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.
These approaches require object-centric images to perform matching.
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.
While the research community appears to have developed a consensus on the methods of acquiring annotated data, design and training of CNNs, many questions still remain to be answered.
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
Recent progress in face detection (including keypoint detection), and recognition is mainly being driven by (i) deeper convolutional neural network architectures, and (ii) larger datasets.
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.
We present an algorithm for simultaneous face detection, landmarks localization, pose estimation and gender recognition using deep convolutional neural networks (CNN).
Ranked #4 on Face Detection on Annotated Faces in the Wild
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.