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

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

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”

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

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”

QUEST Hierarchy for Hyperspectral Face Recognition

Face recognition is an attractive biometric due to the ease in which photographs of the human face can be acquired and processed. The non-intrusive ability of many surveillance systems permits face recognition applications to be used in a myriad of environments. Despite decades of impressive research in this area, face recognition still struggles with variations in illumination, pose and expression not to mention the larger challenge of willful circumvention. The integration of supporting contextual information in a fusion hierarchy known as QUalia Exploitation of Sensor Technology (QUEST) is a novel approach for hyperspectral face recognition that results in performance advantages and a robustness not seen in leading face recognition methodologies. This research demonstrates a method for the exploitation of hyperspectral imagery and the intelligent processing of contextual layers of spatial, spectral, and temporal information. This approach illustrates the benefit of integrating spatial and spectral domains of imagery for the automatic extraction and integration of novel soft features (biometric). The establishment of the QUEST methodology for face recognition results in an engineering advantage in both performance and efficiency compared to leading and classical face recognition techniques. An interactive environment for the testing and expansion of this recognition framework is also provided