1,417 research outputs found
Image Resolution Susceptibility of Face Recognition Models
Face recognition approaches often rely on equal image resolution for
verification faces on two images. However, in practical applications, those
image resolutions are usually not in the same range due to different image
capture mechanisms or sources. In this work, we first analyze the impact of
image resolutions on the face verification performance with a state-of-the-art
face recognition model. For images, synthetically reduced to resolution, the verification performance drops from
increasingly down to almost . Especially, for cross-resolution image
pairs (one high- and one low-resolution image), the verification accuracy
decreases even further. We investigate this behavior more in-depth by looking
at the feature distances for every 2-image test pair. To tackle this problem,
we propose the following two methods: 1) Train a state-of-the-art
face-recognition model straightforward with low-resolution images
directly within each batch. \\ 2) Train a siamese-network structure and adding
a cosine distance feature loss between high- and low-resolution features. Both
methods show an improvement for cross-resolution scenarios and can increase the
accuracy at very low resolution to approximately . However, a
disadvantage is that a specific model needs to be trained for every
resolution-pair ...Comment: 19 pages, 15 figures, 2 table
Cross-Quality LFW: A Database for Analyzing Cross-Resolution Image Face Recognition in Unconstrained Environments
Real-world face recognition applications often deal with suboptimal image
quality or resolution due to different capturing conditions such as various
subject-to-camera distances, poor camera settings, or motion blur. This
characteristic has an unignorable effect on performance. Recent
cross-resolution face recognition approaches used simple, arbitrary, and
unrealistic down- and up-scaling techniques to measure robustness against
real-world edge-cases in image quality. Thus, we propose a new standardized
benchmark dataset and evaluation protocol derived from the famous Labeled Faces
in the Wild (LFW). In contrast to previous derivatives, which focus on pose,
age, similarity, and adversarial attacks, our Cross-Quality Labeled Faces in
the Wild (XQLFW) maximizes the quality difference. It contains only more
realistic synthetically degraded images when necessary. Our proposed dataset is
then used to further investigate the influence of image quality on several
state-of-the-art approaches. With XQLFW, we show that these models perform
differently in cross-quality cases, and hence, the generalizing capability is
not accurately predicted by their performance on LFW. Additionally, we report
baseline accuracy with recent deep learning models explicitly trained for
cross-resolution applications and evaluate the susceptibility to image quality.
To encourage further research in cross-resolution face recognition and incite
the assessment of image quality robustness, we publish the database and code
for evaluation.Comment: 9 pages, 4 figures, 2 table
Octuplet Loss: Make Face Recognition Robust to Image Resolution
Image resolution, or in general, image quality, plays an essential role in
the performance of today's face recognition systems. To address this problem,
we propose a novel combination of the popular triplet loss to improve
robustness against image resolution via fine-tuning of existing face
recognition models. With octuplet loss, we leverage the relationship between
high-resolution images and their synthetically down-sampled variants jointly
with their identity labels. Fine-tuning several state-of-the-art approaches
with our method proves that we can significantly boost performance for
cross-resolution (high-to-low resolution) face verification on various datasets
without meaningfully exacerbating the performance on high-to-high resolution
images. Our method applied on the FaceTransformer network achieves 95.12% face
verification accuracy on the challenging XQLFW dataset while reaching 99.73% on
the LFW database. Moreover, the low-to-low face verification accuracy benefits
from our method. We release our code to allow seamless integration of the
octuplet loss into existing frameworks
Explainable Model-Agnostic Similarity and Confidence in Face Verification
Recently, face recognition systems have demonstrated remarkable performances
and thus gained a vital role in our daily life. They already surpass human face
verification accountability in many scenarios. However, they lack explanations
for their predictions. Compared to human operators, typical face recognition
network system generate only binary decisions without further explanation and
insights into those decisions. This work focuses on explanations for face
recognition systems, vital for developers and operators. First, we introduce a
confidence score for those systems based on facial feature distances between
two input images and the distribution of distances across a dataset. Secondly,
we establish a novel visualization approach to obtain more meaningful
predictions from a face recognition system, which maps the distance deviation
based on a systematic occlusion of images. The result is blended with the
original images and highlights similar and dissimilar facial regions. Lastly,
we calculate confidence scores and explanation maps for several
state-of-the-art face verification datasets and release the results on a web
