5,757 research outputs found
Towards hand biometrics in mobile devices
The increasing demand of security oriented to mobile applications has raised the attention to biometrics, as a proper and suitable solution for providing secure environment to mobile devices. With this aim, this document presents a biometric system based on hand geometry oriented to mobile devices, involving a high degree of freedom in terms of illumination, hand rotation and distance to camera. The user takes a picture of their own hand in the free space, without requiring any flat surface to locate the hand, and without removals of rings, bracelets or watches. The proposed biometric system relies on an accurate segmentation procedure, able to isolate hands from any background; a feature extraction, invariant to orientation, illumination, distance to camera and background; and a user classification, based on k-Nearest Neighbor approach, able to provide an accurate results on individual identification. The proposed method has been evaluated with two own databases collected with a HTC mobile. First database contains 120 individuals, with 20 acquisitions of both hands. Second database is a synthetic database, containing 408000 images of hand samples in different backgrounds: tiles, grass, water, sand, soil and the like. The system is able to identify individuals properly with False Reject Rate of 5.78% and False Acceptance Rate of 0.089%, using 60 features (15 features per finger
Towards hand biometrics in mobile devices
The increasing demand of security oriented to mobile applications has raised the attention to biometrics, as a proper and suitable solution for providing secure environment to mobile devices. With this aim, this document presents a biometric system based on hand geometry oriented to mobile devices, involving a high degree of freedom in terms of illumination, hand rotation and distance to camera. The user takes a picture of their own hand in the free space, without requiring any flat surface to locate the hand, and without removals of rings, bracelets or watches. The proposed biometric system relies on an accurate segmentation procedure, able to isolate hands from any background; a feature extraction, invariant to orientation, illumination, distance to camera and background; and a user classification, based on k-Nearest Neighbor approach, able to provide an accurate results on individual identification. The proposed method has been evaluated with two own databases collected with a HTC mobile. First database contains 120 individuals, with 20 acquisitions of both hands. Second database is a synthetic database, containing 408000 images of hand samples in different backgrounds: tiles, grass, water, sand, soil and the like. The system is able to identify individuals properly with False Reject Rate of 5.78% and False Acceptance Rate of 0.089%, using 60 features (15 features per finger
Towards hand biometrics in mobile devices
The increasing demand of security oriented to mobile applications has raised the attention to biometrics, as a proper and suitable solution for providing secure environment to mobile devices. With this aim, this document presents a biometric system based on hand geometry oriented to mobile devices, involving a high degree of freedom in terms of illumination, hand rotation and distance to camera. The user takes a picture of their own hand in the free space, without requiring any flat surface to locate the hand, and without removals of rings, bracelets or watches. The proposed biometric system relies on an accurate segmentation procedure, able to isolate hands from any background; a feature extraction, invariant to orientation, illumination, distance to camera and background; and a user classification, based on k-Nearest Neighbor approach, able to provide an accurate results on individual identification. The proposed method has been evaluated with two own databases collected with a HTC mobile. First database contains 120 individuals, with 20 acquisitions of both hands. Second database is a synthetic database, containing 408000 images of hand samples in different backgrounds: tiles, grass, water, sand, soil and the like. The system is able to identify individuals properly with False Reject Rate of 5.78% and False Acceptance Rate of 0.089%, using 60 features (15 features per finger
A robustness verification system for mobile phone authentication based on gestures using Linear Discriminant Analysis
This article evaluates an authentication technique for mobiles based on gestures. Users create a remindful identifying gesture to be considered as their in-air signature. This work analyzes a database of 120 gestures of different vulnerability, obtaining an Equal Error Rate (EER) of 9.19% when robustness of gestures is not verified. Most of the errors in this EER come from very simple and easily forgeable gestures that should be discarded at enrollment phase. Therefore, an in-air signature robustness verification system using Linear Discriminant Analysis is proposed to infer automatically whether the gesture is secure or not. Different configurations have been tested obtaining a lowest EER of 4.01% when 45.02% of gestures were discarded, and an optimal compromise of EER of 4.82% when 19.19% of gestures were automatically rejected
Hand Image Segmentation by means of Gaussian Multiscale Aggregation for biometric applications
Applying biometrics to daily scenarios involves demanding requirements in terms of software and hardware. On the contrary, current biometric techniques are also being adapted to present-day devices, like mobile phones, laptops and the like, which are far from meeting the previous stated requirements. In fact, achieving a combination of both necessities is one of the most difficult problems at present in biometrics. Therefore, this paper presents a segmentation algorithm able to provide suitable solutions in terms of precision for hand biometric recognition, considering a wide range of backgrounds like carpets, glass, grass, mud, pavement, plastic, tiles or wood. Results highlight that segmentation accuracy is carried out with high rates of precision (F-measure 88%)), presenting competitive time results when compared to state-of-the-art segmentation algorithms time performanc
Combining reinforcement learning and conventional control to improve automatic guided vehicles tracking of complex trajectories
With the rapid growth of logistics transportation in the framework of Industry 4.0,
automated guided vehicle (AGV) technologies have developed speedily. These systems present two coupled control problems: the control of the longitudinal velocity,
essential to ensure the application requirements such as throughput and tag time,
and the trajectory tracking control, necessary to ensure the proper accuracy in loading and unloading manoeuvres. When the paths are very short or have abrupt
changes, the kinematic constraints play a restrictive role, and the tracking control
becomes more challenging. In this case, advanced control strategies such as those
based on intelligent techniques, including machine learning (ML) can be useful.
