ORCID Identifier(s)


Graduation Semester and Year




Document Type


Degree Name

Master of Science in Computer Science


Computer Science and Engineering

First Advisor

Vassilis Athitsos


In this work, we address the problem of clustering faces according to their individual identities present inherently in the dataset.The current clustering frameworks are either based on some heuristic method or require labelled data for training the models,also some of them make assumptions on data distribution or shape of the clusters.We have framed the problem of forming clusters to that of link prediction on graphs and learn how to do that in a completely unsupervised way by proposing to use Variational Graph Autoencoders and use Graph Attentional Network as the Encoder. We call this network as Variational Attentional Graph Autoencoder(VAGAE).Our framework is not based on any assumptions of data distribution or shape of clusters and learns without any use of labelled data. We solve the problem in the following way, we first extract features from a feature extractor which has been trained in an unsupervised way using Convolutional Adversarial Autoencoders and have compared the results of it with a pre-trained Inception Resnet feature extractor trained using FaceNet triplet loss algorithm. We then generate Instance Subgraphs(ISG) for each instance by finding the K-Nearest neighbours for each instance upto 2-hops and connect the edges to the instance only if it satisfies an approximate rank- order distance threshold. We then pass the ISG’s to the Variational Graph Autoencoder which uses a Graph Attention Network as an encoder to learn general graph structure features from ISG’s and perform link prediction. We then transitively merge the link prediction prediction results to form final clusters. We evaluate our results on the first 50 faces from the Youtube Faces dataset and show that the results are decent enough or even better in some metrics compared to the other clustering methods.We have also evaluated our method on Link based Face Clustering via Graph Convolution Network and have shown that we get decent performance on data taken from same distribution for train-test, even though we train our model using unsupervised learning.


Face clustering, Graph variational autoencoders, Graph attention networks


Computer Sciences | Physical Sciences and Mathematics


Degree granted by The University of Texas at Arlington