Presentation Attack Detection (PAD) is essential for ensuring the security of face recognition (FR) systems, particularly in the context of mobile authentication in various sectors, such as online banking and government services. However, current PAD methods are often sensitive to the data domain, partly due to the limitations of training PAD datasets. In this paper, we introduce the SO-TERIA dataset, which provides captures of bona-fide and diverse Presentation Attacks (PAs) recorded using smart-phones. The dataset was collected responsibly from 70 consenting individuals, as opposed to web scraping. It includes face videos, motion data, and depth information (when available) as well as a novel projector-based replay attack. To demonstrate the utility of the SOTERIA dataset, we evaluate the vulnerability of a SOTA FR model (IRes-Net100) to the PAs in the dataset. We also analyze the PAD capabilities of a SOTA PAD model (DeepPixBis) through cross-dataset experiments as well as on real attacks observed in an industrial application. Our findings show the effectiveness and versatility of the SOTERIA dataset in advancing PAD research, in particular toward generalization.

This paper presents an approach for spotting and accurately localizing identity documents in the wild. Contrary to blind solutions that often rely on borders and corners detection, the proposed approach requires a classification a priori along with a list of predefined models. The matching and accurate localization are performed using specific ID document features. This process is especially difficult due to the intrinsic variable nature of ID models (text fields, multi-pass printing with offset, unstable layouts, added artifacts, blinking security elements, nonrigid materials). We tackle the problem by putting different combinations of features in competition within a multi-hypothesis exploration where only the best document quadrilateral candidate is retained thanks to a custom visual similarity metric. The idea is to find, in a given context, at least one feature able to correctly crop the document. The proposed solution has been tested and has shown its benefits on both the MIDV-500 academic dataset and an industrial one supposedly more representative of a real-life application.

Identity document verification consists on checking its conformity to one or eventually a set of authentic documents. This verification is usually performed through visible patterns matching. In this paper, we propose a new efficient visual descriptor for pattern comparison. As most of existing descriptors incorporate either color or spatial information; the proposed descriptor, called Grid-3CD, includes both information. This descriptor is based on color connected components (CC) extracted from a quantified image. It consists of a set of 6-tuples computed on a grid of pixels sampled from the color-quantified image. The 6-tuple of a given pixel describes the density, the mass center, the bounding box and the color of the CC that contains this pixel. The efficiency of this descriptor for identity document verification is shown using two strategies of pattern comparison. The first one is unsupervised and based on a distance measure whereas the second is supervised and based on one-class Support Vector Machine (SVM). The experimentation of the new descriptor on four datasets of identity documents totaling 3250 documents shows an average accuracy of about 90%, outperforming state-of-the-art descriptors.

This paper studies the problem of document image classification. More specifically, we address the classification of documents composed of few textual information and complex background (such as identity documents). Unlike most existing systems, the proposed approach simultaneously locates the document and recognizes its class. The latter is defined by the document nature (passport, ID, etc.), emission country, version, and the visible side (main or back). This task is very challenging due to unconstrained capturing conditions, sparse textual information, and varying components that are irrelevant to the classification, e.g. photo, names, address, etc. First, a base of document models is created from reference images. We show that training images are not necessary and only one reference image is enough to create a document model. Then, the query image is matched against all models in the base. Unknown documents are rejected using an estimated quality based on the extracted document. The matching process is optimized to guarantee an execution time independent from the number of document models. Once the document model is found, a more accurate matching is performed to locate the document and facilitate information extraction. Our system is evaluated on several datasets with up to 3042 real documents (representing 64 classes) achieving an accuracy of 96.6%.

This paper addresses the problem of machine printed and handwritten text separation in real noisy documents. We have proposed in a previous work a robust separation system relying on a proximity string segmentation algorithm. The extracted pseudo-lines and pseudo-words are used as basic blocks for classification. A multi-class support vector machine (SVM) with Gaussian kernel associates first an appropriate label to each pseudo-word. Then, the local neighborhood of each pseudo-word is studied in order to propagate the context and correct the classification errors. In this work, we first propose to model the separation problem by conditional random fields considering the horizontal neighborhood. As the considered neighborhood is too local to solve certain error cases, we have enhanced this method by using a more global context based on class dominance in the pseudo-line. The method has been evaluated on business documents. It separates handwritten and printed text with better scores (99.1% and 99.2% respectively), contrary to noise which is very random in these documents (90.1%).

We propose in this paper a new contextual modelling method for combining syntactic and structural information for the recognition of online handwritten mathematical expressions. Those models are used to find the most likely combination of segmentation/recognition hypotheses proposed by a 2D segment or. Models are based on structural information concerning the layouts of symbols. They are learned from a mathematical expressions dataset to prevent the use of heuristic rules which are fuzzy by nature. The system is tested with a large base of synthetic expressions and also with a set of real complex expressions.

We discuss in this paper some issues related to the problem of mathematical expression recognition. The very first important issue is to define how to ground truth a dataset of handwritten mathematical expressions, and next we have to face the problem of benchmarking systems. We propose to define some indicators and the way to compute them so as they reflect the actual performances of a given system.

In this paper, we propose a new framework for online handwritten mathematical expression recognition. The proposed architecture aims at handling mathematical expression recognition as a simultaneous optimization of symbol segmentation, symbol recognition, and 2D structure recognition under the restriction of a mathematical expression grammar. To achieve this goal, we consider a hypothesis generation mechanism supporting a 2D grouping of elementary strokes, a cost function defining the global likelihood of a solution, and a dynamic programming scheme giving at the end the best global solution according to a 2D grammar and a classifier. As a classifier, a neural network architecture is used; it is trained within the overall architecture allowing rejecting incorrect segmented patterns. The proposed system is trained with a set of synthetic online handwritten mathematical expressions. When tested on a set of real complex expressions, the system achieves promising results at both symbol and expression interpretation levels.