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 1 .
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Thanks to Mr. James Bond, we are aware that diamonds are forever but, are fingerprints? It is well known that biometrics brings to the security field a new paradigm; unlike traditional systems, individuals are not identified by something that they have or they know, but by what they are. While such an approach entails some clear advantages, an important question remains: is what we are today the same as what we will be tomorrow? This paper addresses such a key problem in the fingerprint modality based on a database of over 400K impressions coming from more than 250K different fingers. The database was acquired under real operational conditions and contains fingerprints from subjects aged 0-25 and 65-98 years. Fingerprint pairs were collected with a time difference that ranges between 0 and 7 years. Such a unique set of data has allowed us to analyze both the age and ageing effects, shedding some new light into issues, such as fingerprint permanence and fingerprint quality.
The friction ridge pattern is a 3D structure which, in its natural state, is not deformed by contact with a surface''. Building upon this rather trivial observation, the present work constitutes a first solid step towards a paradigm shift in fingerprint recognition from its very foundations. We explore and evaluate the feasibility to move from current technology operating on 2D images of elastically deformed impressions of the ridge pattern, to a new generation of systems based on full-3D models of the natural nondeformed ridge pattern itself. There are already a small number of previous studies that have already started scratching the surface of 3D fingerprint recognition and that should not go overlooked. However, the vast majority of these few successful approaches published so far, are based on the reconstruction of fingerprints from multiple 2D images acquired with different lighting conditions (photometric stereo 3D reconstruction) or acquired from different angles (stereo vision 3D reconstruction). Such reconstruction methods lead in general to 2D fingerprints wrapped over the overall volume of the finger. These volumetric fingerprints have shown some promising performance, but still miss the real depth information of the ridge pattern, which, in the best case scenario, is coarsely estimated during the error-prone reconstruction process. In the present work we take one step further, directly acquiring for the first time in a consistent and repeatable manner, full-3D fingerprint models stored as point-clouds, where each point is defined by its [x, y, z] coordinates. This way, the 3D data is directly measured by the sensor, with no post-processing reconstruction stage required. The complete recognition system developed represents as well an alternative to traditional technology based on minutiae detection. It shows that image-based processing algorithms and descriptors can be successfully applied to the new full-3D data, reaching very competitive results and confirming the high distinctiveness of the models.INDEX TERMS 3D data processing, fingerprint recognition, laser sensing.
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