17 research outputs found
Disentangling Racial Phenotypes: Fine-Grained Control of Race-related Facial Phenotype Characteristics
Achieving an effective fine-grained appearance variation over 2D facial images, whilst preserving facial identity, is a challenging task due to the high complexity and entanglement of common 2D facial feature encoding spaces. Despite these challenges, such fine-grained control, by way of disentanglement is a crucial enabler for data-driven racial bias mitigation strategies across multiple automated facial analysis tasks, as it allows to analyse, characterise and synthesise human facial diversity. In this paper, we propose a novel GAN framework to enable fine-grained control over individual race-related phenotype attributes of the facial images. Our framework factors the latent (feature) space into elements that correspond to race-related facial phenotype representations, thereby separating phenotype aspects (e.g. skin, hair colour, nose, eye, mouth shapes), which are notoriously difficult to annotate robustly in real-world facial data. Concurrently, we also introduce a high quality augmented, diverse 2D face image dataset drawn from CelebA-HQ for GAN training. Unlike prior work, our framework only relies upon 2D imagery and related parameters to achieve state-of-the-art individual control over race-related phenotype attributes with improved photo-realistic output
Seeing Through the Data: A Statistical Evaluation of Prohibited Item Detection Benchmark Datasets for X-ray Security Screening
The rapid progress in automatic prohibited object detection within the context of X-ray security screening, driven forward by advances in deep learning, has resulted in the first internationally-recognized, application-focused object detection performance standard (ECAC Common Testing Methodology for Automated Prohibited Item Detection Systems). However, the ever-increasing volume of detection work in this application area is highly reliant on a limited set of large-scale benchmark detection datasets that are specific to this domain. This study provides a comprehensive quantitative analysis of the underlying distribution of the prohibited item instances in three of the most prevalent X-ray security imagery benchmark and how these correlate against the detection performance of six state-of-the-art object detectors spanning multiple contemporary object detection paradigms. We focus on object size, location and aspect ratio within the image in addition to looking at global properties such as image colour distribution. Our results show a clear correlation between false negative (missed) detections and object size with the distribution of undetected items being statistically smaller in size than those typically found in the corresponding dataset as a whole. For false positive detections, the size distribution of such false alarm instances is shown to differ from the corresponding dataset test distribution in all cases. Furthermore, we observe that onestage, anchor-free object detectors may be more vulnerable to the detection of heavily occluded or cluttered objects than other approaches whilst the detection of smaller prohibited item instances such as bullets remains more challenging than other object types
Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentation
Whilst face recognition applications are becoming increasingly prevalent
within our daily lives, leading approaches in the field still suffer from
performance bias to the detriment of some racial profiles within society. In
this study, we propose a novel adversarial derived data augmentation
methodology that aims to enable dataset balance at a per-subject level via the
use of image-to-image transformation for the transfer of sensitive racial
characteristic facial features. Our aim is to automatically construct a
synthesised dataset by transforming facial images across varying racial
domains, while still preserving identity-related features, such that racially
dependant features subsequently become irrelevant within the determination of
subject identity. We construct our experiments on three significant face
recognition variants: Softmax, CosFace and ArcFace loss over a common
convolutional neural network backbone. In a side-by-side comparison, we show
the positive impact our proposed technique can have on the recognition
performance for (racial) minority groups within an originally imbalanced
training dataset by reducing the pre-race variance in performance.Comment: CVPR 2020 - Fair, Data Efficient and Trusted Computer Vision Worksho
Does lossy image compression affect racial bias within face recognition?
