scholarly journals Design of an Active Multispectral SWIR Camera System for Skin Detection and Face Verification

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Holger Steiner ◽  
Sebastian Sporrer ◽  
Andreas Kolb ◽  
Norbert Jung
Keyword(s):  
Face Recognition ◽  
Imaging System ◽  
Short Wave ◽  
Classification Performance ◽  
High Spectral Resolution ◽  
Face Verification ◽  
Camera System ◽  
Spectral Signatures ◽  
Face Images ◽  
Small Band

Biometric face recognition is becoming more frequently used in different application scenarios. However, spoofing attacks with facial disguises are still a serious problem for state of the art face recognition algorithms. This work proposes an approach to face verification based on spectral signatures of material surfaces in the short wave infrared (SWIR) range. They allow distinguishing authentic human skin reliably from other materials, independent of the skin type. We present the design of an active SWIR imaging system that acquires four-band multispectral image stacks in real-time. The system uses pulsed small band illumination, which allows for fast image acquisition and high spectral resolution and renders it widely independent of ambient light. After extracting the spectral signatures from the acquired images, detected faces can be verified or rejected by classifying the material as “skin” or “no-skin.” The approach is extensively evaluated with respect to both acquisition and classification performance. In addition, we present a database containing RGB and multispectral SWIR face images, as well as spectrometer measurements of a variety of subjects, which is used to evaluate our approach and will be made available to the research community by the time this work is published.

scholarly journals Homomorphic Filtering and Phase-Based Matching for Cross-Spectral Cross-Distance Face Recognition

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4575
Author(s):  
Fitri Arnia ◽  
Maulisa Oktiana ◽  
Khairun Saddami ◽  
Khairul Munadi ◽  
Roslidar Roslidar ◽  
...  
Keyword(s):  
Face Recognition ◽  
Near Infrared ◽  
Recognition Performance ◽  
Extraction Process ◽  
Face Verification ◽  
Surveillance Systems ◽  
Long Distance ◽  
Face Images ◽  
Homomorphic Filtering ◽  
Band Limited

Facial recognition has a significant application for security, especially in surveillance technologies. In surveillance systems, recognizing faces captured far away from the camera under various lighting conditions, such as in the daytime and nighttime, is a challenging task. A system capable of recognizing face images in both daytime and nighttime and at various distances is called Cross-Spectral Cross Distance (CSCD) face recognition. In this paper, we proposed a phase-based CSCD face recognition approach. We employed Homomorphic filtering as photometric normalization and Band Limited Phase Only Correlation (BLPOC) for image matching. Different from the state-of-the-art methods, we directly utilized the phase component from an image, without the need for a feature extraction process. The experiment was conducted using the Long-Distance Heterogeneous Face Database (LDHF-DB). The proposed method was evaluated in three scenarios: (i) cross-spectral face verification at 1m, (ii) cross-spectral face verification at 60m, and (iii) cross-spectral face verification where the probe images (near-infrared (NIR) face images) were captured at 1m and the gallery data (face images) was captured at 60 m. The proposed CSCD method resulted in the best recognition performance among the CSCD baseline approaches, with an Equal Error Rate (EER) of 5.34% and a Genuine Acceptance Rate (GAR) of 93%.

scholarly journals Investigating the Potential of a Newly Developed UAV-Mounted VNIR/SWIR Imaging System for Monitoring Crop Traits—A Case Study for Winter Wheat

2021 ◽  
Vol 13 (9) ◽  
pp. 1697
Author(s):  
Alexander Jenal ◽  
Hubert Hüging ◽  
Hella Ellen Ahrends ◽  
Andreas Bolten ◽  
Jens Bongartz ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Near Infrared ◽  
Imaging System ◽  
N Uptake ◽  
Ground Truth ◽  
Short Wave ◽  
Camera System ◽  
Ground Truth Data ◽  
Single Observation ◽  
Wheat Field

UAV-based multispectral multi-camera systems are widely used in scientific research for non-destructive crop traits estimation to optimize agricultural management decisions. These systems typically provide data from the visible and near-infrared (VNIR) domain. However, several key absorption features related to biomass and nitrogen (N) are located in the short-wave infrared (SWIR) domain. Therefore, this study investigates a novel multi-camera system prototype that addresses this spectral gap with a sensitivity from 600 to 1700 nm by implementing dedicated bandpass filter combinations to derive application-specific vegetation indices (VIs). In this study, two VIs, GnyLi and NRI, were applied using data obtained on a single observation date at a winter wheat field experiment located in Germany. Ground truth data were destructively sampled for the entire growing season. Likewise, crop heights were derived from UAV-based RGB image data using an improved approach developed within this study. Based on these variables, regression models were derived to estimate fresh and dry biomass, crop moisture, N concentration, and N uptake. The relationships between the NIR/SWIR-based VIs and the estimated crop traits were successfully evaluated (R2: 0.57 to 0.66). Both VIs were further validated against the sampled ground truth data (R2: 0.75 to 0.84). These results indicate the imaging system’s potential for monitoring crop traits in agricultural applications, but further multitemporal validations are needed.