Hence, in this work, we present an intelligent hybrid control scheme that combines
reinforcement learning-based control (RLC) with conventional PI regulators to face
both control problems simultaneously. On the one hand, PIs are used to control the
speed of each wheel. On the other hand, the input reference of these regulators is
calculated by the RLC in order to reduce the guiding error of the path tracking and to
maintain the longitudinal speed. The latter is compared with a PID path following
controller. The PID regulators have been tuned by genetic algorithms. The RLC allows
the vehicle to learn how to improve the trajectory tracking in an adaptive way and
thus, the AGV can face disturbances or unknown physical system parameters that
may change due to friction and degradation of AGV mechanical components. Extensive simulation experiments of the proposed intelligent control strategy on a hybrid
tricycle and differential AGV model, that considers the kinematics and the dynamics
of the vehicle, prove the efficiency of the approach when following different
demanding trajectories. The performance of the RL tracking controller in comparison
with the optimized PID gives errors around 70% smaller, and the average maximum
error is also 48% lower.Open access funding enabled and organized by Projekt DEAL
Control de edad en redes sociales mediante biometría facial
Actualmente, las redes sociales se han instaurado como un mecamismo muy potente de comunicaci¿on y contacto entre individuos. Sin embargo, las pol¿?ticas de privacidad que normalmente han acompa?nado a estas redes sociales no han sido capaces de evitar el mal uso de las mismas en temas relacionados con protecci¿on a menores. El caso m¿as significativo es el de adultos, haci¿endose pasar por menores. Este trabajo investiga la viabilidad del uso de t¿ecnicas biom¿etricas basadas en rasgos faciales para la detecci¿on de rangos de edad, con el prop¿osito de evitar que adultos se hagan pasar por menores, o incluso que ciertos menores puedan acceder a redes sociales, cuyo acceso debe estar trestringido por su edad. Los resultados muestran que es posible hacer esta distinci¿on entre adultos y menores, seleccionando edades umbrales cercanas a los 18 a? nos, con tasas de acierto cercanas al 80 %, y empleando clasificadores basados en m¿aquinas de vector soporte (SVMs) lineales
Hybrid Optimized Fuzzy Pitch Controller of a Floating Wind Turbine with Fatigue Analysis
Floating offshore wind turbines (FOWTs) are systems with complex and highly nonlinear
dynamics; they are subjected to heavy loads, making control with classical strategies a challenge. In
addition, they experience vibrations due to wind and waves. Furthermore, the control of the blade
angle itself may generate vibrations. To address this issue, in this work we propose the design of
an intelligent control system based on fuzzy logic to maintain the rated power of an FOWT while
reducing the vibrations. A gain scheduling incremental proportional–derivative fuzzy controller is
tuned by genetic algorithms (GAs) and combined with a fuzzy-lookup table to generate the pitch
reference. The control gains optimized by the GA are stored in a database to ensure a proper operation
for different wind and wave conditions. The software Matlab/Simulink and the simulation tool FAST
are used. The latter simulates the nonlinear dynamics of a real 5 MW barge-type FOWT with irregular
waves. The hybrid control strategy has been evaluated against the reference baseline controller
embedded in FAST in different environmental scenarios. The comparison is assessed in terms of
output power and structure stability, with up to 23% and 33% vibration suppression rate for tower
top displacement and platform pitch, respectively, with the new control scheme. Fatigue damage
equivalent load (DEL) of the blades has been also estimated with satisfactory results.This work has been partially supported by the Spanish Ministry of Science and Innovation under the project MCI/AEI/FEDER number RTI2018-094902-B-C21 and PDI2021-123543OB-C21
Performance and Extreme Conditions Analysis Based on Iterative Modelling Algorithm for Multi-Trailer AGVs
Automatic guidance vehicles (AGV) are industrial vehicles that play an important role in
the development of smart manufacturing systems and Industry 4.0. To provide these autonomous
systems with the flexibility that is required today in these industrial workspaces, AGV computational
models are necessary in order to analyze their performance and design efficient planning and control
strategies. To address these issues, in this work, the mathematical model and the algorithm that
implement a computational control-oriented simulation model of a hybrid tricycle-differential AGV
with multi-trailers have been developed. Physical factors, such as wheel-ground interaction and the
effect of vertical and lateral loads on its dynamics, have been incorporated into the model. The model
has been tested in simulation with two different controllers and three trajectories: a circumference,
a square, and an s-shaped curve. Furthermore, it has been used to analyze extreme situations of
slipping and capsizing and the influence of the number of trailers on the tracking error and the
control effort. This way, the minimum lateral friction coefficient to avoid slipping and the minimum
ratio between the lateral and height displacement of the center of gravity to avoid capsizing have
been obtained. In addition, the effect of a change in the friction coefficient has also been simulated
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