Proceedings ArticleDOI
Annotated crowd video face database
Tejas I. Dhamecha,Priyanka Verma,Mahek Shah,Richa Singh,Mayank Vatsa +4 more
- pp 106-112
TLDR
An annotated crowd video face (ACVF-2014) database is provided, along with face landmark information to encourage research in this important problem and two distinct use-case scenarios are provided.Abstract:
Research in face recognition under constrained environment has achieved an acceptable level of performance. However, there is a significant scope for improving face recognition capabilities in unconstrained environment including surveillance videos. Such videos are likely to record multiple people within the field of view. Face recognition in such a setting poses a set of challenges including unreliable face detection, multiple subjects performing different actions, low resolution, and sensor interoperability. In general, existing video face databases contain one subject in a video sequence. However, real world video sequences are more challenging and generally contain more than one person in a video. Therefore, in this paper, we provide an annotated crowd video face (ACVF-2014) database, along with face landmark information to encourage research in this important problem. The ACVF-2014 dataset contains 201 videos of 133 subjects where each video contains multiple subjects. We provide two distinct use-case scenarios, define their experimental protocols, and report baseline verification results using OpenBR and FaceVACS. The results show that both the baseline results do not yield more than 0.16 genuine accept rate @ 0.01 false accept rate. A software package is also developed to help researchers evaluate their systems using the defined protocols.read more
Citations
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Journal ArticleDOI
A survey on deep learning based face recognition
Guodong Guo,Guodong Guo,Na Zhang +2 more
TL;DR: Major deep learning concepts pertinent to face image analysis and face recognition are reviewed, and a concise overview of studies on specific face recognition problems is provided, such as handling variations in pose, age, illumination, expression, and heterogeneous face matching.
Proceedings ArticleDOI
Cross-spectral cross-resolution video database for face recognition
Maneet Singh,Shruti Nagpal,Nikita Gupta,Sanchit Gupta,Soumyadeep Ghosh,Richa Singh,Mayank Vatsa +6 more
TL;DR: It is asserted that this dataset can help researchers develop robust face recognition algorithms to handle real world surveillance scenarios and is presented to present baseline results with two commercial matchers for two experimental scenarios, where very low performance of both the matchers is observed.
Journal ArticleDOI
Real-time face alignment: evaluation methods, training strategies and implementation optimization
TL;DR: An extended set of evaluation metrics that allow novel evaluations to mitigate the typical problems found in real-time tracking contexts are proposed and generated models are faster, smaller, more accurate, more robust in specific challenging conditions and smoother in tracking systems.
Journal ArticleDOI
CrowdFaceDB: Database and benchmarking for face verification in crowd
TL;DR: CrowdFaceDB video face database is presented that fills the gap in unconstrained face recognition for crowd surveillance and showcases the exigent nature of crowd Surveillance and limitations of existing algorithms/systems.
Cross-spectral cross-resolution face recognition in videos
TL;DR: This research presents a video database which can be utilized to benchmark face recognition algorithms addressing crossspectral and cross-resolution matching and proposes an algorithm FaceFinder, which addresses shortcomings of existing face detectors by making use of human body segmentation results of a trained Convolutional Neural Network model specifically designed for semantic segmentation.
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