This paper presents a summary of the 2nd Latent in the Wild Fingerprint Recognition Competition held at the 2025 International Joint Conference on Biometrics. The competition has two tracks: latent fingerprint 1) recognition, and 2) quality assessment. It attracted a total of 12 participating teams from academia and industry for both tracks, representing 10 countries. In total, 8 valid submissions were evaluated by the organizers. The competition aimed to advance the state-of-the-art in latent fingerprint recognition and quality assessment by providing a challenging dataset of latent fingerprints collected in natural, non-ideal conditions. This paper summarizes the dataset, evaluation protocols, submitted methods, and the competition results.

The process of grouping and subsequently recognising objects in cluttered images is one laden with difficulties; however, results can be greatly enhanced if the inherent uncertainty of image-features is taken into account. This paper shows that starting with the individual edgel's uncertainty it is possible to calculate covariance-information for all derived quantities. This information can be used to choose between competing algorithms, selecting the one that produces the more reliable results, but also as an aid during the recognition process. The consequent application of error-propagation leads to a new formulation for the calculation of the cross-ratio, which is both robust and efficient in dealing with measured lines, and does not require knowledge about the vanishing point. Extensive Monte-Carlo simulations as well as the application to images of cluttered street-scenes demonstrate the robustness and suitability of the approach.

In any object recognition system a major and primary task is to associate those image features, within an image of a complex scene, that arise from an individual object. The key idea here is that a geometric class defined in 3D induces relationships in the image which must hold between points on the image outline (the perspective projection of the object). The resulting image constraints enable both identification and grouping of image features belonging to objects of that class. The classes include surfaces of revolution, canal surfaces (pipes) and polyhedra. Recognition proceeds by first recognising an object as belonging to one of the classes (for example a surface of revolution) and subsequently identifying the object (for example as a particular vase). This differs from conventional object recognition systems where recognition is generally targetted at particular objects. These classes also support the computation of 3D invariant descriptions including symmetry axes, canonical coordinate frames and projective signatures. The constraints and grouping methods are viewpoint invariant, and proceed with no information on object pose. We demonstrate the effectiveness of this class-based grouping on real, cluttered scenes using grouping algorithms developed for rotationally symmetric surfaces, canal-surfaces and polyhedra.>

We demonstrate that viewpoint-invariant representations can be obtained from images for a useful class of 3D smooth object. The class of surfaces are those generated as the envelope of a sphere of varying radius swept along an axis. This class includes canal surfaces and surfaces of revolution. The representations are computed, using only image information, from the symmetry set of the object's outline. They are viewpoint-invariant under weak-perspective imaging, and quasi-invariant to an excellent approximation under perspective imaging. To this approximation, the planar axis of a canal surface is recovered up to an affine ambiguity from perspective images. Examples are given of the representations obtained from real images, which demonstrate stability and object discrimination, for both canal surfaces and surfaces of revolution. Finally, the representations are used as the basis for a model-based object recognition system