The massive availability of cameras and personal devices results in a wide variability between imaging conditions, producing large intra-class variations and performance drop if such images are compared for person recognition. However, as biometric solutions are extensively deployed, it will be common to replace acquisition hardware as it is damaged or newer designs appear, or to exchange information between agencies or applications in heterogeneous environments. Furthermore, variations in imaging bands can also occur. For example, faces are typically acquired in the visible (VW) spectrum, while iris images are captured in the near-infrared (NIR) spectrum. However, cross-spectrum comparison may be needed if for example a face from a surveillance camera needs to be compared against a legacy iris database. Here, we propose a multialgorithmic approach to cope with cross-sensor periocular recognition. We integrate different systems using a fusion scheme based on linear logistic regression, in which fused scores tend to be log-likelihood ratios. This allows easy combination by just summing scores of available systems. We evaluate our approach in the context of the 1st Cross-Spectral Iris Periocular Competition, whose aim was to compare person recognition approaches when periocular data from VW and NIR images is matched. The proposed fusion approach achieves reductions in error rates of up to 20-30 in cross-spectral NIR-VW comparison, leading to an EER of 0.22 and a FRR of just 0.62 for FAR=0.01 , representing the best overall approach of the mentioned competition.. Experiments are also reported with a database of VW images from two different smartphones, achieving even higher relative improvements in performance. We also discuss our approach from the point of view of template size and computation times, with the most computationally heavy system playing an important role in the results.
Latest advancements have resulted in devices with ability to see through fog, rain, at night, and to operate at long ranges. In the work @cite_19 , the authors carry out experiments with different wavelengths, namely VW, NIR, SWIR (ShortWave Infrared), and MWIR (MiddleWave Infrared. In the mentioned paper, they use images captured at distances of 1.5 m, 50 m, and 105 m. Feature extraction is done with a bank of Gabor filters, with the magnitude and phase responses further encoded with three descriptors: Weber Local Descriptor (WLD) @cite_73 , Local Binary Patterns (LBP) @cite_38 , and Histogram of Oriented Gradients (HOG) @cite_55 . Extensive experiments are done in this work between the different spectra and standoff distances. Recently, the work @cite_6 presented a new multispectral database captured in eight bands across VW and NIR spectrum (530 to 1000 nm). 52 subjects were acquired using a custom-built sensor which captures ocular images simultaneously in the eight bands. The descriptors evaluated were Histogram of Oriented Gradients (HOG), perceptual descriptors (GIST), Log-Gabor filters (LG), and Binarized Statistical Image Features (BSIF). The cross-band accuracy varied greatly depending on the reference and probe bands, ranging from 8.46 .
@cite_38 @cite_55 @cite_6 @cite_19 @cite_73
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A new compound operator is proposed for heterogeneous periocular recognition.The new operator outperforms three basic operators and three state-of-the-art compound operators.NIR, SWIR, MWIR and LWIR spectra at different standoff distances are considered.A metric is introduced to measure image quality and explain its impact on performance.The new operator does not require training and can be applied to various datasets. Cross-spectral matching of active and passive infrared (IR) periocular images to a visible light periocular image gallery is a challenging research problem. This scenario is motivated by a number of surveillance applications such as recognition of subjects at night or in harsh environmental conditions. This problem becomes even more challenging with a varying standoff distance. To address this problem a new compound operator named GWLH that fuses three local descriptors - Histogram of Gradients (HOG), Local Binary Patterns (LBP) and Weber Local Descriptors (WLD) - applied to the outputs of Gabor filters is proposed. The local operators encode both magnitude and phase information. When applied to periocular regions, GWLH outperforms other compound operators that recently appeared in the literature. During performance evaluation LBP, Gabor filters, HOG, and a fusion of HOG and LBP establish a baseline for the performance comparison, while other compound operators such as Gabor followed by HOG and LBP as well as Gabor followed by WLD, LBP and GLBP present the state-of-the-art. The active IR band is presented by short-wave infrared (SWIR) and near-infrared (NIR) and passive IR is presented by mid-wave infrared (MWIR) and long-wave infrared (LWIR). In addition to varying spectrum, we also vary the standoff distance of SWIR and NIR