Prevailing defense schemes against adversarial face images tend to overfit to the perturbations in the training set and fail to generalize to unseen adversarial attacks. We propose a new self-supervised adversarial defense framework, namely FaceGuard, that can automatically detect, localize, and purify a wide variety of adversarial faces without utilizing pre-computed adversarial training samples. During training, FaceGuard automatically synthesizes challenging and diverse adversarial attacks, enabling a classifier to learn to distinguish them from real faces. Concurrently, a purifier attempts to remove the adversarial perturbations in the image space. Experimental results on LFW, Celeb-A, and FFHQ datasets show that FaceGuard can achieve 99.81%, 98.73%, and 99.35% detection accuracies, respectively, on six unseen adversarial attack types. In addition, the proposed method can enhance the face recognition performance of ArcFace from 34.27% TAR @ 0.1% FAR under no defense to 77.46% TAR @ 0.1% FAR. Code, pre-trained models and dataset will be publicly available.

State-of-the-art defense mechanisms against face attacks achieve near perfect accuracies within one of three attack categories, namely adversarial, digital manipulation, or physical spoofs, however, they fail to generalize well when tested across all three categories. Poor generalization can be attributed to learning incoherent attacks jointly. To over-come this shortcoming, we propose a unified attack detection framework, namely UniFAD, that can automatically cluster 25 coherent attack types belonging to the three categories. Using a multi-task learning framework along with k-means clustering, UniFAD learns joint representations for coherent attacks, while uncorrelated attack types are learned separately. Proposed UniFAD outperforms prevailing defense methods and their fusion with an overall TDR = 94.73% @ 0.2% FDR on a large fake face dataset consisting of 341K bona fide images and 448K attack images of 25 types across all 3 categories. Proposed method can detect an attack within 3 milliseconds on a Nvidia 2080Ti. UniFAD can also identify the attack categories with 97.37% accuracy. Code and dataset will be publicly available.

Person re-identification (Re-ID) on aerial-ground platforms has emerged as an intriguing topic within computer vision, presenting a plethora of unique challenges. Highflying altitudes of aerial cameras make persons appear differently in terms of viewpoints, poses, and resolution compared to the images of the same person viewed from ground cameras. Despite its potential, few algorithms have been developed for person re-identification on aerial-ground data, mainly due to the absence of comprehensive datasets. In response, we have collected a large-scale dataset and organized the Aerial-Ground person Re-IDentification Challenge (AG-ReID2023) to foster advancements in the field. The dataset comprises 100,502 images with 1,615 unique identities, including 51,530 training images featuring 807 identities. The test set is divided into two subsets: Aerial to Ground (808 ids, 4,348 query images, 19,259 gallery images) and Ground to Aerial (808 ids, 4,151 query images, 21,214 gallery images). In addition, we manually annotate individuals with their matching IDs across cameras and provide 15 soft attribute labels. The AG-ReID2023 Challenge in conjunction with the 7thIEEE International Joint Conference on Biometrics (IJCB) has garnered interest from numerous institutes, resulting in the submission of five distinct algorithms. We provide an in-depth examination of the evaluation outcomes and present our findings from the contest. For additional details, kindly refer to the official website1.1https://agreid23.github.io.

The automated generation of 3D human heads has been an intriguing and challenging task for computer vision researchers. Prevailing methods synthesize realistic avatars but with limited control over the diversity and quality of rendered outputs and suffer from limited correlation between shape and texture of the character. We propose a method that offers quality, diversity, control, and realism along with explainable network design, all desirable features to game-design artists in the domain. First, our proposed Geometry Generator identifies disentangled latent directions and generate novel and diverse samples. A Render Map Generator then learns to synthesize multiply high-fidelty physically-based render maps including Albedo, Glossiness, Specular, and Normals. For artists preferring fine-grained control over the output, we introduce a novel Color Transformer Model that allows semantic color control over generated maps. We also introduce quantifiable metrics called Uniqueness and Novelty and a combined metric to test the overall performance of our model. Demo for both shapes & textures can be found: https://munch-seven.vercel.app/. We will release our model along with the synthetic dataset.