platform. We optimize the platform for a user-friendly interaction and hope to
further improve the understanding of machine learning decisions. The source
code is available on GitHub, and the web platform is publicly available at
http://explainable-face-verification.ey.r.appspot.com
[2+2] Photocycloaddition Reactions of Eniminium Ions by Triplet Sensitization
In dieser Arbeit wurde der Triplett-Zustand von Eniminiumionen hinsichtlich seiner Reaktivität in [2+2]-Photocycloadditionen untersucht. Es wurde gezeigt, dass dieser Zustand durch Sensibilisierung populiert werden kann und energetisch niedriger liegt als der Triplett-Zustand der entsprechenden Carbonylverbindungen. Die intermolekularen [2+2]-Photocycloadditionen von chiralen Eniminiumionen lieferten die entsprechenden Cyclobutancarbaldehyde in guten Ausbeuten und hohen Enantiomerenüberschüssen.This thesis describes the investigation of the triplet state of eniminium ions in the context of its reactivity in [2+2] photocycloaddition reactions. It was shown that this triplet state can be populated by sensitization and that it is lower in energy than the triplet state of the corresponding carbonyl compounds. The intermolecular [2+2] photocycloadditions of chiral eniminium ions gave the corresponding cyclobutanecarbaldehydes in good yields and high enantiomeric excesses
Overexpression of uridine diphospho glucuronosyltransferase 2B17 in high risk chronic lymphocytic leukemia
Uridine diphospho glucuronosyltransferase 2B17 (UGT2B17) glucuronidates androgens and xenobiotics including certain drugs. The UGT2B17 gene shows a remarkable copy number variation (CNV), which predisposes for solid tumors and influences drug response. Here, we identify a yet undescribed UGT2B17 mRNA overexpression in poor-risk chronic lymphocytic leukemia (CLL). In total, 320 CLL patients and 449 healthy donors were analyzed. High (above median) UGT2B17 expression was associated with established CLL poor prognostic factors and resulted in shorter treatment-free and overall survival (hazard ratio ([death] 2.18; 95% CI 1.18-4.01; P = .013). The prognostic impact of mRNA expression was more significant than that of UGT2B17 CNV. UGT2B17 mRNA levels in primary CLL samples directly correlated with functional glucuronidation activity toward androgens and the anticancer drug vorinostat (R > 0.9, P < .001). After treatment with fludarabine containing regimens UGT2B17 was up-regulated particularly in poor responders (P = .030). We observed an exclusive involvement of the 2B17 isoform within the UGT protein family. Gene expression profiling of a stable UGT2B17 knockdown in the CLL cell line MEC-1 demonstrated a significant involvement in key cellular processes. These findings establish a relevant role of UGT2B17 in CLL with functional consequences and potential therapeutic implications
Combining targeted and systematic prostate biopsy improves prostate cancer detection and correlation with the whole mount histopathology in biopsy naĂŻve and previous negative biopsy patients
OBJECTIVE: Guidelines for previous negative biopsy (PNB) cohorts with a suspicion of prostate cancer (PCa) after positive multiparametric (mp) magnetic-resonance-imaging (MRI) often favour the fusion-guided targeted prostate-biopsy (TB) only approach for Prostate Imaging-Reporting and Data System (PI-RADS) ≥3 lesions. However, recommendations lack direct biopsy performance comparison within biopsy naïve (BN) vs. PNB patients and its prognostication of the whole mount pathology report (WMPR), respectively. We suppose, that the combination of TB and concomitant TRUS-systematic biopsy (SB) improves the PCa detection rate of PI-RADS 2, 3, 4 or 5 lesions and the International Society of Urological Pathology (ISUP)-grade predictability of the WMPR in BN- and PNB patients. METHODS: Patients with suspicious mpMRI, elevated prostate-specific-antigen and/or abnormal digital rectal examination were included. All PI-RADS reports were intramurally reviewed for biopsy planning. We compared the PI-RADS score substratified TB, SB or combined approach (TB/SB) associated BN- and PNB-PCa detection rate. Furthermore, we assessed the ISUP-grade variability between biopsy cores and the WMPR. RESULTS: According to BN (n = 499) vs. PNB (n = 314) patients, clinically significant (cs) PCa was detected more frequently by the TB/SB approach (62 vs. 43%) than with the TB (54 vs. 34%) or SB (57 vs. 34%) (all p < 0.0001) alone. Furthermore, we observed that the TB/SB strategy detects a significantly higher number of csPCa within PI-RADS 3, 4 or 5 reports, both in BN and PNB men. In contrast, applied biopsy techniques were equally effective to detect csPCa within PI-RADS 2 lesions. In case of csPCa diagnosis the TB approach was more often false-negative in PNB patients (BN 11% vs. PNB 19%; p = 0.02). The TB/SB technique showed in general significantly less upgrading, whereas a higher agreement was only observed for the total and BN patient cohort. CONCLUSION: Despite csPCa is more frequently found in BN patients, the TB/SB method always detected a significantly higher number of csPCa within PI-RADS 3, 4 or 5 reports of our BN and PNB group. The TB/SB strategy predicts the ISUP-grade best in the total and BN cohort and in general shows the lowest upgrading rates, emphasizing its value not only in BN but also PNB patients
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