scholarly journals Robust Face Recognition Based on a New Supervised Kernel Subspace Learning Method

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1643 ◽  
Author(s):  
Ali Khalili Mobarakeh ◽  
Juan Antonio Cabrera Carrillo ◽  
Juan Jesús Castillo Aguilar
Keyword(s):  
Face Recognition ◽  
Dimensional Space ◽  
Subspace Learning ◽  
Classification Performance ◽  
Learning Method ◽  
Structure Information ◽  
Pattern Recognition Methods ◽  
Face Images ◽  
Robust Face Recognition ◽  
Complex Variation

Face recognition is one of the most popular techniques to achieve the goal of figuring out the identity of a person. This study has been conducted to develop a new non-linear subspace learning method named “supervised kernel locality-based discriminant neighborhood embedding,” which performs data classification by learning an optimum embedded subspace from a principal high dimensional space. In this approach, not only nonlinear and complex variation of face images is effectively represented using nonlinear kernel mapping, but local structure information of data from the same class and discriminant information from distinct classes are also simultaneously preserved to further improve final classification performance. Moreover, in order to evaluate the robustness of the proposed method, it was compared with several well-known pattern recognition methods through comprehensive experiments with six publicly accessible datasets. Experiment results reveal that our method consistently outperforms its competitors, which demonstrates strong potential to be implemented in many real-world systems.

Fuzzy Aggregation Method Using Fisherface and Wavelet Decomposition for Face Recognition

2004 ◽  
Vol 8 (4) ◽  
pp. 379-384 ◽  
Author(s):  
Keun-Chang Kwak ◽  
◽  
Witold Pedrycz ◽  
Hyoun-Joo Go ◽  
Myung-Geun Chun ◽  
...  
Keyword(s):  
Face Recognition ◽  
Wavelet Decomposition ◽  
Classification Performance ◽  
Aggregation Method ◽  
Second Stage ◽  
Face Images ◽  
Fuzzy Aggregation ◽  
Two Phases ◽  
The Individual ◽  
High Consistency

In this paper, we propose the fuzzy aggregation method for face recognition based on subimage sets decomposed by wavelets. The proposed approach consists of four main stages. The first stage uses the wavelet decomposition that helps extract intrinsic features of face images. The second stage of the approach applies a fisherface method to these four subimages obtained by wavelet decomposition. The choice of the fisherface method in this setting is motivated by its insensitivity to large variation in light direction, face pose, and facial expression. The last two phases are concerned with the aggregation of the individual classifiers by means of the fuzzy integral. The experiments use an n-fold cross-validation to assure high consistency of the classification results. The experimental results obtained for the Yale face databases reveal that the approach presented in this paper yields better classification performance in comparison with the results obtained by other recognition methods.

Face Deduplication in Video Surveillance

2017 ◽  
Vol 32 (03) ◽  
pp. 1856001 ◽  
Author(s):  
Qi Chen ◽  
Li Yang ◽  
Dongping Zhang ◽  
Ye Shen ◽  
Shuying Huang
Keyword(s):  
Face Recognition ◽  
Video Surveillance ◽  
Surveillance System ◽  
Quality Evaluation ◽  
Face Image ◽  
Face Verification ◽  
Identification Methods ◽  
Verification Method ◽  
Security Industry ◽  
Face Images

The video surveillance system based on face analysis has played an increasingly important role in the security industry. Compared with identification methods of other physical characteristics, face verification method is easy to be accepted by people. In the video surveillance scene, it is common to capture multiple faces belonging to a same person. We cannot get a good result of face recognition if we use all the images without considering image quality. In order to solve this problem, we propose a face deduplication system which is combined with face detection and face quality evaluation to obtain the highest quality face image of a person. The experimental results in this paper also show that our method can effectively detect the faces and select the high-quality face images, so as to improve the accuracy of face recognition.

scholarly journals Investigating the Potential of a Newly Developed UAV-based VNIR/SWIR Imaging System for Forage Mass Monitoring

2020 ◽  
Vol 88 (6) ◽  
pp. 493-507
Author(s):  
Alexander Jenal ◽  
Ulrike Lussem ◽  
Andreas Bolten ◽  
Martin Leon Gnyp ◽  
Jürgen Schellberg ◽  
...  
Keyword(s):  
Near Infrared ◽  
Imaging System ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Ground Truth ◽  
Short Wave ◽  
Data Sets ◽  
Camera System ◽  
Ground Truth Data ◽  
Calibration Methods

