Pyramid Mean Representation of Image Sequences for Fast Face Retrieval in Unconstrained Video Data
Lecture Notes in Computer Science 8888, Advances in Visual Computing, 10th International Symposium, ISVC 2014, Proceedings, Part II, 2014.
10th International Symposium on Visual Computing (ISVC), Las Vegas, USA, 8.-10. Dezember 2014
This paper addresses the problem of face retrieval on large datasets
by proposing an efficient representation for face videos. In comparison to the
classical face verification problem, face retrieval poses additional challenges originating
from database size. First, a different characteristic of recognition performance
is required because retrieval scenarios have only very few correct face
samples embedded in a large amount of imposters. In addition, the large number
of samples in the database requires fast matching techniques. In this contribution,
we present a face retrieval system which addresses these challenges.
The first step consists of a set of measures to reduce frame descriptor dimension
which saves processing time while keeping recognition performance. Afterwards,
a novel Pyramid Mean Representation (PMR) of face videos is presented which
allows for fast and accurate queries on large databases. The key concept is a hierarchical
data representation with increasing sparsity which is used for an iterative
query evaluation in a coarse to fine manner. The effectiveness of the proposed
system is evaluated on the currently largest and most challenging public dataset
of unconstrained videos, the YouTube Faces Database. In addition to the official
verification test protocol, we define a protocol for face retrieval using a leave-oneout
strategy. The proposed system achieves the best performance in this protocol
with less processing time than baseline methods.