Comparison of body shape descriptors for biometric recognition using MMW images

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. E. González-Sosa, R. Vera-Rodríguez, Julián Fiérrez, J. Ortega-García, "Comparison of Body Shape Descriptors for Biometric Recognition using MMW Images" in 22nd International Conference on Pattern Recognition (ICPR), Stockholm (Sweden), 2014, 124 - 129.The use of Millimetre wave images has been proposed recently in the biometric field to overcome certain limitations when using images acquired at visible frequencies. In this paper, several body shape-based techniques were applied to model the silhouette of images of people acquired at 94 GHz. We put forward several methods for the parameterization and classification stage with the objective of finding the best configuration in terms of biometric recognition performance. Contour coordinates, shape contexts, Fourier descriptors and silhouette landmarks were used as feature approaches and for classification we utilized Euclidean distance and a dynamic programming method. Results showed that the dynamic programming algorithm improved the performance of the system with respect to the baseline Euclidean distance and the necessity of a minimum resolution of the contour to achieve promising equal error rates. The use of the contour coordinates is the most suitable feature to use in the system regarding the performance and the computational cost involved when having at least 3 images for model training. Besides, Fourier descriptors are more robust against rotations, which may be of interest when dealing with few training images.This work has been partially supported by projects TeraSense (CSD2008-00068), Bio-Shield (TEC2012-34881), Contexts (S2009/TIC-1485) and “Cátedra UAM-Telefónica”

Body shape-based biometric recognition using millimeter wave images

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. González-Sosa, E. ; Vera-Rodríguez, R. ; Fierrez, J. ; Ortega-García, J. "Body shape-based biometric recognition using millimeter wave images" in 47th International Carnahan Conference on Security Technology, Medellin, 2013, pp. 1-5Proceedings of 47th International Carnahan Conference on Security Technology, Medellin, October 2013The use of MMW images has been proposed recently in the biometric field aiming to overcome certain limitations when using images acquired at visible frequencies. In this paper, several body shape-based techniques are applied to model the silhouette of images of people acquired at 94 GHz. Three main approaches are presented: a baseline system based on the Euclidean distance, a dynamic programming method and a procedure using Shape Contexts descriptors. Results show that the dynamic time warping algorithm achieves the best results regarding the system performance (around 1.3% EER) and the computation cost. Results achieved here are also compared to previous works based on the extraction of geometric measures between several key points of the body contour. An average relative improvement of 33% EER is achieved for the work reported here.This work has been partially supported by projects TeraSense (CSD2008-00068), Bio-Shield (TEC2012-34881), Contexts (S2009/TIC-1485) and “Cátedra UAM-Telefónica”

Body shape-based biometric person recognition from mmW images

A growing interest has arisen in the security community for the use of millimeter waves in order to detect weapons and concealed objects. Also, the use of millimetre wave images has been proposed recently for biometric person recognition to overcome certain limitations of images acquired at visible frequencies. This paper proposes a biometric person recognition system based on shape information extracted from millimetre wave images. To this aim, we report experimental results using millimeter wave images with different body shape-based feature approaches: contour coordinates, shape contexts, Fourier descriptors and row and column profiles, using Dynamic Time Warping for matching. Results suggest the potential of performing person recognition through millimetre waves using only shape information, a functionality that could be easily integrated in the security scanners deployed in airportsThis work has been partially supported by project CogniMetrics TEC2015-70627-R (MINECO/FEDER), and the SPATEK network (TEC2015-68766-REDC

Reconocimiento biométrico basado en la forma del cuerpo usando imágenes en la banda MMW

Comunicación presentada en las Jornadas de Reconocimiento Biométrico de Personas (JRBP 2013)El uso de imágenes en la banda de MMW ha sido propuesto recientemente en el área del reconocimiento biométrico con el objetivo de solventar ciertas limitaciones que presentan los sistemas basados en imágenes adquiridas en frecuencias visibles. En este trabajo se aplican varias técnicas de modelado de forma para modelar la silueta de imágenes de personas adquiridas a 94 GHz. Se usan tres enfoques principales: un sistema básico basado en la distancia Euclídea, un método de programacion dinámica y un procedimiento utilizando los descriptores Shape Contexts. Los resultados muestran que la técnica de programacion dinámica consigue los mejores resultados en cuanto al rendimiento del sistema (en torno a 1.3% EER) y el coste computacional. Se lleva a cabo además una comparación con un trabajo previo basado en la extracción de distancias geométricas entre varios puntos de referencia del contorno del cuerpo. En este trabajo se consigue un 33% EER de mejora relativa media.Este trabajo ha sido nanciado parcialmente por los proyectos TeraSense (CSD2008- 00068), Bio-Shield (TEC2012-34881), Contexts (S2009/TIC-1485) y la Cátedra UAM-Telefónica

Dealing with occlusions in face recognition by region-based fusion

The last research efforts made in the face recognition community have been focusing in improving the robustness of systems under different variability conditions like change of pose, expression, illumination, low resolution and occlusions. Occlusions are also a manner of evading identification, which is commonly used when committing crimes or thefts. In this work we propose an approach based on the fusion of non occluded facial regions that is robust to occlusions in a simple and effective manner. We evaluate the region-based approach in three face recognition systems: Face++ (a commercial software based on CNN) and two advancements over LBP systems, one considering multiple scales and other considering a larger number of facial regions. We report experiments based on the ARFace database and prove the robustness of using only non-occluded facial regions, the effectiveness of a large number of regions and the limitations of the commercial system when dealing with occlusionsThis work has been partially supported by project Cogni- Metrics TEC2015-70627-R (MINECO/FEDER). E. Gonzalez- Sosa is supported by a PhD scholarship from Universidad Autonoma de Madri