AbstractRemote sensing systems based on unmanned aerial vehicles (UAVs) are well suited for airborne monitoring of small to medium-sized farmland in agricultural applications. An imaging system is often used in the form of a multispectral multi-camera system to derive well-established vegetation indices (VIs) efficiently. This study investigates the potential of such a multi-camera system with a novel approach to extend spectral sensitivity from visible-to-near-infrared (VNIR) to short-wave infrared (SWIR) (400–1700 nm) for estimating forage mass from an aerial carrier platform. The system test was performed in a grassland fertilizer trial in Germany near Cologne in late July 2019. Within 37 min, a spectral response in four different wavelength bands in the NIR and SWIR range was acquired during two consecutive flights. Spectral image data were calibrated to reflectance using two different methods. The resulting reflectance data sets were processed to orthomosaics for each wavelength band. From these orthomosaics for both calibration methods, the four-band NIR/SWIR GnyLi VI and the two-band NIR/SWIR Normalized Ratio Index (NRI), were calculated. During both UAV flights, spectral ground truth data were recorded with a spectroradiometer on 12 plots in total for validation of camera-based spectral data. The camera and spectroradiometer data sets were directly compared in resulting reflectance and further analyzed with simple linear regression (SLR) models to predict dry matter (DM) yield. In the camera-based SLRs, the NRI performed best with $$R^2$$ R 2 of 0.73 and 0.75 (RMSE: 0.18 and 0.17) before the GnyLi with $$R^{2}$$ R 2 of 0.71 and 0.73 (RMSE: 0.19 and 0.18). These results clearly indicate the potential of the camera system for applications in forage mass monitoring.

scholarly journals Optimized Method for Mapping Inorganic Pigments by Means of Multispectral Imaging Combined with Hyperspectral Spectroscopy for the Study of Vincenzo Pasqualoni’s Wall Painting at the Basilica of S. Nicola in Carcere in Rome

Minerals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 839
Author(s):  
Lucilla Pronti ◽  
Giuseppe Capobianco ◽  
Margherita Vendittelli ◽  
Anna Candida Felici ◽  
Silvia Serranti ◽  
...  
Keyword(s):  
Multispectral Imaging ◽  
Imaging System ◽  
Principal Component ◽  
Reflectance Spectroscopy ◽  
Wall Painting ◽  
High Spectral Resolution ◽  
Spectral Difference ◽  
Color Palette ◽  
A Minor ◽  
Nir Reflectance Spectroscopy

Multispectral imaging is a preliminary screening technique for the study of paintings. Although it permits the identification of several mineral pigments by their spectral behavior, it is considered less performing concerning hyperspectral imaging, since a limited number of wavelengths are selected. In this work, we propose an optimized method to map the distribution of the mineral pigments used by Vincenzo Pasqualoni for his wall painting placed at the Basilica of S. Nicola in Carcere in Rome, combining UV/VIS/NIR reflectance spectroscopy and multispectral imaging. The first method (UV/VIS/NIR reflectance spectroscopy) allowed us to characterize pigment layers with a high spectral resolution; the second method (UV/VIS/NIR multispectral imaging) permitted the evaluation of the pigment distribution by utilizing a restricted number of wavelengths. Combining the results obtained from both devices was possible to obtain a distribution map of a pictorial layer with a high accuracy level of pigment recognition. The method involved the joint use of point-by-point hyperspectral spectroscopy and Principal Component Analysis (PCA) to identify the pigments in the color palette and evaluate the possibility to discriminate all the pigments recognized, using a minor number of wavelengths acquired through the multispectral imaging system. Finally, the distribution and the spectral difference of the different pigments recognized in the multispectral images, (in this case: red ochre, yellow ochre, orpiment, cobalt blue-based pigments, ultramarine and chrome green) were shown through PCA false-color images.

scholarly journals Some Information Geometric Aspects of Cyber Security by Face Recognition

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 878
Author(s):  
C. T. J. Dodson ◽  
John Soldera ◽  
Jacob Scharcanski
Keyword(s):  
Face Recognition ◽  
Cyber Security ◽  
Probability Distributions ◽  
Geodesic Distance ◽  
Recognition Method ◽  
Frequency Distributions ◽  
Face Images ◽  
User Access ◽  
Multidimensional Methods ◽  
Joint Probabilities

