Large-scale face recognition datasets are collected by crawling the Internet and without individuals' consent, raising legal, ethical, and privacy concerns. With the recent advances in generative models, recently several works proposed generating synthetic face recognition datasets to mitigate concerns in web-crawled face recognition datasets. This paper presents the summary of the Synthetic Data for Face Recognition (SDFR) Competition held in conjunction with the 18th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2024) and established to investigate the use of synthetic data for training face recognition models. The SDFR competition was split into two tasks, allowing participants to train face recognition systems using new synthetic datasets and/or existing ones. In the first task, the face recognition backbone was fixed and the dataset size was limited, while the second task provided almost complete freedom on the model backbone, the dataset, and the training pipeline. The submitted models were trained on existing and also new synthetic datasets and used clever methods to improve training with synthetic data. The submissions were evaluated and ranked on a diverse set of seven benchmarking datasets. The paper gives an overview of the submitted face recognition models and reports achieved performance compared to baseline models trained on real and synthetic datasets. Furthermore, the evaluation of submissions is extended to bias assessment across different demography groups. Lastly, an outlook on the current state of the research in training face recognition models using synthetic data is presented, and existing problems as well as potential future directions are also discussed.

Real-world camera networks are often characterized by very wide baselines covering a wide range of viewpoints. We describe a method not only calibrating each camera sequence added to the system automatically, but also taking advantage of multi-view correspondences to make the entire calibration framework more robust. Novel camera sequences can be seamlessly integrated into the system at any time, adding to the robustness of future computations. One of the challenges consists in establishing correspondences between cameras. Initializing a bag of features from a calibrated frame, correspondences between cameras are established in a two-step procedure. First, affine invariant features of camera sequences are warped into a common coordinate frame and a coarse matching is obtained between the collected features and the incrementally built and updated bag of features. This allows us to warp images to a common view. Second, scale invariant features are extracted from the warped images. This leads to both more numerous and more accurate correspondences. Finally, the parameters are optimized in a bundle adjustment. Adding the feature descriptors and the optimized 3D positions to the bag of features, we obtain a feature-based scene abstraction, allowing for the calibration of novel sequences and the correction of drift in single-view calibration tracking. We demonstrate that our approach can deal with wide baselines. Novel sequences can seamlessly be integrated in the calibration framework.

It is widely agreed that efficient visual search requires the integration of target-driven top-down information and image-driven bottom-up information. Yet the problem of gaze planning - that is, selecting the next best gaze location given the current observations - remains largely unsolved. We propose a probabilistic system that models the gaze sequence as a finite-horizon Bayesian sequential decision process. Direct policy search is used to reason about the next best gaze locations. The system integrates bottom-up saliency information, top-down target knowledge and additional context information through principled Bayesian priors. This results in proposal gaze locations that depend not only the featural visual saliency, but also on prior knowledge and the spatial likelihood of locating the target. The system has been implemented using state-of- the-art object detectors and evaluated on a real-world dataset by comparing it to gaze sequences proposed by a pure bottom-up saliency-based process and to an object detection approach that analyzes the full image. The target-directed attention system is shown to result in higher object detection precision than both competitors, to attend to more relevant targets than the bottom-up attention system, and to require significantly less computation time than the exhaustive approach.

Categorization of scenes is a fundamental process of human vision that allows us to efficiently and rapidly analyze our surroundings. Several studies have explored the processes underlying human scene categorization, but they have focused on processing global image information. In this study, we present both psychophysical and computational experiments that investigate the role of local versus global image information in scene categorization. In a first set of human experiments, categorization performance is tested when only local or only global image information is present. Our results suggest that humans rely on local, region-based information as much as on global, configural information. In addition, humans seem to integrate both types of information for intact scene categorization. In a set of computational experiments, human performance is compared to two state-of-the-art computer vision approaches that have been shown to be psychophysically plausible and that model either local or global information. In addition to the influence of local versus global information, in a second series of experiments, we investigated the effect of color on the categorization performance of both the human observers and the computational model. Analysis of the human data suggests that color is an additional channel of perceptual information that leads to higher categorization results at the expense of increased reaction times in the intact condition. However, it does not affect reaction times when only local information is present. When color is removed, the employed computational model follows the relative performance decrease of human observers for each scene category and can thus be seen as a perceptually plausible model for human scene categorization based on local image information.

Natural scenes constitute a very heterogeneous stimulus class. Each semantic category contains exemplars of varying typicality. It is, therefore, an interesting question whether humans can categorize natural scenes consistently into a relatively small number of categories, such as, coasts, rivers/lakes, forests, plains, and mountains. This is particularly important for applications, such as, image retrieval systems. Only if typicality is consistently perceived across different individuals, a general image-retrieval system makes sense. In this study, we use psychophysics and computational modeling to gain a deeper understanding of scene typicality. In the first psychophysical experiment, we used a forced-choice categorization task in which each of 250 natural scenes had to be classified into one of the following five categories: coasts, rivers/lakes, forests, plains, and mountains. In the second experiment, the typicality of each scene had to be rated on a 50-point scale for each of the five categories. The psychophysical results show high consistency between participants not only in the categorization of natural scenes, but also in the typicality ratings. In order to model human perception, we then employ a computational approach that uses an intermediatesemantic modeling stepby extracting local semantic concepts, such as, rock, water, and sand. Based on the human typicality ratings, we learn a psychophysically plausible distance measure that leads to a high correlation between the computational and the human ranking of natural scenes. Interestingly, model comparisons without a semantic-modeling step correlated much less with human performance, suggesting that our model is psychophysically very plausible.

The majority of today's content based image retrieval systems rely on low-level image descriptors which limit their capability to support meaningful interactions with the users. Even though relevance feedback helps, most of the current interaction paradigms are far from the semantic representations which most people use to categorize and describe image content. Therefore we propose a concept called "vocabulary-supported image retrieval" which aims to enable the user to access an image database in a more natural way. In particular this paper develops a technique to predict the system's performance with respect to the user query. This allows the system to translate the user query into an internal query which may satisfy predefined criteria such as precision and recall rates. In addition, given the performance parameters of the system's sub-components, the feasibility and the success of the retrieval process can be evaluated beforehand and optimized dynamically online.

The effect of circuit level distortions on system level performance is becoming an important issue in optimizing the design of low power receivers. Circuit level distortions which in the past were insignificant in full power designs, must be accounted for in the link budgets of low power systems. In this paper, we present an analysis that demonstrates and quantifies the effect of one such distortion. We present a closed form expression for the BER of differentially detected /spl pi//4 DQPSK in the presence of AWGN and distorted by an IQ phase imbalance in the receiver.

The effect of circuit level distortions on system level performance is becoming an important issue in optimizing the design of low power receivers. Circuit level distortions that can be largely ignored in full power designs, must be accounted for in the link budget of low power systems. In this paper, we present an analysis that demonstrates and quantifies the effect of one such distortion. We present a closed form expression for the BER of differentially detected /spl pi//4 DQPSK in the presence of AWGN and distorted by a quadrature gain imbalance in the receiver.