We present an algorithm for extracting key-point descriptors using deep
convolutional neural networks (CNN). Unlike many existing deep CNNs, our model
computes local features around a given point in an image. We also present a
face alignment algorithm based on regression using these local descriptors. The
proposed method called Local Deep Descriptor Regression (LDDR) is able to
localize face landmarks of varying sizes, poses and occlusions with high
accuracy. Deep Descriptors presented in this paper are able to uniquely and
efficiently describe every pixel in the image and therefore can potentially
replace traditional descriptors such as SIFT and HOG. Extensive evaluations on
five publicly available unconstrained face alignment datasets show that our
deep descriptor network is able to capture strong local features around a given
landmark and performs significantly better than many competitive and
state-of-the-art face alignment algorithms.

本篇研究提出了一种基于深度卷积神经网络的关键点描述符提取算法，其能够高效提取面部特征从而替代传统方法 SIFT 和 HOG，并构建了一种基于回归的面部对齐算法，称为 LDDR，该算法在公开数据集上进行了验证，并有望在多种尺度、角度和遮挡下实现高精度的面部关键点的定位。