This study investigates the impact of commonplace lossy image compression on face recognition algorithms with regard to the racial characteristics of the subject. We adopt a recently proposed racial phenotype-based bias analysis methodology to measure the effect of varying levels of lossy compression across racial phenotype categories. Additionally, we determine the relationship between chroma-subsampling and race-related phenotypes for recognition performance. Prior work investigates the impact of lossy JPEG compression algorithm on contemporary face recognition performance. However, there is a gap in how this impact varies with different race-related inter-sectional groups and the cause of this impact. Via an extensive experimental setup, we demonstrate that common lossy image compression approaches have a more pronounced negative impact on facial recognition performance for specific racial phenotype categories such as darker skin tones (by up to 34.55%). Furthermore, removing chromasubsampling during compression improves the false matching rate (up to 15.95%) across all phenotype categories affected by the compression, including darker skin tones, wide noses, big lips, and monolid eye categories. In addition, we outline the characteristics that may be attributable as the underlying cause of such phenomenon for lossy compression algorithms such as JPEG
Measuring Hidden Bias within Face Recognition via Racial Phenotypes
Recent work reports disparate performance for intersectional racial groups across face recognition tasks: face verification and identification. However, the definition of those racial groups has a significant impact on the underlying findings of such racial bias analysis. Previous studies define these groups based on either demographic information (e.g. African, Asian etc.) or skin tone (e.g. lighter or darker skins). The use of such sensitive or broad group definitions has disadvantages for bias investigation and subsequent counter-bias solutions design. By contrast, this study introduces an alternative racial bias analysis methodology via facial phenotype attributes for face recognition. We use the set of observable characteristics of an individual face where a race-related facial phenotype is hence specific to the human face and correlated to the racial profile of the subject. We propose categorical test cases to investigate the individual influence of those attributes on bias within face recognition tasks. We compare our phenotypebased grouping methodology with previous grouping strategies and show that phenotype-based groupings uncover hidden bias without reliance upon any potentially protected attributes or ill-defined grouping strategies. Furthermore, we contribute corresponding phenotype attribute category labels for two face recognition tasks: RFW for face verification and VGGFace2 (test set) for face identification
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Human iPSC-derived fallopian tube organoids with BRCA1 mutation recapitulate early-stage carcinogenesis.
Germline pathogenic mutations in BReast CAncer (BRCA1) genes are thought to drive normal fallopian tube epithelial (FTE) cell transformation to high-grade serous ovarian cancer. No human models capture the sequence of events for disease initiation and progression. Here, we generate induced pluripotent stem cells (iPSCs) from healthy individuals and young ovarian cancer patients with germline pathogenic BRCA1 mutations (BRCA1mut). Following differentiation into FTE organoids, BRCA1mut lines exhibit cellular abnormalities consistent with neoplastic transformation compared to controls. BRCA1mut organoids show an increased production of cancer-specific proteins and survival following transplantation into mice. Organoids from women with the most aggressive ovarian cancer show the greatest pathology, indicating the potential value to predict clinical severity prior to disease onset. These human FTE organoids from BRCA1mut carriers provide a faithful physiological in vitro model of FTE lesion generation and early carcinogenesis. This platform can be used for personalized mechanistic and drug screening studies
RFWD3–Mdm2 ubiquitin ligase complex positively regulates p53 stability in response to DNA damage
In unstressed cells, the tumor suppressor p53 is maintained at low levels by ubiquitin-mediated proteolysis mainly through Mdm2. In response to DNA damage, p53 is stabilized and becomes activated to turn on transcriptional programs that are essential for cell cycle arrest and apoptosis. Activation of p53 leads to accumulation of Mdm2 protein, a direct transcriptional target of p53. It is not understood how p53 is protected from degradation when Mdm2 is up-regulated. Here we report that p53 stabilization in the late phase after ionizing radiation correlates with active ubiquitination. We found that an E3 ubiquitin ligase RFWD3 (RNF201/FLJ10520) forms a complex with Mdm2 and p53 to synergistically ubiquitinate p53 and is required to stabilize p53 in the late response to DNA damage. This process is regulated by the DNA damage checkpoint, because RFWD3 is phosphorylated by ATM/ATR kinases and the phosphorylation mutant fails to stimulate p53 ubiquitination. In vitro experiments suggest that RFWD3 is a p53 E3 ubiquitin ligase and that RFWD3–Mdm2 complex restricts the polyubiquitination of p53 by Mdm2. Our study identifies RFWD3 as a positive regulator of p53 stability when the G1 cell cycle checkpoint is activated and provides an explanation for how p53 is protected from degradation in the presence of high levels of Mdm2