Table 3 illustrates the performance of different eigenvalue statistical rate on FDDB dataset.
Comparisons with Other State-of-the-Art Face Detectors on FDDB. We compare the performance of our method with other state-of-the-art methods on FDDB dataset.
In this paper, we propose a face detection method based on two deep convolutional neural networks with SVM classifier; our method has achieved 89.24% recall rate on FDDB and also achieved high accuracy on other datasets.
Learned-Miller, "FDDB: a benchmark for face detection in unconstrained settings," Tech.
Caption: Figure 7: Comparisons of our method with other face detectors on FDDB dataset.
Caption: Figure 9: Qualitative face detection results of our detector on (a) FDDB, (b) AFW, and (c) LFW.