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1. On Matching Visible to Passive Infrared Face Images Using Image Synthesis & Denoising

   https://doi.org/10.1109/FG.2017.129  

   Osia, Nnamdi; Bourlai, Thirimachos (June 2017, 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017))

   Performing a direct match between images from different spectra (i.e., passive infrared and visible) is challenging because each spectrum contains different information pertaining to the subject’s face. In this work, we investigate the benefits and limitations of using synthesized visible face images from thermal ones and vice versa in cross-spectral face recognition systems. For this purpose, we propose utilizing canonical correlation analysis (CCA) and manifold learning dimensionality reduction (LLE). There are four primary contributions of this work. First, we formulate the cross-spectral heterogeneous face matching problem (visible to passive IR) using an image synthesis framework. Second, a new processed database composed of two datasets consistent of separate controlled frontal face subsets (VIS-MWIR and VIS-LWIR) is generated from the original, raw face datasets collected in three different bands (visible, MWIR and LWIR). This multi-band database is constructed using three different methods for preprocessing face images before feature extraction methods are applied. There are: (1) face detection, (2) CSU’s geometric normalization, and (3) our recommended geometric normalization method. Third, a post-synthesis image denoising methodology is applied, which helps alleviate different noise patterns present in synthesized images and improve baseline FR accuracy (i.e. before image synthesis and denoising is applied) in practical heterogeneous FR scenarios. Finally, an extensive experimental study is performed to demonstrate the feasibility and benefits of cross-spectral matching when using our image synthesis and denoising approach. Our results are also compared to a baseline commercial matcher and various academic matchers provided by the CSU’s Face Identification Evaluation System. 

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2. Photo Sleuth: Combining Collective Intelligence and Computer Vision to Identify Historical Portraits

   Mohanty, Vikram; Thames, David; Luther, Kurt (January 2018, ACM Conference on Collective Intelligence (CI 2018))

   Identifying people in photographs is a critical task in a wide variety of domains, from national security [7] to journalism [14] to human rights investigations [1]. The task is also fundamentally complex and challenging. With the world population at 7.6 billion and growing, the candidate pool is large. Studies of human face recognition ability show that the average person incorrectly identifies two people as similar 20–30% of the time, and trained police detectives do not perform significantly better [11]. Computer vision-based face recognition tools have gained considerable ground and are now widely available commercially, but comparisons to human performance show mixed results at best [2,10,16]. Automated face recognition techniques, while powerful, also have constraints that may be impractical for many real-world contexts. For example, face recognition systems tend to suffer when the target image or reference images have poor quality or resolution, as blemishes or discolorations may be incorrectly recognized as false positives for facial landmarks. Additionally, most face recognition systems ignore some salient facial features, like scars or other skin characteristics, as well as distinctive non-facial features, like ear shape or hair or facial hair styles. This project investigates how we can overcome these limitations to support person identification tasks. By adjusting confidence thresholds, users of face recognition can generally expect high recall (few false negatives) at the cost of low precision (many false positives). Therefore, we focus our work on the “last mile” of person identification, i.e., helping a user find the correct match among a large set of similarlooking candidates suggested by face recognition. Our approach leverages the powerful capabilities of the human vision system and collaborative sensemaking via crowdsourcing to augment the complementary strengths of automatic face recognition. The result is a novel technology pipeline combining collective intelligence and computer vision. We scope this project to focus on identifying soldiers in photos from the American Civil War era (1861– 1865). An estimated 4,000,000 soldiers fought in the war, and most were photographed at least once, due to decreasing costs, the increasing robustness of the format, and the critical events separating friends and family [17]. Over 150 years later, the identities of most of these portraits have been lost, but as museums and archives increasingly digitize and publish their collections online, the pool of reference photos and information has never been more accessible. Historians, genealogists, and collectors work tirelessly to connect names with faces, using largely manual identification methods [3,9]. Identifying people in historical photos is important for preserving material culture [9], correcting the historical record [13], and recognizing contributions of marginalized groups [4], among other reasons. 

