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 $5\, \times 5\, \mathrm{px}$ resolution, the verification performance drops from $99.23\%$ increasingly down to almost $55\%$. 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 $50\%$ 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 $70\%$. 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 naïve 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