62 papers with code • 0 benchmarks • 5 datasets
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The proposed method addresses two issues in adapting state- of-the-art generic object detection ConvNets (e. g., faster R-CNN) for face detection: (i) One is to eliminate the heuristic design of prede- fined anchor boxes in the region proposals network (RPN) by exploit- ing a 3D mean face model.
We propose a straightforward method that simultaneously reconstructs the 3D facial structure and provides dense alignment.
Face image quality is an important factor to enable high performance face recognition systems.
In our experiments with an off-the-shelf face recognition software we observe the following phenomena: 1) The amount of real training data needed to train competitive deep face recognition systems can be reduced significantly.
In this paper, we propose to tackle these three challenges in an new alignment framework termed 3D Dense Face Alignment (3DDFA), in which a dense 3D Morphable Model (3DMM) is fitted to the image via Cascaded Convolutional Neural Networks.
To address this, we introduce a versatile model that learns a non-linear representation of a face using spectral convolutions on a mesh surface.