CN-Celeb-AV: A Multi-Genre Audio-Visual Dataset for Person Recognition
This provides a new benchmark dataset for researchers working on AVPR in unconstrained conditions, though it is incremental as it builds on existing data collection efforts.
The paper tackles the lack of datasets for audio-visual person recognition (AVPR) in unconstrained real-world scenarios by introducing CN-Celeb-AV, a multi-genre dataset with over 419k video segments from 1,136 persons, and demonstrates it better reflects real-world complexities compared to existing benchmarks.
Audio-visual person recognition (AVPR) has received extensive attention. However, most datasets used for AVPR research so far are collected in constrained environments, and thus cannot reflect the true performance of AVPR systems in real-world scenarios. To meet the request for research on AVPR in unconstrained conditions, this paper presents a multi-genre AVPR dataset collected `in the wild', named CN-Celeb-AV. This dataset contains more than 419k video segments from 1,136 persons from public media. In particular, we put more emphasis on two real-world complexities: (1) data in multiple genres; (2) segments with partial information. A comprehensive study was conducted to compare CN-Celeb-AV with two popular public AVPR benchmark datasets, and the results demonstrated that CN-Celeb-AV is more in line with real-world scenarios and can be regarded as a new benchmark dataset for AVPR research. The dataset also involves a development set that can be used to boost the performance of AVPR systems in real-life situations. The dataset is free for researchers and can be downloaded from http://cnceleb.org/.