FarSight: A Physics-Driven Whole-Body Biometric System at Large Distance and Altitude

• URL: http://arxiv.org/abs/2306.17206v2
• Date: Wed, 6 Sep 2023 16:35:52 GMT
• Title: FarSight: A Physics-Driven Whole-Body Biometric System at Large Distance and Altitude
• Authors: Feng Liu, Ryan Ashbaugh, Nicholas Chimitt, Najmul Hassan, Ali Hassani, Ajay Jaiswal, Minchul Kim, Zhiyuan Mao, Christopher Perry, Zhiyuan Ren, Yiyang Su, Pegah Varghaei, Kai Wang, Xingguang Zhang, Stanley Chan, Arun Ross, Humphrey Shi, Zhangyang Wang, Anil Jain and Xiaoming Liu
• Abstract summary: This paper presents the end-to-end design, development and evaluation of FarSight. FarSight is an innovative software system designed for whole-body (fusion of face, gait and body shape) biometric recognition. We test FarSight's effectiveness using the newly acquired IARPA Biometric Recognition and Identification at Altitude and Range dataset.
• Score: 67.55994773068191
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Whole-body biometric recognition is an important area of research due to its vast applications in law enforcement, border security, and surveillance. This paper presents the end-to-end design, development and evaluation of FarSight, an innovative software system designed for whole-body (fusion of face, gait and body shape) biometric recognition. FarSight accepts videos from elevated platforms and drones as input and outputs a candidate list of identities from a gallery. The system is designed to address several challenges, including (i) low-quality imagery, (ii) large yaw and pitch angles, (iii) robust feature extraction to accommodate large intra-person variabilities and large inter-person similarities, and (iv) the large domain gap between training and test sets. FarSight combines the physics of imaging and deep learning models to enhance image restoration and biometric feature encoding. We test FarSight's effectiveness using the newly acquired IARPA Biometric Recognition and Identification at Altitude and Range (BRIAR) dataset. Notably, FarSight demonstrated a substantial performance increase on the BRIAR dataset, with gains of +11.82% Rank-20 identification and +11.3% TAR@1% FAR.

Related papers

• Long-Range Biometric Identification in Real World Scenarios: A Comprehensive Evaluation Framework Based on Missions [11.557368031775717]
  This paper evaluates research solutions for identifying individuals at ranges and altitudes. By fusing face and body features, we propose developing robust biometric systems for effective long-range identification.
  arXiv  Detail & Related papers  (2024-09-03T02:17:36Z)
• AG-ReID.v2: Bridging Aerial and Ground Views for Person Re-identification [39.58286453178339]
  Aerial-ground person re-identification (Re-ID) presents unique challenges in computer vision. We introduce AG-ReID.v2, a dataset specifically designed for person Re-ID in mixed aerial and ground scenarios. This dataset comprises 100,502 images of 1,615 unique individuals, each annotated with matching IDs and 15 soft attribute labels.
  arXiv  Detail & Related papers  (2024-01-05T04:53:33Z)
• Whole-body Detection, Recognition and Identification at Altitude and Range [57.445372305202405]
  We propose an end-to-end system evaluated on diverse datasets. Our approach involves pre-training the detector on common image datasets and fine-tuning it on BRIAR's complex videos and images. We conduct thorough evaluations under various conditions, such as different ranges and angles in indoor, outdoor, and aerial scenarios.
  arXiv  Detail & Related papers  (2023-11-09T20:20:23Z)
• An Efficient Ensemble Explainable AI (XAI) Approach for Morphed Face Detection [1.2599533416395763]
  We present a novel visual explanation approach named Ensemble XAI to provide a more comprehensive visual explanation for a deep learning prognostic model (EfficientNet-Grad1) The experiments have been performed on three publicly available datasets namely Face Research Lab London Set, Wide Multi-Channel Presentation Attack (WMCA) and Makeup Induced Face Spoofing (MIFS)
  arXiv  Detail & Related papers  (2023-04-23T13:43:06Z)
• Geometric-aware Pretraining for Vision-centric 3D Object Detection [77.7979088689944]
  We propose a novel geometric-aware pretraining framework called GAPretrain. GAPretrain serves as a plug-and-play solution that can be flexibly applied to multiple state-of-the-art detectors. We achieve 46.2 mAP and 55.5 NDS on the nuScenes val set using the BEVFormer method, with a gain of 2.7 and 2.1 points, respectively.
  arXiv  Detail & Related papers  (2023-04-06T14:33:05Z)
• Expanding Accurate Person Recognition to New Altitudes and Ranges: The BRIAR Dataset [1.965868228519436]
  Face recognition technology has advanced significantly in recent years due largely to the availability of large and increasingly complex training datasets for use in deep learning models. These datasets, however, have limited utility in more advanced security, forensics, and military applications. This paper describes methods used to collect and curate the dataset, and the dataset's characteristics at the current stage.
  arXiv  Detail & Related papers  (2022-11-03T15:51:39Z)
• Multi-Modal Human Authentication Using Silhouettes, Gait and RGB [59.46083527510924]
  Whole-body-based human authentication is a promising approach for remote biometrics scenarios. We propose Dual-Modal Ensemble (DME), which combines both RGB and silhouette data to achieve more robust performances for indoor and outdoor whole-body based recognition. Within DME, we propose GaitPattern, which is inspired by the double helical gait pattern used in traditional gait analysis.
  arXiv  Detail & Related papers  (2022-10-08T15:17:32Z)
• Biometrics: Trust, but Verify [49.9641823975828]
  Biometric recognition has exploded into a plethora of different applications around the globe. There are a number of outstanding problems and concerns pertaining to the various sub-modules of biometric recognition systems.
  arXiv  Detail & Related papers  (2021-05-14T03:07:25Z)
• Generalized Iris Presentation Attack Detection Algorithm under Cross-Database Settings [63.90855798947425]
  Presentation attacks pose major challenges to most of the biometric modalities. We propose a generalized deep learning-based presentation attack detection network, MVANet. It is inspired by the simplicity and success of hybrid algorithm or fusion of multiple detection networks.
  arXiv  Detail & Related papers  (2020-10-25T22:42:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.