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3. Examining StyleGAN as a Utility-Preserving Face De-identification Method

   https://doi.org/10.56553/popets-2023-0114  

   Moosavi Khorzooghi, Seyyed Mohammad; Nilizadeh, Shirin (October 2023, Proceedings on Privacy Enhancing Technologies)

   Several face de-identification methods have been proposed to preserve users’ privacy by obscuring their faces. These methods, however, can degrade the quality of photos, and they usually do not preserve the utility of faces, i.e., their age, gender, pose, and facial expression. Recently, advanced generative adversarial network models, such as StyleGAN [ 33], have been proposed, which generate realistic, high-quality imaginary faces. In this paper, we investigate the use of StyleGAN in generating de-identified faces through style mixing, where the styles or features of the target face and an auxiliary face get mixed to generate a de-identified face that carries the utilities of the target face. We examined this de-identification method for preserving utility and privacy by implementing several face detection, verification, and identification attacks and conducting a user study. The results from our extensive experiments, human evaluation, and comparison with two state-of-the-art face de-identification methods, i.e., CIAGAN and DeepPrivacy, show that StyleGAN performs on par or better than these methods, preserving users’ privacy and images’ utility. In particular, the results of the machine learning-based experiments show that StyleGAN0-4 preserves utility better than CIAGAN and DeepPrivacy while preserving privacy at the same level. StyleGAN 0-3 preserves utility at the same level while providing more privacy. In this paper, for the first time, we also performed a carefully designed user study to examine both privacy and utility-preserving properties of StyleGAN 0-3, 0-4, and 0-5, as well as CIAGAN and DeepPrivacy from the human observers’ perspectives. Our statistical tests showed that participants tend to verify and identify StyleGAN 0-5 images easier than DeepPrivacy images. All the methods but StyleGAN 0-5 had significantly lower identification rates than CIAGAN. Regarding utility, as expected, StyleGAN 0-5 performed significantly better in preserving some attributes. Among all methods, on average, participants believe gender has been preserved the most while naturalness has been preserved the least. 

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4. Visual Expertise Explains Image Inversion Effects

   Gahl, Martha; Kulkarni, Shubham; Pathak, Nikhil; Russell, Alex; Cottrell, Garrison W (November 2023, Proceedings of Machine Learning Research Volume 243)

   Fumero, Marco; Rodolà, Emanuele; Domine, Clementine; Locatello, Francesco; Dziugaite, Gintare Karolina; Caron, Mathilde (Ed.)

   We present an anatomically-inspired neurocomputational model, including a foveated retina and the log-polar mapping from the visual field to the primary visual cortex, that recreates image inversion effects long seen in psychophysical studies. We show that visual expertise, the ability to discriminate between subordinate-level categories, changes the performance of the model on inverted images. We first explore face discrimination, which, in humans, relies on configural information. The log-polar transform disrupts configural information in an inverted image and leaves featural information relatively unaffected. We suggest this is responsible for the degradation of performance with inverted faces. We then recreate the effect with other subordinate-level category discriminators and show that the inversion effect arises as a result of visual expertise, where configural information becomes relevant as more identities are learned at the subordinate-level. Our model matches the classic result: faces suffer more from inversion than mono-oriented objects, which are more disrupted than non-mono-oriented objects when objects are only familiar at a basic-level, and simultaneously shows that expert-level discrimination of other subordinate-level categories respond similarly to inversion as face experts. 

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5. Re-identification of individuals in genomic datasets using public face images

   https://doi.org/10.1126/sciadv.abg3296  

   Venkatesaramani, Rajagopal; Malin, Bradley A.; Vorobeychik, Yevgeniy (November 2021, Science Advances)

   Recent studies suggest that genomic data can be matched to images of human faces, raising the concern that genomic data can be re-identified with relative ease. However, such investigations assume access to well-curated images, which are rarely available in practice and challenging to derive from photos not generated in a controlled laboratory setting. In this study, we reconsider re-identification risk and find that, for most individuals, the actual risk posed by linkage attacks to typical face images is substantially smaller than claimed in prior investigations. Moreover, we show that only a small amount of well-calibrated noise, imperceptible to humans, can be added to images to markedly reduce such risk. The results of this investigation create an opportunity to create image filters that enable individuals to have better control over re-identification risk based on linkage. 

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