probes. In all but one case of the combination of spectrum and range, GWLH outperforms all the other operators. A sharpness metric is introduced to measure the quality of heterogeneous periocular images and to emphasize the need in development of image enhancement approaches for heterogeneous periocular biometrics. Based on the statistics of the sharpness metric, the performance difference between compound and single operators is increasing proportionally with increasing sharpness metric values. Short wave infrared (SWIR) is an emerging imaging modality in surveillance applications. It is able to capture clear long range images of a subject in harsh atmospheric conditions and at night time. However, matching SWIR images against a gallery of color images is a very challenging task. The photometric properties of images in these two spectral bands are highly distinct. This work presents a novel cross-spectral face recognition scheme that encodes images filtered with a bank of Gabor filters followed by three local operators: Simplified Weber Local Descriptor, Local Binary Pattern, and Generalized Local Binary Pattern. Both magnitude and phase of filtered images are encoded. Matching encoded face images is performed by using a symmetric I-divergence. We quantify the verification and identification performance of the cross-spectral matcher on two multispectral face datasets. In the first dataset (PRE-TINDERS), both SWIR and visible gallery images are captured at a close distance (about 2 meters). In the second dataset (TINDERS), the probe SWIR images are collected at longer ranges (50 and 106 meters). The results on PRE-TINDERS dataset form a baseline for matching long range data. We also demonstrate the capability of the proposed approach by comparing its performance with the performance of Faceit G8, a commercial face recognition engine distributed by L1. The results show that the designed method outperforms Faceit G8 in terms of verification and identification rates on both datasets. article i nfo The remote detection of water stress in a citrus orchard was investigated using leaf-level measurements of chlorophyll fluorescence and Photochemical Reflectance Index (PRI) data, seasonal time-series of crown tem- perature and PRI, and high-resolution airborne imagery. The work was conducted in an orchard where a reg- ulated deficit irrigation (RDI) experiment generated a gradient in water stress levels. Stomatal conductance (Gs) and water potential (Ψ) were measured over the season on each treatment block. The airborne data consisted on thermal and hyperspectral imagery acquired at the time of maximum stress differences among treatments, prior to the re-watering phase, using a miniaturized thermal camera and a micro-hyperspectral imager on board an unmanned aerial vehicle (UAV). The hyperspectral imagery was acquired at 40 cm resolution and 260 spectral bands in the 400-885 nm spectral range at 6.4 nm full width at half maximum (FWHM) spectral resolution and 1.85 nmsampling interval,enablingthe identificationof pure crownsfor extractingradiance andreflectance hyperspectral spectra from each tree. The FluorMOD model was used to investigate the retrieval of chlorophyll fluorescence by applying the Fraunhofer Line Depth (FLD) principle using three spectral bands (FLD3), which demonstrated that fluorescence retrievalwas feasible with the configuration of the UAV micro-hyperspectral in- strument flown over the orchard. Results demonstrated the link between seasonal PRI and crown temperature acquired from instrumented trees and field measurements of stomatal conductance and water potential. The sensitivity of PRI and Tc-Ta time-series to water stress levels demonstrated a time delay of PRI vs Tc-Ta during the recovery phase after re-watering started. At the time of the maximum stress difference among treatment blocks, the airborneimagery acquired fromthe UAV platform demonstrated that the crown temperature yielded the best coefficient of determination for Gs (r 2 Multispectral images that combine visual-optical (VIS) and infrared (IR) image information are a promising source of data for automatic person detection. Especially in automotive or surveillance applications, challenging conditions such as insufficient illumination or large distances between camera and object occur regularly and can affect image quality. This leads to weak image contrast or low object resolution. In order to detect persons under such conditions, we apply deep learning for effectively fusing the VIS and IR information in multispectral images. We present a novel multispectral Region Proposal Network (RPN) that is built up on the pre-trained very deep convolutional network VGG-16. The proposals of this network are further evaluated using a Boosted Decision Trees classifier in order to reduce potential false positive detections. With a log-average miss rate of 29:83 on the reasonable test set of the KAIST Multispectral Pedestrian Detection Benchmark, we improve the current state-of-the-art by about 18 .