266 research outputs found
Influence of segmentation on deep iris recognition performance
Despite the rise of deep learning in numerous areas of computer vision and
image processing, iris recognition has not benefited considerably from these
trends so far. Most of the existing research on deep iris recognition is
focused on new models for generating discriminative and robust iris
representations and relies on methodologies akin to traditional iris
recognition pipelines. Hence, the proposed models do not approach iris
recognition in an end-to-end manner, but rather use standard heuristic iris
segmentation (and unwrapping) techniques to produce normalized inputs for the
deep learning models. However, because deep learning is able to model very
complex data distributions and nonlinear data changes, an obvious question
arises. How important is the use of traditional segmentation methods in a deep
learning setting? To answer this question, we present in this paper an
empirical analysis of the impact of iris segmentation on the performance of
deep learning models using a simple two stage pipeline consisting of a
segmentation and a recognition step. We evaluate how the accuracy of
segmentation influences recognition performance but also examine if
segmentation is needed at all. We use the CASIA Thousand and SBVPI datasets for
the experiments and report several interesting findings.Comment: 6 pages, 3 figures, 3 tables, submitted to IWBF 201
Semi-supervised auto-encoder for facial attributes recognition
The particularity of our faces encourages many researchers to exploit their features in different domains such as user identification, behaviour analysis, computer technology, security, and psychology. In this paper, we present a method for facial attributes analysis. The work addressed to analyse facial images and extract features in the purpose to recognize demographic attributes: age, gender, and ethnicity (AGE). In this work, we exploited the robustness of deep learning (DL) using an updating version of autoencoders called the deep sparse autoencoder (DSAE). In this work we used a new architecture of DSAE by adding the supervision to the classic model and we control the overfitting problem by regularizing the model. The pass from DSAE to the semi-supervised autoencoder (DSSAE) facilitates the supervision process and achieves an excellent performance to extract features. In this work we focused to estimate AGE jointly. The experiment results show that DSSAE is created to recognize facial features with high precision. The whole system achieves good performance and important rates in AGE using the MORPH II databas
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
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