  Plenty of face detection and recognition methods have been proposed and got
delightful results in decades. Common face recognition pipeline consists of: 1)
face detection, 2) face alignment, 3) feature extraction, 4) similarity
calculation, which are separated and independent from each other. The separated
face analyzing stages lead the model redundant calculation and are hard for
end-to-end training. In this paper, we proposed a novel end-to-end trainable
convolutional network framework for face detection and recognition, in which a
geometric transformation matrix was directly learned to align the faces,
instead of predicting the facial landmarks. In training stage, our single CNN
model is supervised only by face bounding boxes and personal identities, which
are publicly available from WIDER FACE \cite{Yang2016} dataset and
CASIA-WebFace \cite{Yi2014} dataset. Tested on Face Detection Dataset and
Benchmark (FDDB) \cite{Jain2010} dataset and Labeled Face in the Wild (LFW)
\cite{Huang2007} dataset, we have achieved 89.24\% recall for face detection
task and 98.63\% verification accuracy for face recognition task
simultaneously, which are comparable to state-of-the-art results.
