IJB–S: IARPA Janus Surveillance Video Benchmark
We present IJB-S dataset, an open-source IARPA Janus Surveillance Video Benchmark and associated protocols. The dataset consists of images and surveillance video collected from 202 subjects at a Department of Defense (DoD) training facility. Surveillance video was captured across multiple vignettes representative of a variety of real-world surveillance use cases that are particularly of interest to law enforcement and national security communities. Each video was annotated by human subject matter experts in order to generate ground truth identity and bounding box face labels. In total, over 10 million annotations were collected for the dataset. We present benchmark results utilizing state of the art deep learning approaches such as FaceNet. Our results illustrate and characterize the difficulty of the dataset.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
An Automatic System for Unconstrained Video-Based Face Recognition
Although deep learning approaches have achieved performance surpassing humans for still image-based face recognition, unconstrained video-based face recognition is still a challenging task due to large volume of data to …
Face RecognitionPushing the Frontiers of Unconstrained Face Detection and Recognition: IARPA Janus Benchmark A
Rapid progress in unconstrained face recognition has resulted in a saturation in recognition accuracy for current benchmark datasets. While important for early progress, a chief limitation in most benchmark datasets is t…
BenchmarkingFace DetectionFace RecognitionRobust Face RecognitionUncertainty Modeling of Contextual-Connections between Tracklets for Unconstrained Video-based Face Recognition
Unconstrained video-based face recognition is a challenging problem due to significant within-video variations caused by pose, occlusion and blur. To tackle this problem, an effective idea is to propagate the identity fr…
Face RecognitionUnconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks
Over the last five years, methods based on Deep Convolutional Neural Networks (DCNNs) have shown impressive performance improvements for object detection and recognition problems. This has been made possible due to the a…
Face DetectionFace RecognitionFace Verificationobject-detection+1Template-based Multi-Domain Face Recognition
Despite the remarkable performance of deep neural networks for face detection and recognition tasks in the visible spectrum, their performance on more challenging non-visible domains is comparatively still lacking. While…
Domain AdaptationDomain GeneralizationFace DetectionFace Recognition