A multimodal face verification process is presented for standard 2D color images, 2.5D range images and 3D meshes. A normalization in orientation and position is essential for 2.5D and 3D images to obtain a corrected frontal image. This is achieved using the spin images of the nose tip and both eyes, which feed an SVM classifier. First, a traditional principal component analysis followed by an SVM classifier are applied to both 2D and 2.5D images. Second, an iterative closest point algorithm is used to match 3D meshes. In all cases, the equal error rate is computed for different kinds of images in the training and test phases. In general, 2.5D range images show the best results (0.1% EER for frontal images). A special improvement in success rate for turned faces has been obtained for normalized 2.5D and 3D images compared to standard 2D images.

Gender classification of human faces is a trending topic and a remarkable biometric task. This research area has useful applications in several fields, such as automated border control (ABC) and forensic work. There are many approaches to gender classification in the literature; the classical approaches usually use real faces. Although good performances have been achieved, data collection remains a problem. Additionally, the privacy of individuals must be included in many existing works. These drawbacks can be overcome by using fake faces. Recently, the creation of a robust fake face corpus using machine learning has become possible. Our main contribution in the present paper is to experimentally investigate the ability of an artificial deepfake corpus to be a substitute for real corpora in facial gender classification tasks. We propose a deep learning-based approach using convolutional neural networks trained with fake faces and tested on real faces. By exploiting artificial faces, data collection obstacles are resolved for the training step, and privacy is highly preserved. Four classifiers based on popular convolutional neural network architectures were implemented. In the test phase, we used faces of real identities extracted from well-known experimental databases such as Face Recognition Technology (FERET), Faculdade de Engenharia Industrial (FEI) faces, Face Recognition and Artificial Vision (FRAV) and Labeled Faces in the Wild (LFW). The results achieved are very promising. We obtained high accuracy rates and low EER scores. They are similar to those of research works using real faces. As a result of this work, we propose a gender-labeled deepfake facial dataset containing more than 200k deepfake corpora that we will make available upon request for research purposes.

This paper presents a novelty system for the detection of driving-risk situations based on the knowledge acquired from traffic safety experts. A complete methodology to generate a driving-risk reference signal has been developed. A set of driving sessions was executed in a very realistic truck simulator, where several measures and visual information from the vehicle, the driver, and the road were collected. Two kinds of experiments were designed, i.e., controlled driving sessions (where several risk situations were induced) and natural driving sessions (where no risk situations were induced and a natural driving behavior was expected). A group of traffic safety experts from the Royal Automobile Club of Spain was consulted to evaluate the driving risk in each simulated session. The information acquired from the traffic safety experts was used to develop a methodology to combine their evaluations. The risks detected with the proposed methodology were analyzed to determine the most common human factors related with the generation of driving-risk situations.

Automated border control systems are the first critical infrastructure point when crossing a border country. Crossing border lines for unauthorized passengers is a high security risk to any country. This paper presents a multispectral analysis of presentation attack detection for facial biometrics using the learned features from a convolutional neural network. Three sensors are considered to design and develop a new database that is composed of visible (VIS), near-infrared (NIR), and thermal images. Most studies are based on laboratory or ideal conditions-controlled environments. However, in a real scenario, a subject’s situation is completely modified due to diverse physiological conditions, such as stress, temperature changes, sweating, and increased blood pressure. For this reason, the added value of this study is that this database was acquired in situ. The attacks considered were printed, masked, and displayed images. In addition, five classifiers were used to detect the presentation attack. Note that thermal sensors provide better performance than other solutions. The results present better outputs when all sensors are used together, regardless of whether classifier or feature-level fusion is considered. Finally, classifiers such as KNN or SVM show high performance and low computational level.

A problem of current interest is how to emulate nature by acquiring information in a neuromorphic-like fashion; namely, by using configurable hardware and electronic systems to emulate the information gathering and processing strategies of biological systems. In this paper, we introduce BioCAMSHIFT, an algorithm for a bio-inspired system that acquires information via a neuromorphic process and uses it to track multiple objects. The system consists of a silicon retina that simulates the behavior of the human eye together with a communication system that uses an Address-Event Representation protocol to transmit information in a way analogous to that of biological neural systems. An unsupervised procedure, based on the CAMSHIFT algorithm, is then used for multi-object tracking. It takes advantage of the retina’s high event rate to adapt to the changing sizes of the objects in its field of view. The proposed system has been experimentally validated using a data set from Freeway 210 in Pasadena, California, demonstrating a significantly better improvement in terms of multi-vehicle detection and tracking performance over the current state of the art.

In this paper, we describe a new framework to combine experts’ judgments for the prevention of driving risks in a cabin truck. In addition, the methodology shows how to choose among the experts the one whose predictions fit best the environmental conditions. The methodology is applied over data sets obtained from a high immersive cabin truck simulator in natural driving conditions. A nonparametric model, based in Nearest Neighbors combined with Restricted Least Squared methods is developed. Three experts were asked to evaluate the driving risk using a Visual Analog Scale (VAS), in order to measure the driving risk in a truck simulator where the vehicle dynamics factors were stored. Numerical results show that the methodology is suitable for embedding in real time systems.