— Face recognition has been studied extensively;
however, real-world face recognition still remains a challenging
task. The demand for unconstrained practical face recognition is
rising with the explosion of online multimedia such as social
networks, and video surveillance footage where face analysis
is of significant importance. In this paper, we approach face
recognition in the context of graph theory. We recognize an
unknown face using an external reference face graph (RFG).
An RFG is generated and recognition of a given face is achieved
by comparing it to the faces in the constructed RFG. Centrality
measures are utilized to identify distinctive faces in the reference
face graph. The proposed RFG-based face recognition algorithm
is robust to the changes in pose and it is also alignment free. The
RFG recognition is used in conjunction with DCT locality sensitive
hashing for efficient retrieval to ensure scalability. Experiments
are conducted on several publicly available databases and
the results show that the proposed approach outperforms the
state-of-the-art methods without any preprocessing necessities
such as face alignment. Due to the richness in the reference set
construction, the proposed method can also handle illumination and expression variation.