In many of the least developed and developing countries, a multitude of infants continue to suffer and die from vaccine-preventable diseases and malnutrition. Lamentably, the lack of official identification documentation makes it exceedingly difficult to track which infants have been vaccinated and which infants have received nutritional supplements. Answering these questions could prevent this infant suffering and premature death around the world. To that end, we propose Infant-Prints, an end-to-end, low-cost, infant fingerprint recognition system. Infant-Prints is comprised of our (i) custom built, compact, low-cost (85 USD), high-resolution (1,900 ppi), ergonomic fingerprint reader, and (ii) high-resolution infant fingerprint matcher. To evaluate the efficacy of Infant-Prints, we collected a longitudinal infant fingerprint database captured in 4 different sessions over a 12-month time span (December 2018 to January 2020), from 315 infants at the Saran Ashram Hospital, a charitable hospital in Dayalbagh, Agra, India. Our experimental results demonstrate, for the first time, that Infant-Prints can deliver accurate and reliable recognition (over time) of infants enrolled between the ages of 2-3 months, in time for effective delivery of vaccinations, healthcare, and nutritional supplements (TAR=95.2% @ FAR = 1.0% for infants aged 8-16 weeks at enrollment and authenticated 3 months later).

State-of-the-art presentation attack detection approaches tend to overfit to the presentation attack instruments seen during training and fail to generalize to unknown presentation attack instruments. Given that face presentation attack detection is inherently a local task, we propose a face presentation attack detection framework, namely Self-Supervised Regional Fully Convolutional Network (SSR-FCN), that is trained to learn local discriminative cues from a face image in a self-supervised manner. The proposed framework (i) improves generalizability while maintaining the computational efficiency of holistic face presentation attack detection approaches (<; 4 ms on a Nvidia GTX 1080Ti GPU), and (ii) is more interpretable since it localizes the parts of the face that are labeled as presentation attacks. Experimental results show that SSR-FCN can achieve TDR = 65% @ 2.0% FDR when evaluated on a dataset, SiW-M, comprising of 13 different presentation attack instruments under unknown attacks while achieving competitive performances under standard benchmark datasets (Oulu-NPU, CASIA-MFSD, and Replay-Attack).

Face recognition systems have been shown to be vulnerable to adversarial faces resulting from adding small perturbations to probe images. Such adversarial images can lead state-of-the-art face matchers to falsely reject a genuine subject (obfuscation attack) or falsely match to an impostor (impersonation attack). Current approaches to crafting adversarial faces lack perceptual quality and take an unreasonable amount of time to generate them. We propose, AdvFaces, an automated adversarial face synthesis method that learns to generate minimal perturbations in the salient facial regions via Generative Adversarial Networks. Once AdvFaces is trained, a hacker can automatically generate imperceptible face perturbations that can evade four black-box state-of-the-art face matchers with attack success rates as high as 97.22% and 24.30% at 0.1 % False Accept Rate, for obfuscation and impersonation attacks, respectively.

Given a face image of a recovered child at age ageprobe, we search a gallery of missing children with known identities and age agegallery at which they were either lost or stolen in an attempt to unite the recovered child with his family. We propose a feature aging module that can age-progress deep face features output by a face matcher to improve the recognition accuracy of age-separated child face images. In addition, the feature aging module guides age-progression in the image space such that synthesized aged gallery faces can be utilized to further enhance cross-age face matching accuracy of any commodity face matcher. For time lapses larger than 10 years (the missing child is recovered after 10 or more years), the proposed age-progression module improves the rank-1 open-set identification accuracy of CosFace from 22.91 % to 25.04% on a child celebrity dataset, namely ITWCC. The proposed method also outperforms state-of-the-art approaches with a rank-1 identification rate of 95.91 %, compared to 94.91 %, on a public aging dataset, FG-NET, and 99.58%, compared to 99.50%, on CACD-VS. These results suggest that aging face features enhances the ability to identify young children who are possible victims of child trafficking or abduction.

We propose, WarpGAN, a fully automatic network that can generate caricatures given an input face photo. Besides transferring rich texture styles, WarpGAN learns to automatically predict a set of control points that can warp the photo into a caricature, while preserving identity. We introduce an identity-preserving adversarial loss that aids the discriminator to distinguish between different subjects. Moreover, WarpGAN allows customization of the generated caricatures by controlling the exaggeration extent and the visual styles. Experimental results on a public domain dataset, WebCaricature, show that WarpGAN is capable of generating caricatures that not only preserve the identities but also outputs a diverse set of caricatures for each input photo. Five caricature experts suggest that caricatures generated by WarpGAN are visually similar to hand-drawn ones and only prominent facial features are exaggerated.