In this paper, we deal with two challenges for measuring the similarity of
the subject identities in practical video-based face recognition - the
variation of the head pose in uncontrolled environments and the computational
expense of processing videos. Since the frame-wise feature mean is unable to
characterize the pose diversity among frames, we define and preserve the
overall pose diversity and closeness in a video. Then, identity will be the
only source of variation across videos since the pose varies even within a
single video. Instead of simply using all the frames, we select those faces
whose pose point is closest to the centroid of the K-means cluster containing
that pose point. Then, we represent a video as a bag of frame-wise deep face
features while the number of features has been reduced from hundreds to K.
Since the video representation can well represent the identity, now we measure
the subject similarity between two videos as the max correlation among all
possible pairs in the two bags of features. On the official 5,000 video-pairs
of the YouTube Face dataset for face verification, our algorithm achieves a
comparable performance with VGG-face that averages over deep features of all
frames. Other vision tasks can also benefit from the generic idea of employing
geometric cues to improve the descriptiveness of deep features.

本研究针对视频人脸识别中的身份相似度计算中的两大挑战：头部姿态在未受控制的环境下的变化和视频处理的计算复杂度，提出通过保留视频的整体姿态和距离以及减少深度特征分析，测评视频身份相似度的算法，可以达到与 VGG-face 相当的识别率，并在其他视觉任务中也能受益于使用几何线索来提高深度特征的描述能力。