CrowdFaceDB: Database and benchmarking for face verification in crowd

• Published in: Pattern recognition letters Vol. 107; pp. 17 - 24
• Main Authors: Dhamecha, Tejas I., Shah, Mahek, Verma, Priyanka, Vatsa, Mayank, Singh, Richa
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.05.2018; Elsevier Science Ltd
• Subjects: Benchmark database; Benchmarks; Crowd monitoring; Crowds; Face detection; Face recognition; Field of view; Pattern recognition; Protocol verification; Surveillance; Benchmark database; Face recognition; 41A10; 65D05; 65D17; Face detection; 41A05
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2017.12.028

•A large crowd video face dataset, termed as CrowdFaceDB, is prepared.•Ground truth annotations along with benchmarking results of popular face detection and verification algorithms is provided.•The results on CrowdFaceDB reflect the poor state of face detection and verification algorithms in unconstrained environments.•The results reflect the poor state of the art of face detection and verification algorithms in unconstrained environments.•An end-to-end evaluation package is provided to facilitate benchmarking for both face detection and recognition. Face recognition research has benefited from the availability of challenging face databases and benchmark results on popular databases show very high performance on single-person per image/video databases. However, in real world surveillance scenarios, the environment is unconstrained and the videos are likely to record multiple subjects within the field of view. In such crowd surveillance videos, both face detection and recognition are still considered as onerous tasks. One of the key factors for limited research in this direction is unavailability of benchmark databases. This paper presents CrowdFaceDB video face database that fills the gap in unconstrained face recognition for crowd surveillance. The two fold contributions are: (1) developing an unconstrained crowd video face database of over 250 subjects, and (2) creating a benchmark protocol and performing baseline experiments for both face detection and verification. The experimental results showcase the exigent nature of crowd surveillance and limitations of existing algorithms/systems.