In this paper we develop a Quality Assessment approach for face recognition based on deep learning. The method consists of a Convolutional Neural Network, FaceQnet, that is used to predict the suitability of a specific input image for face recognition purposes. The training of FaceQnet is done using the VGGFace2 database. We employ the BioLab-ICAO framework for labeling the VGGFace2 images with quality information related to their ICAO compliance level. The groundtruth quality labels are obtained using FaceNet to generate comparison scores. We employ the groundtruth data to fine-tune a ResNet-based CNN, making it capable of returning a numerical quality measure for each input image. Finally, we verify if the FaceQnet scores are suitable to predict the expected performance when employing a specific image for face recognition with a COTS face recognition system. Several conclusions can be drawn from this work, most notably: 1) we managed to employ an existing ICAO compliance framework and a pretrained CNN to automatically label data with quality information, 2) we trained FaceQnet for quality estimation by fine-tuning a pre-trained face recognition network (ResNet-50), and 3) we have shown that the predictions from FaceQnet are highly correlated with the face recognition accuracy of a state-of-the-art commercial system not used during development. FaceQnet is publicly available in GitHub.
Another approach is described in @cite_14 . The authors presented the BioLab-ICAO framework, an evaluation tool for automatic ICAO compliance checking. The paper defined @math different individual tests performed by the framework for each input image. The output consists of a numerical score for each test, going from @math to @math . Those @math individual scores were nevertheless not integrated into a final single quality metric.
@cite_14
2024804726 2069094335 2108706252 2075655036
Face images to be included into machine readable travel documents have to fulfill quality requirements defined by international ISO standards. The concept of quality in this context extends the common idea of image quality: usually a bad quality image presents visual defects such as blurring or noise while, according to ISO ICAO standard, other factors could make a given sample a poor quality image (e.g., presence of dark glasses or mouth open). The verification of face image conformance to ISO ICAO standards is carried out mostly by humans today, through visual inspection, since a totally automatic evaluation is still not satisfactory. The objective of this work is to present the BioLab-ICAO framework, an evaluation benchmark which will be made available to the scientific community, designed to encourage the research on this topic; it consists of a large ground truth database, a well-defined testing protocol, and baseline algorithms for image compliance verification. This work provides a MATLAB-based framework for image and video quality evaluation that integrates test content generation, subjective testing interfaces, objective quality metric evaluation and correlation analysis into a single application. It also provides an HTML-based help system and a Graphical User Interface that makes applications based on the framework user friendly. The framework supports a configurable interface that makes it easy to integrate existing or new quality metrics and to compare them against each other. The framework abstracts the user from underlying details like file format, report generation and test flow control. Biomedical Natural Language Processing (BioNLP) attempts to capture biomedical phenomena from texts by extracting relations between biomedical entities (i.e. proteins and genes). Traditionally, only binary relations have been extracted from large numbers of published papers. Recently, more complex relations (biomolecular events) have also been extracted. Such events may include several entities or other relations. To evaluate the performance of the text mining systems, several shared task challenges have been arranged for the BioNLP community. With a common and consistent task setting, the BioNLP'09 shared task evaluated complex biomolecular events such as binding and regulation.Finding these events automatically is important in order to improve biomedical event extraction systems. In the present paper, we propose an automatic event extraction system, which contains a model for complex events, by solving a classification problem with rich features. The main contributions of the present paper are: (1) the proposal of an effective bio-event detection method using machine learning, (2) provision of a high-performance event extraction system, and (3) the execution of a quantitative error analysis. The proposed complex (binding and regulation) event detector outperforms the best system from the BioNLP'09 shared task challenge. We describe a system for extracting complex events among genes and proteins from biomedical literature, developed in context of the BioNLP'09 Shared Task on Event Extraction. For each event, its text trigger, class, and arguments are extracted. In contrast to the prevailing approaches in the domain, events can be arguments of other events, resulting in a nested structure that better captures the underlying biological statements. We divide the task into independent steps which we approach as machine learning problems. We define a wide array of features and in particular make extensive use of dependency parse graphs. A rule-based post-processing step is used to refine the output in accordance with the restrictions of the extraction task. In the shared task evaluation, the system achieved an F-score of 51.95 on the primary task, the best performance among the participants.