Active Face Frontalization using Commodity Unmanned Aerial Vehicles
This paper describes a system by which Unmanned Aerial Vehicles (UAVs) can gather high-quality face images that can be used in biometric identification tasks. Success in face-based identification depends in large part on the image quality, and a major factor is how frontal the view is. Face recognition software pipelines can improve identification rates by synthesizing frontal views from non-frontal views by a process call {\em frontalization}. Here we exploit the high mobility of UAVs to actively gather frontal images using components of a synthetic frontalization pipeline. We define a frontalization error and show that it can be used to guide an UAVs to capture frontal views. Further, we show that the resulting image stream improves matching quality of a typical face recognition similarity metric. The system is implemented using an off-the-shelf hardware and software components and can be easily transfered to any ROS enabled UAVs.
Code (0)
등록된 구현이 없습니다.
Tasks
Face RecognitionSimilar Papers 제목 키워드 기반
Towards Large-Scale Pose-Invariant Face Recognition Using Face Defrontalization
Face recognition under extreme head poses is a challenging task. Ideally, a face recognition system should perform well across different head poses, which is known as pose-invariant face recognition. To achieve pose inva…
Face AlignmentFace RecognitionRobust Face RecognitionEffective Face Frontalization in Unconstrained Images
"Frontalization" is the process of synthesizing frontal facing views of faces appearing in single unconstrained photos. Recent reports have suggested that this process may substantially boost the performance of face reco…
Face RecognitionUGV-Conditioned Multi-UAV Informative Planning on a Shared Exposure Belief
Safe ground navigation in large, threat-augmented environments requires aerial support that actively reduces the risks that a ground vehicle faces along its route. Existing aerial reconnaissance systems focus on mapping …
Active Perception Applied To Unmanned Aerial Vehicles Through Deep Reinforcement Learning
Unmanned Aerial Vehicles (UAV) have been standing out due to the wide range of applications in which they can be used autonomously. However, they need intelligent systems capable of providing a greater understanding of w…
Contrastive LearningDeep Reinforcement Learningreinforcement-learningReinforcement Learning (RL)Pose-invariant face recognition via feature-space pose frontalization
Pose-invariant face recognition has become a challenging problem for modern AI-based face recognition systems. It aims at matching a profile face captured in the wild with a frontal face registered in a database. Existin…
Face RecognitionRobust Face Recognition