Abstract-Current evaluation datasets for face detection, which is of great value in real-world applications, are still somewhat out-of-date. We propose a new face detection dataset MALF (short for Multi-Attribute Labelled Faces), which contains 5,250 images collected from the Internet and ∼12,000 labelled faces. The MALF dataset highlights in two main features: 1) It is the largest dataset for evaluation of face detection in the wild, and the annotation of multiple facial attributes makes it possible for fine-grained performance analysis. 2) To reveal the 'true' performances of algorithms in practice, MALF adopts an evaluation metric that puts stress on the recall rate at a relatively low false alarm rate. Besides providing a large dataset for face detection evaluation, this paper also collects more than 20 state-of-the-art algorithms, both from academia and industry, and conducts a fine-grained comparative evaluation of these algorithms, which can be considered as a summary of past advances made in face detection. The dataset and up-to-date results of the evaluation can be found at http: //www.cbsr.ia.ac.cn/faceevaluation/.
The MALF dataset REF is a large dataset with 5,250 images annotated with multiple facial attributes and it is specifically constructed for fine grained evaluation.
Fine-grained evaluation on face detection in the wild
2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)
2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)
Computer Science