A decision-level fusion strategy for multimodal ocular biometric in visible spectrum based on posterior probability

Das, Abhijit; Pal, Umapada; Ferrer, Miguel A.; Blumenstein, Michael

doi:10.1109/btas.2017.8272772

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…In this section, we present the results of the group evaluation that: (i) analyze the performance of different sclera segmentation models over multiple test datasets, (ii) investigate performance differences of the models across various data subgroups and training configurations, and (iii) study the correlations between bias and overall segmentation performance. We make our evaluation code publicly available to ensure the reproducibility of our results 11 .…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Each of these steps is critical for the overall accuracy and trustworthiness of the recognition procedure and has to ensure consistent performance across diverse data characteristics, e.g., gender, ethnicity, acquisition device, gaze direction. The recent interest in sclera biometrics has led to considerable advances with all four steps and among others resulted in powerful segmentation models [7]- [9], novel recognition techniques [4], [5], [10], but also multi-biometric systems with impressive performance characteristics [11], [12]. However, to the best of our knowledge, the literature fails to address an important issue in this field: the bias and fairness of sclera-oriented biometric algorithms [13].…”

Section: Introductionmentioning

confidence: 99%

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Exploring Bias in Sclera Segmentation Models: A Group Evaluation Approach

Vitek

Das

Lucio

et al. 2023

IEEE Trans.Inform.Forensic Secur.

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Bias and fairness of biometric algorithms have been key topics of research in recent years, mainly due to the societal, legal and ethical implications of potentially unfair decisions made by automated decision-making models. A considerable amount of work has been done on this topic across different biometric modalities, aiming at better understanding the main sources of algorithmic bias or devising mitigation measures. In this work, we contribute to these efforts and present the first study investigating bias and fairness of sclera segmentation models. Although sclera segmentation techniques represent a key component of sclerabased biometric systems with a considerable impact on the overall recognition performance, the presence of different types of biases in sclera segmentation methods is still underexplored. To address this limitation, we describe the results of a group evaluation effort (involving seven research groups), organized to explore the performance of recent sclera segmentation models within a common experimental framework and study performance differences (and bias), originating from various demographic as well as environmental factors. Using five diverse datasets, we analyze seven independently developed sclera segmentation models in different experimental configurations. The results of our experiments suggest that there are significant differences in the overall segmentation performance across the seven models and that among the considered factors, ethnicity appears to be the biggest cause of bias. Additionally, we observe that training with representative and balanced data does not necessarily lead to less biased results. Finally, we find that in general there appears to be a negative correlation between the amount of bias observed (due to eye color, ethnicity and acquisition device) and the overall segmentation performance, suggesting that advances in the field of semantic segmentation may also help with mitigating bias.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring Bias in Sclera Segmentation Models: A Group Evaluation Approach

Vitek

Das

Lucio

et al. 2023

IEEE Trans.Inform.Forensic Secur.

View full text Add to dashboard Cite

show abstract

“…3. Multimodal ocular biometric in visible spectrum based on posterior probability method [46]. (Posterior probability decision)…”

Section: Decision Layer Experimentsmentioning

confidence: 99%

Unsteady State Lightweight Iris Certification Based on Multi-Algorithm Parallel Integration

Liu

Zhu

et al. 2019

Algorithms

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Aimed at the one-to-one certification problem of unsteady state iris at different shooting times, a multi-algorithm parallel integration general model structure is proposed in this paper. The iris in the lightweight constrained state affected by defocusing, deflection, and illumination is taken as the research object, the existing algorithms are combined into the model structure effectively, and a one-to-one certification algorithm for lightweight constrained state unsteady iris was designed based on multi-algorithm integration and maximum trusted decision. In this algorithm, a sufficient number of iris internal feature points from the unstable state texture were extracted as effective iris information through the image processing layer composed of various filtering processing algorithms, thereby eliminating defocused interference. In the feature recognition layer, iris deflection interference was excluded by the improved methods of Gabor and Hamming and Haar and BP for the stable features extracted by the image processing layer, and two certification results were obtained by means of parallel recognition. The correct number of certifications for an algorithm under a certain lighting condition were counted. The method with the most correct number was set as the maximum trusted method under this lighting condition, and the results of the maximum trusted method were taken as the final decision, thereby eliminating the effect of illumination. Experiments using the JLU and CASIA iris libraries under the prerequisites in this paper show that the correct recognition rate of the algorithm can reach a high level of 98% or more, indicating that the algorithm can effectively improve the accuracy of the one-to-one certification of lightweight constrained state unsteady iris. Compared with the latest architecture algorithms, such as CNN and deep learning, the proposed algorithm is more suitable for the prerequisites presented in this paper, which has good environmental inclusiveness and can better improve existing traditional algorithms’ effectiveness through the design of a parallel integration model structure.

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Design of transfer learning structure for slot wedge tightness inspection robot

Zhao

Ding

et al. 2020

Robotics and Autonomous Systems

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A decision-level fusion strategy for multimodal ocular biometric in visible spectrum based on posterior probability

Cited by 4 publications

References 12 publications

Exploring Bias in Sclera Segmentation Models: A Group Evaluation Approach

Exploring Bias in Sclera Segmentation Models: A Group Evaluation Approach

Unsteady State Lightweight Iris Certification Based on Multi-Algorithm Parallel Integration

Design of transfer learning structure for slot wedge tightness inspection robot

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