1 research outputs found
A Reminiscence of ”Mastermind”: Iris/Periocular Biometrics by ”In-Set” CNN Iterative Analysis
Convolutional neural networks (CNNs) have
emerged as the most popular classification models in biometrics
research. Under the discriminative paradigm of pattern
recognition, CNNs are used typically in one of two ways: 1)
verification mode (”are samples from the same person?”), where
pairs of images are provided to the network to distinguish
between genuine and impostor instances; and 2) identification
mode (”whom is this sample from?”), where appropriate feature
representations that map images to identities are found. This
paper postulates a novel mode for using CNNs in biometric
identification, by learning models that answer to the question ”is
the query’s identity among this set?”. The insight is a reminiscence
of the classical Mastermind game: by iteratively analysing the
network responses when multiple random samples of k gallery
elements are compared to the query, we obtain weakly correlated
matching scores that - altogether - provide solid cues to infer
the most likely identity. In this setting, identification is regarded
as a variable selection and regularization problem, with sparse
linear regression techniques being used to infer the matching
probability with respect to each gallery identity. As main strength,
this strategy is highly robust to outlier matching scores, which
are known to be a primary error source in biometric recognition.
Our experiments were carried out in full versions of two
well known irises near-infrared (CASIA-IrisV4-Thousand) and
periocular visible wavelength (UBIRIS.v2) datasets, and confirm
that recognition performance can be solidly boosted-up by the
proposed algorithm, when compared to the traditional working
modes of CNNs in biometrics.info:eu-repo/semantics/publishedVersio