Pairwise Relational Networks for Face Recognition

by   Bong-Nam Kang, et al.

Existing face recognition using deep neural networks is difficult to know what kind of features are used to discriminate the identities of face images clearly. To investigate the effective features for face recognition, we propose a novel face recognition method, called a pairwise relational network (PRN), that obtains local appearance patches around landmark points on the feature map, and captures the pairwise relation between a pair of local appearance patches. The PRN is trained to capture unique and discriminative pairwise relations among different identities. Because the existence and meaning of pairwise relations should be identity dependent, we add a face identity state feature, which obtains from the long short-term memory (LSTM) units network with the sequential local appearance patches on the feature maps, to the PRN. To further improve accuracy of face recognition, we combined the global appearance representation with the pairwise relational feature. Experimental results on the LFW show that the PRN using only pairwise relations achieved 99.65 state feature achieved 99.76 pairwise relations and the PRN using pairwise relations and the face identity state feature achieved the state-of-the-art (95.7 achieved comparable results to the state-of-the-art for both face verification and face identification tasks on the IJB-A, and the state-of-the-art on the IJB-B.


page 5

page 10


Pairwise Relational Networks using Local Appearance Features for Face Recognition

We propose a new face recognition method, called a pairwise relational n...

Attentional Feature-Pair Relation Networks for Accurate Face Recognition

Human face recognition is one of the most important research areas in bi...

DeepID3: Face Recognition with Very Deep Neural Networks

The state-of-the-art of face recognition has been significantly advanced...

Universal Adversarial Spoofing Attacks against Face Recognition

We assess the vulnerabilities of deep face recognition systems for image...

Deep Learning Face Representation by Joint Identification-Verification

The key challenge of face recognition is to develop effective feature re...

Concealing the identity of faces in oblique images with adaptive hopping Gaussian mixtures

Cameras mounted on Micro Aerial Vehicles (MAVs) are increasingly used fo...

A New Pairwise Deep Learning Feature For Environmental Microorganism Image Analysis

Environmental microorganism (EM) offers a high-efficient, harmless, and ...

Please sign up or login with your details

Forgot password? Click here to reset