  This paper tackles face recognition in videos employing metric learning
methods and similarity ranking models. The paper compares the use of the
Siamese network with contrastive loss and Triplet Network with triplet loss
implementing the following architectures: Google/Inception architecture, 3D
Convolutional Network (C3D), and a 2-D Long short-term memory (LSTM) Recurrent
Neural Network. We make use of still images and sequences from videos for
training the networks and compare the performances implementing the above
architectures. The dataset used was the YouTube Face Database designed for
investigating the problem of face recognition in videos. The contribution of
this paper is two-fold: to begin, the experiments have established 3-D
Convolutional networks and 2-D LSTMs with the contrastive loss on image
sequences do not outperform Google/Inception architecture with contrastive loss
in top $n$ rank face retrievals with still images. However, the 3-D Convolution
networks and 2-D LSTM with triplet Loss outperform the Google/Inception with
triplet loss in top $n$ rank face retrievals on the dataset; second, a Support
Vector Machine (SVM) was used in conjunction with the CNNs' learned feature
representations for facial identification. The results show that feature
representation learned with triplet loss is significantly better for n-shot
facial identification compared to contrastive loss. The most useful feature
representations for facial identification are from the 2-D LSTM with triplet
loss. The experiments show that learning spatio-temporal features from video
sequences is beneficial for facial recognition in videos.