Secure user access to devices and datasets is widely enabled by fingerprint or face recognition. Organization of the necessarily large secure digital object datasets, with objects having content that may consist of images, text, video or audio, involves efficient classification and feature retrieval processing. This usually will require multidimensional methods applicable to data that is represented through a family of probability distributions. Then information geometry is an appropriate context in which to provide for such analytic work, whether with maximum likelihood fitted distributions or empirical frequency distributions. The important provision is of a natural geometric measure structure on families of probability distributions by representing them as Riemannian manifolds. Then the distributions are points lying in this geometrical manifold, different features can be identified and dissimilarities computed, so that neighbourhoods of objects nearby a given example object can be constructed. This can reveal clustering and projections onto smaller eigen-subspaces which can make comparisons easier to interpret. Geodesic distances can be used as a natural dissimilarity metric applied over data described by probability distributions. Exploring this property, we propose a new face recognition method which scores dissimilarities between face images by multiplying geodesic distance approximations between 3-variate RGB Gaussians representative of colour face images, and also obtaining joint probabilities. The experimental results show that this new method is more successful in recognition rates than published comparative state-of-the-art methods.

Face recognition in an intelligent door lock with ResNet model based on deep learning

2021 ◽  
pp. 1-11
Author(s):  
Suphawimon Phawinee ◽  
Jing-Fang Cai ◽  
Zhe-Yu Guo ◽  
Hao-Ze Zheng ◽  
Guan-Chen Chen
Keyword(s):  
Face Recognition ◽  
Detection System ◽  
Low Cost ◽  
Ease Of Use ◽  
Raspberry Pi ◽  
Training Methods ◽  
Graphics Recognition ◽  
Face Images ◽  
The Face ◽  
Door Lock

Internet of Things is considerably increasing the levels of convenience at homes. The smart door lock is an entry product for smart homes. This work used Raspberry Pi, because of its low cost, as the main control board to apply face recognition technology to a door lock. The installation of the control sensing module with the GPIO expansion function of Raspberry Pi also improved the antitheft mechanism of the door lock. For ease of use, a mobile application (hereafter, app) was developed for users to upload their face images for processing. The app sends the images to Firebase and then the program downloads the images and captures the face as a training set. The face detection system was designed on the basis of machine learning and equipped with a Haar built-in OpenCV graphics recognition program. The system used four training methods: convolutional neural network, VGG-16, VGG-19, and ResNet50. After the training process, the program could recognize the user’s face to open the door lock. A prototype was constructed that could control the door lock and the antitheft system and stream real-time images from the camera to the app.

scholarly journals Multimodal Low Resolution Face and Frontal Gait Recognition from Surveillance Video

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1013
Author(s):  
Sayan Maity ◽  
Mohamed Abdel-Mottaleb ◽  
Shihab S. Asfour
Keyword(s):  
Face Recognition ◽  
High Resolution ◽  
Gait Cycle ◽  
Gait Recognition ◽  
Recognition System ◽  
Multimodal Fusion ◽  
Surveillance Video ◽  
Low Resolution ◽  
Model Free ◽  
Face Images

Biometric identification using surveillance video has attracted the attention of many researchers as it can be applicable not only for robust identification but also personalized activity monitoring. In this paper, we present a novel multimodal recognition system that extracts frontal gait and low-resolution face images from frontal walking surveillance video clips to perform efficient biometric recognition. The proposed study addresses two important issues in surveillance video that did not receive appropriate attention in the past. First, it consolidates the model-free and model-based gait feature extraction approaches to perform robust gait recognition only using the frontal view. Second, it uses a low-resolution face recognition approach which can be trained and tested using low-resolution face information. This eliminates the need for obtaining high-resolution face images to create the gallery, which is required in the majority of low-resolution face recognition techniques. Moreover, the classification accuracy on high-resolution face images is considerably higher. Previous studies on frontal gait recognition incorporate assumptions to approximate the average gait cycle. However, we quantify the gait cycle precisely for each subject using only the frontal gait information. The approaches available in the literature use the high resolution images obtained in a controlled environment to train the recognition system. However, in our proposed system we train the recognition algorithm using the low-resolution face images captured in the unconstrained environment. The proposed system has two components, one is responsible for performing frontal gait recognition and one is responsible for low-resolution face recognition. Later, score level fusion is performed to fuse the results of the frontal gait recognition and the low-resolution face recognition. Experiments conducted on the Face and Ocular Challenge Series (FOCS) dataset resulted in a 93.5% Rank-1 for frontal gait recognition and 82.92% Rank-1 for low-resolution face recognition, respectively. The score level multimodal fusion resulted in 95.9% Rank-1 recognition, which demonstrates the superiority and robustness of the proposed approach.

Sign in / Sign up

Export Citation Format

Share Document