Convolutional neural networks for ocular smartphone-based biometrics

Ahuja, Karan; Islam, Rahul; Barbhuiya, Ferdous Ahmed; Dey, Kuntal

doi:10.1016/j.patrec.2017.04.002

Cited by 41 publications

(26 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Furthermore, apart from the deep learning methods, we also show the comparison with handcrafted features such as Histogram of Oriented Gradients (HOG) [6] and Daisy features (similar to SIFT) [33]. The 1 Kandaswamy et al [12] has reported results on this database, but the protocol used in their work is transfer learning based. Santos et al [27] had performed cross-sensor experiments, but evaluated their algorithm on the entire dataset.…”

Section: Resultsmentioning

confidence: 99%

“…We perform two experiments on the dataset. (a): In the first experiment, for training, all the images in the enrollment set are used and for testing, the images in the verification set act as probes for the enrolled images via which identification is performed similar to [1]. (b): In order to compare with [37], the training and testing was performed only on the images captured via the iPhone in day light conditions (as per the protocol used in [37]).…”

Section: Visob Datasetmentioning

confidence: 99%

See 1 more Smart Citation

Heterogeneity Aware Deep Embedding for Mobile Periocular Recognition

Rishabh

Baweja

Ghosh

et al. 2018

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)

View full text Add to dashboard Cite

Mobile biometric approaches provide the convenience of secure authentication with an omnipresent technology. However, this brings an additional challenge of recognizing biometric patterns in an unconstrained environment including variations in mobile camera sensors, illumination conditions, and capture distance. To address the heterogeneous challenge, this research presents a novel heterogeneity aware loss function within a deep learning framework. The effectiveness of the proposed loss function is evaluated for periocular biometrics using the CSIP, IMP and VISOB mobile periocular databases. The results show that the proposed algorithm yields state-of-the-art results in a heterogeneous environment and improves generalizability for crossdatabase experiments.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Visob Datasetmentioning

confidence: 99%

Heterogeneity Aware Deep Embedding for Mobile Periocular Recognition

Rishabh

Baweja

Ghosh

et al. 2018

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)

View full text Add to dashboard Cite

show abstract

“…The remainder of this paper is organized as follows: Section II summarizes the related work, and Section III provides a detailed description of the proposed in-set analysis. In Section IV we discuss our results and the conclusions are given in Section V. 2 http://www.jonathanjordan.staff.shef.ac.uk/IntroPS/part5.pdf…”

Section: Advantages and Weaknessesmentioning

confidence: 99%

“…3) VW Periocular biometrics Ahuja et al [1] (extended in [2]) compared the effectiveness of unsupervised/supervised CNNs for periocular recognition in the visible spectrum (VW), observing optimal performance when CNNs were used exclusively to extract 512-dimensional feature vectors, latter matched by the cosine similarity. Zhao and Kumar [51] fused scores from multiple CNNs, one of them tuned according to identity and the remaining incorporating explicit semantic information, such as gender, ethnicity and age.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Reminiscence of “<italic>Mastermind</italic>”: Iris/Periocular Biometrics by “<italic>In-Set</italic>” CNN Iterative Analysis

Proença

Neves

2019

IEEE Trans.Inform.Forensic Secur.

View full text Add to dashboard Cite

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.

show abstract