Millimetre wave person recognition: hand-crafted vs learned features

Imaging using millimeter waves (mmWs) has many advantages including ability to penetrate obscurants such as clothes and polymers. Although conceal weapon detection has been the predominant mmW imaging application, in this paper, we aim to gain some insight about the potential of using mmW images for person recognition. We report experimental results using the mmW TNO database consisting of 50 individuals based on both hand-crafted and learned features from Alexnet and VGG-face pretrained CNN models. Results suggest that: i) mmW torso region is more discriminative than mmW face and the entire body, ii) CNN features produce better results compared to hand-crafted features on mmW faces and the entire body, and iii) hand-crafted features slightly outperform CNN features on mmW torsoThis work has been partially supported by project CogniMetrics TEC2015-70627-R (MINECO/FEDER), and the SPATEK network (TEC2015-68766-REDC). E. GonzalezSosa is supported by a PhD scholarship from Universidad Autonoma de Madrid. Vishal M. Patel was partially supported by US Office of Naval Research (ONR) Grant YIP N00014-16-1-3134. Authors wish to thank also TNO for providing access to the databas

Pose variability compensation using projective transformation for forensic face recognition

Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. E. Gonzalez-Sosa, R. Vera-Rodriguez, J. Fierrez, P. Tome and J. Ortega-Garcia, "Pose Variability Compensation Using Projective Transformation for Forensic Face Recognition," Biometrics Special Interest Group (BIOSIG), 2015 International Conference of the, Darmstadt, 2015, pp. 1-5. doi: 10.1109/BIOSIG.2015.7314615The forensic scenario is a very challenging problem within the face recognition community. The verification problem in this case typically implies the comparison between a high quality controlled image against a low quality image extracted from a close circuit television (CCTV). One of the downsides that frequently presents this scenario is pose deviation since CCTV devices are usually placed in ceilings and the subject normally walks facing forward. This paper proves the value of the projective transformation as a simple tool to compensate the pose distortion present in surveillance images in forensic scenarios. We evaluate the influence of this projective transformation over a baseline system based on principal component analysis and support vector machines (PCA-SVM) for the SCface database. The application of this technique improves greatly the performance, being this improvement more striking with closer images. Results suggest the convenience of this transformation within the preprocessing stage of all CCTV images. The average relative improvement reached with this method is around 30% of EER.This work has been partially supported in part by Bio-Shield (TEC2012-34881) from Spanish MINECO, in part by BEAT (FP7-SEC-284989) from EU and in part by Cátedra UAM-Telefónica. E. Gonzalez-Sosa is supported by a PhD scholarship from Universidad Autonoma de Madrid

Feature exploration for biometric recognition using millimetre wave body images

The electronic version of this article is the complete one and can be found online at: http://dx.doi.org/10.1186/s13640-015-0084-3The use of millimetre wave images has been proposed recently in the biometric field to overcome certain limitations when using images acquired at visible frequencies. Furthermore, the security community has started using millimetre wave screening scanners in order to detect concealed objects. We believe we can exploit the use of these devices by incorporating biometric functionalities. This paper proposes a biometric recognition system based on the information of the silhouette of the human body, which may be seen as a type of soft biometric trait. To this aim, we report experimental results on the BIOGIGA database with four feature extraction approaches (contour coordinates, shape contexts, Fourier descriptors and landmarks) and three classification methods (Euclidean distance, dynamic time warping and support vector machines). The best configuration of 1.33 % EER is achieved when using contour coordinates with dynamic time warping.This work has been partially supported by projects TeraSense (CSD2008-00068), Bio-Shield (TEC2012-34881) and BEAT (FP7-SEC-284989) from EU. E. Gonzalez-Sosa is supported by a PhD scholarship from Universidad Autonoma de Madrid

A Survey of Super-Resolution in Iris Biometrics With Evaluation of Dictionary-Learning

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThe lack of resolution has a negative impact on the performance of image-based biometrics. While many generic super-resolution methods have been proposed to restore low-resolution images, they usually aim to enhance their visual appearance. However, an overall visual enhancement of biometric images does not necessarily correlate with a better recognition performance. Reconstruction approaches thus need to incorporate the specific information from the target biometric modality to effectively improve recognition performance. This paper presents a comprehensive survey of iris super-resolution approaches proposed in the literature. We have also adapted an eigen-patches’ reconstruction method based on the principal component analysis eigen-transformation of local image patches. The structure of the iris is exploited by building a patch-position-dependent dictionary. In addition, image patches are restored separately, having their own reconstruction weights. This allows the solution to be locally optimized, helping to preserve local information. To evaluate the algorithm, we degraded the high-resolution images from the CASIA Interval V3 database. Different restorations were considered, with 15 × 15 pixels being the smallest resolution evaluated. To the best of our knowledge, this is the smallest resolutions employed in the literature. The experimental framework is complemented with six publicly available iris comparators that were used to carry out biometric verification and identification experiments. The experimental results show that the proposed method significantly outperforms both the bilinear and bicubic interpolations at a very low resolution. The performance of a number of comparators attains an impressive equal error rate as low as 5% and a Top-1 accuracy of 77%–84% when considering the iris images of only 15 × 15 pixels. These results clearly demonstrate the benefit of using trained super-resolution techniques to improve the quality of iris images prior to matchingThis work was supported by the EU COST Action under Grant IC1106. The work of F. Alonso-Fernandez and J. Bigun was supported in part by the Swedish Research Council, in part by the Swedish Innovation Agency, and in part by the Swedish Knowledge Foundation through the CAISR/SIDUS-AIR projects. The work of J. Fierrez was supported by the Spanish MINECO/FEDER through the CogniMetrics Project under Grant TEC2015-70627-R. The authors acknowledge the Halmstad University Library for its support with the open access fee