the problem of image-based recognition.