Biometric applications, such as person re-identification (ReID), are often deployed on energy constrained devices. While recent ReID methods prioritize high retrieval performance, they often come with large computational costs and high search time, rendering them less practical in real-world settings. In this work, we propose an input-adaptive network with multiple exit blocks, that can terminate computation early if the retrieval is straightforward or noisy, saving a lot of computation. To assess the complexity of the input, we introduce a temporal-based classifier driven by a new training strategy. Furthermore, we adopt a binary hash code generation approach instead of relying on continuous-valued features, which significantly improves the search process by a factor of 20. To ensure similarity preservation, we utilize a new ranking regularizer that bridges the gap between continuous and binary features. Extensive analysis of our proposed method is conducted on three datasets: Market1501, MSMT17 (Multi-Scene Multi-Time), and the BGC1 (BRIAR Government Collection). Using our approach, more than 70% of the samples with compact hash codes exit early on the Market1501 dataset, saving 80% of the networks computational cost and improving over other hash-based methods by 60%. These results demonstrate a significant improvement over dynamic networks and showcase comparable accuracy performance to conventional ReID methods.

While biometric-face and whole-body-recognition technology have recently advanced and matured, there are increasing interests in enhanced long-range recognition capabilities. However, long-range recognition requires the use of large, specialized datasets to support research and development for next generation systems. Moreover, existing datasets are further limited by the types of modalities (face or whole-body), number of subjects, maximum standoff distance, clothing variability, or availability restrictions. For long-range recognition, low-quality probe (query) images, which are often acquired from extended standoff distances or aerial platforms, are matched against higher quality gallery images and frequently results in poor identification performance. To address the growing needs for relevant data sources, a large-scale biometric dataset was collected and curated for long-range biometric recognition. This dataset is comprised of more than 1.2 million outdoor and 250,000 indoor (face and whole-body) images from more than 250 subjects that were acquired using various high-end cameras, including Canon and Nikon DSLR cameras, surveillance cameras, specialized long-range face cameras, and UAV platforms. The primary goal of this dataset is to support the development of algorithms for face and whole-body recognition at extended standoff distances. The availability of such a dataset is crucial in advancing technology for recognition under challenging conditions such as atmospheric turbulence.

Face and person recognition have recently achieved remarkable success under challenging scenarios, such as off-pose and cross-spectrum matching. However, long-range recognition systems are often hindered by atmospheric turbulence, leading to spatially and temporally varying distortions in the image. Current solutions rely on generative models to reconstruct a turbulent-free image, but often preserve photo-realism instead of discriminative features that are essential for recognition. This can be attributed to the lack of large-scale datasets of turbulent and pristine paired images, necessary for optimal reconstruction. To address this issue, we propose a new weakly supervised framework that employs a parameter-efficient self-attention module to generate domain agnostic representations, aligning turbulent and pristine images into a common subspace. Additionally, we introduce a new tilt map estimator that predicts geometric distortions observed in turbulent images. This estimate is used to re-rank gallery matches, resulting in up to 13.86% improvement in rank-1 accuracy. Our method does not require synthesizing turbulent-free images or ground-truth paired images, and requires significantly fewer annotated samples, enabling more practical and rapid utility of increasingly large datasets. We analyze our framework using two datasets—Long-Range Face Identification Dataset (LRFID) and BRIAR Government Collection 1 (BGC1)— achieving enhanced discriminability under varying turbulence and standoff distance.

Face signatures, including size, shape, texture, skin tone, eye color, appearance, and scars/marks, are widely used as discriminative, biometric information for access control. Despite recent advancements in facial recognition systems, presentation attacks on facial recognition systems have become increasingly sophisticated. The ability to detect presentation attacks or spoofing attempts is a pressing concern for the integrity, security, and trust of facial recognition systems. Multispectral imaging has been previously introduced as a way to improve presentation attack detection by utilizing sensors that are sensitive to different regions of the electromagnetic spectrum (e.g., visible, near infrared, long-wave infrared). Although multi-spectral presentation attack detection systems may be discriminative, the need for additional sensors and computational resources substantially increases complexity and costs. Instead, we propose a method that exploits information from infrared imagery during training to increase the discriminability of visible-based presentation attack detection systems. We introduce (1) a new cross-domain presentation attack detection framework that increases the separability of bonafide and presentation attacks using only visible spectrum imagery, (2) an inverse domain regularization technique for added training stability when optimizing our cross-domain presentation attack detection framework, and (3) a dense domain adaptation subnetwork to transform representations between visible and non-visible domains.

Recent advances in person re-identification have demonstrated enhanced discriminability, especially with supervised learning or transfer learning. However, since the data requirements-including the degree of data curations-are becoming increasingly complex and laborious, there is a critical need for unsupervised methods that are robust to large intra-class variations, such as changes in perspective, illumination, articulated motion, resolution, etc. Therefore, we propose an unsupervised framework for person re-identification which is trained in an end-to-end manner without any pre-training. Our proposed framework leverages a new attention mechanism that combines group convolutions to (1) enhance spatial attention at multiple scales and (2) reduce the number of trainable parameters by 59.6%. Additionally, our framework jointly optimizes the network with agglomerative clustering and instance learning to tackle hard samples. We perform extensive analysis using the Market1501 and DukeMTMC-reID datasets to demonstrate that our method consistently outperforms the state-of-the-art methods (with and without pre-trained weights).

The GIS industry relies heavily on manual efforts to build and maintain digital maps. This approach is timeconsuming and requires a sizable workforce not only for map-making but also for quality-checks that are required to resolve the potential errors resulting from manual digitization. With recent advancements in computer vision, several organizations are using machine-learning algorithms to generate map data from images. In the current machine learning based geometry creation process three limitations prevails. Firstly, the output of the algorithms is never served on-demand to a map editing tool. Secondly, after being further fine-tuned manually by annotators/validators, the results are never fed back to the algorithms to identify the errors incurred and improve accuracy. Finally, a lot of manual effort is required to create training data for new terrains and regions. We propose an end-to-end machine learning system integrated with current map-making tools to address these limitations and reduce the manual effort in creating and updating geometry.