Unique Faces Recognition in Videos

06/10/2020
by   Jiahao Huo, et al.
0

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.

READ FULL TEXT

page 1

page 3

research
06/15/2021

Hotel Recognition via Latent Image Embedding

We approach the problem of hotel recognition with deep metric learning. ...
research
02/28/2019

MassFace: an efficient implementation using triplet loss for face recognition

In this paper we present an efficient implementation using triplet loss ...
research
03/25/2021

Deep Similarity Learning for Sports Team Ranking

Sports data is more readily available and consequently, there has been a...
research
08/05/2020

Subclass Contrastive Loss for Injured Face Recognition

Deaths and injuries are common in road accidents, violence, and natural ...
research
12/02/2020

Differential Morphed Face Detection Using Deep Siamese Networks

Although biometric facial recognition systems are fast becoming part of ...
research
08/06/2021

SELM: Siamese Extreme Learning Machine with Application to Face Biometrics

Extreme Learning Machine is a powerful classification method very compet...
research
05/11/2019

Novel Long Short-Term Memory Cell Architectures: Application to Light Field Face Recognition

With the emergence of lenslet light field cameras able to capture rich s...

Please sign up or login with your details

Forgot password? Click here to reset