Verification of Sitter Identity Across Historical Portrait Paintings by Confidence-aware Face Recognition

Huber, Marco F.; Terhörst, Philipp; Thi, Luu, Anh; Kirchbuchner, Florian; Damer, Naser

doi:10.1109/icpr56361.2022.9956452

Cited by 8 publications

(2 citation statements)

References 22 publications

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“…It was proposed a model pointing to increase the transparency of verification decisions: an approach to estimate the uncertainty of face comparison scores and introduce a confidence measure of the system's decision to provide insights into the verification decision. The experimental campaign was proven on three face recognition models on two datasets [18]. This year there are several very interesting research papers on the same subject.…”

Section: State-of-art Of Face Recognationmentioning

confidence: 99%

Proto/Early Renaissance Depictions, Iconographic Analysis and Computerised Face Recognition Connections: 16th Century Frescoes of St. Leocadia Church (Chaves, North of Portugal)

Salavessa,

Aranha,

Moreira

et al. 2023

Preprint

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The content of this paper is a very significant contribution to the heritage regional reconstruction and conservation interventions. The results about the iconographic analysis of the Early-Renaissance frescoes of St. Leocadia Church, in Chaves, in the north-east of Portugal, were presented for the first time. This paper aims to study facial similarity analysis applied to identification of the personages represented in the 1511-1513 mural paintings of the apse of St. Leocadia Church, in Chaves Municipality (North of Portugal) and those ones in the oil paintings by the Proto-Renaissance Portuguese painter Nuno Gonçalves. In this work, it was explored the feasibility of face recognition technologies in answering many ambiguities concerning Manueline stylistic identity and iconography. A proposal was made to identify the characters, fundamental to the meaning of the frescoes. It has been experimentally proved on 7 characters of St. Leocadia Church’s paintings, in comparison with the referred old master portraits. It was used an in-house trained deep face recognition model to extract facial features from images of 15th and 16th century portraits to compare their degree of similarity. It was undertaken facial similarity analysis of the faces in the formidable portrait gallery of the 15th century Portuguese society represented in the Panels of St. Vicent, which has been subject of national and international research for 130 years. On the other, in the oil painting of St. Peter and St. Paul and Infanta St. Joana, of the same old master. The iconographic interpretation of the mural paintings of St. Leocadia, based in the deep facial features of the images, is plausible: a catechetical purpose; the ritual practices of royal ancestor worship in royal portrait apses of the churches; the Portuguese maritime expansion and the macro-imperial ideology of D. Manuel I.

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Section: State-of-art Of Face Recognationmentioning

confidence: 99%

Proto/Early Renaissance Depictions, Iconographic Analysis and Computerised Face Recognition Connections: 16th Century Frescoes of St. Leocadia Church (Chaves, North of Portugal)

Salavessa,

Aranha,

Moreira

et al. 2023

Preprint

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“…Although there are currently no commonly agreed solutions for FV explainability performance assessment, a few authors have attempted to formulate explainability performance assessment objective metrics for the speci c FV explainability tools proposed by themselves [2] [4] [5] [6] [7]. The relatively unexplored landscape of FV explainability performance assessment can be largely attributed to the diversity of explainability tools proposed, such as face features relevance [8] [9] and saliency/heatmaps visualization [10] [1] [2], making the comparison of different strategies to generate FV explanations more di cult.…”

Section: Introductionmentioning

confidence: 99%

Subjective Performance Assessment Protocol for Visual Explanations-based Face Verification Explainability

Bousnina,

Ascenso,

Correia

et al. 2024

Preprint

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The integration of Face Verification (FV) systems into multiple critical moments of daily life has become increasingly prevalent, raising concerns regarding the transparency and reliability of these systems. Consequently, there is a growing need for FV explainability tools to provide insights into the behavior of these systems. FV explainability tools that generate visual explanations, e.g., saliency maps, heatmaps, contour-based visualization maps, and face segmentation maps show promise in enhancing FV transparency by highlighting the contributions of different face regions to the FV decision-making process. However, evaluating the performance of such explainability tools remains challenging due to the lack of standardized assessment metrics and protocols. In this context, this paper proposes a subjective performance assessment protocol for evaluating the explainability performance of visual explanation-based FV explainability tools through pairwise comparisons of their explanation outputs. The proposed protocol encompasses a set of key specifications designed to efficiently collect the subjects’ preferences and estimate explainability performance scores, facilitating the relative assessment of the explainability tools. This protocol aims to address the current gap in evaluating the effectiveness of visual explanation-based FV explainability tools, providing a structured approach for assessing their performance and comparing with alternative tools. The proposed protocol is exercised and validated through an experiment conducted using two distinct heatmap-based FV explainability tools, notably FV-RISE and CorrRISE, taken as examples of visual explanation-based explainability tools, considering the various types of FV decisions, i.e., True Acceptance (TA), False Acceptance (FA), True Rejection (TR), and False Rejection (FR). A group of subjects with variety in age, gender, and ethnicity was tasked to express their preferences regarding the heatmap-based explanations generated by the two selected explainability tools. The subject preferences were collected and statistically processed to derive quantifiable scores, expressing the relative explainability performance of the assessed tools. The experimental results revealed that both assessed explainability tools exhibit comparable explainability performance for FA, TR, and FR decisions with CorrRISE performing slightly better than FV-RISE for TA decisions.

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PIC-Score: Probabilistic Interpretable Comparison Score for Optimal Matching Confidence in Single- and Multi-Biometric Face Recognition

Neto,

Sequeira,

Cardoso

et al. 2023

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Verification of Sitter Identity Across Historical Portrait Paintings by Confidence-aware Face Recognition

Cited by 8 publications

References 22 publications

Proto/Early Renaissance Depictions, Iconographic Analysis and Computerised Face Recognition Connections: 16th Century Frescoes of St. Leocadia Church (Chaves, North of Portugal)

Proto/Early Renaissance Depictions, Iconographic Analysis and Computerised Face Recognition Connections: 16th Century Frescoes of St. Leocadia Church (Chaves, North of Portugal)

Subjective Performance Assessment Protocol for Visual Explanations-based Face Verification Explainability

PIC-Score: Probabilistic Interpretable Comparison Score for Optimal Matching Confidence in Single- and Multi-Biometric Face Recognition

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