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[This corrects the article DOI: 10.1371/journal.pone.0194111.].

This section contains all the poster abstracts from the “Inventory & Biometrics” track of the 2015 Society of American Foresters National Convention, held November 3-7 in Baton Rouge, Louisiana.

This section contains all the presentation abstracts from the “Inventory & Biometrics” track of the 2015 Society of American Foresters National Convention, held November 3-7 in Baton Rouge, Louisiana.

With the increasing demand for information security and security regulations all over the world, biometric recognition technology has been widely used in our everyday life. In this regard, multimodal biometrics technology has gained interest and became popular due to its ability to overcome a number of significant limitations of unimodal biometric systems. In this paper, a new multimodal biometric human identification system is proposed, which is based on a deep learning algorithm for recognizing humans using biometric modalities of iris, face, and finger vein. The structure of the system is based on convolutional neural networks (CNNs) which extract features and classify images by softmax classifier. To develop the system, three CNN models were combined; one for iris, one for face, and one for finger vein. In order to build the CNN model, the famous pertained model VGG-16 was used, the Adam optimization method was applied and categorical cross-entropy was used as a loss function. Some techniques to avoid overfitting were applied, such as image augmentation and dropout techniques. For fusing the CNN models, different fusion approaches were employed to explore the influence of fusion approaches on recognition performance, therefore, feature and score level fusion approaches were applied. The performance of the proposed system was empirically evaluated by conducting several experiments on the SDUMLA-HMT dataset, which is a multimodal biometrics dataset. The obtained results demonstrated that using three biometric traits in biometric identification systems obtained better results than using two or one biometric traits. The results also showed that our approach comfortably outperformed other state-of-the-art methods by achieving an accuracy of 99.39%, with a feature level fusion approach and an accuracy of 100% with different methods of score level fusion.

This research work focuses on a Near-Infra-Red (NIR) finger-images-based multimodal biometric system based on Finger Texture and Finger Vein biometrics. The individual results of the biometric characteristics are fused using a fuzzy system, and the final identification result is achieved. Experiments are performed for three different databases, i.e., the Near-Infra-Red Hand Images (NIRHI), Hong Kong Polytechnic University (HKPU) and University of Twente Finger Vein Pattern (UTFVP) databases. First, the Finger Texture biometric employs an efficient texture feature extracting algorithm, i.e., Linear Binary Pattern. Then, the classification is performed using Support Vector Machine, a proven machine learning classification algorithm. Second, the transfer learning of pre-trained convolutional neural networks (CNNs) is performed for the Finger Vein biometric, employing two approaches. The three selected CNNs are AlexNet, VGG16 and VGG19. In Approach 1, before feeding the images for the training of the CNN, the necessary preprocessing of NIR images is performed. In Approach 2, before the pre-processing step, image intensity optimization is also employed to regularize the image intensity. NIRHI outperforms HKPU and UTFVP for both of the modalities of focus, in a unimodal setup as well as in a multimodal one. The proposed multimodal biometric system demonstrates a better overall identification accuracy of 99.62% in comparison with 99.51% and 99.50% reported in the recent state-of-the-art systems.

In 2020, 18 police forces across England and Wales acquired mobile devices equipped with the capacity to remotely carry out real-time checks of a person's fingerprints against immigration and law enforcement databases. Someone may be \"stopped and scanned\" in any public space, such as a street corner or park, and face immediate detention if a match is found. For four years advocacy groups such as the Racial Justice Network have protested mobile fingerprinting for widening the scope of hostile environment measures that increasingly cut off migrant individuals and communities from public resources and spaces. Faced with such accounts of contemporary migrant struggles within biometric landscapes, this thesis investigates the impact of such technologies in the policing and management of migration in Europe and seeks to address how they create new forms of harm. This thesis thus contributes to debates in critical migration and border studies that examine the relation between the body and its rendering in data, particularly insofar as this supports a growing literature on electronic borders. In this regard, an important body of work has considered how the rendering of life as data as security practice works, how the border is evolving, and the philosophical, gendered and racialised dimensions. However, there has been a lack of research investigating what this biometric data subjectivity and border work does in the world, why it matters and how it profoundly impacts lives. This thesis fills this gap by developing a distinct understanding of harm through an engagement with the concept of 'cramped space' (Thoburn 2016), which names the experience of the social and political world one inhabits as marked with blockages, impediments, and constraints to how one can move through that world. This thesis argues that harm is created in two ways. Firstly, through a form of biometric individuation that makes people visible in ways that would expose them to isolation, confinement, and violence. Secondly, through the creation of atmospheric conditions of fear in which people must attempt to escape this visibilisation by, for example, burning fingerprints or avoiding spaces where they may be fingerprinted. Under such conditions, the 'impossibility of activity' characteristic of cramped space is matched with 'the impossibility of doing nothing if life is to be lived' (2016: 370). This thesis therefore seeks to rethink harm in terms of strategies to survive impossible conditions marked by the mutual imbrication of material, affective and spatial impacts of biometric technologies.

The Biometric authentication has become progressively more desired in current years. With this expansion of cloud computing, database holders be influenced to expand this extensive volume of biometric information & detection operations to CLOUD for eradicate of this high-priced storage and result overheads, is still conveys possible dangers to users' seclusion. In this document, we recommend an well-organized, well planned and confidentiality-protecting biometric classification strategy. Particularly, biometric information was encrypted & farmed out for Cloud database. For complete a biometric confirmation, server holder encrypts the inquiry information and proposes that to cloud. The Cloud implements recognition tasks on the encrypted server and sends this conclusion to the server holder. The systematic protection assessment specifies the recommended system is protected still if attackers can fake detection appeals and conspire through the cloud. Evaluated with previous protocols, investigational and new outcomes prove the recommended strategy accomplishes enhanced performance in both preparation and discovery measures.

In today’s information age, how to accurately identify a person’s identity and protect information security has become a hot topic of people from all walks of life. At present, a more convenient and secure solution to identity identification is undoubtedly biometric identification, but a single biometric identification cannot support increasingly complex and diversified authentication scenarios. Using multimodal biometric technology can improve the accuracy and safety of identification. This paper proposes a biometric method based on finger vein and face bimodal feature layer fusion, which uses a convolutional neural network (CNN), and the fusion occurs in the feature layer. The self-attention mechanism is used to obtain the weights of the two biometrics, and combined with the RESNET residual structure, the self-attention weight feature is cascaded with the bimodal fusion feature channel Concat. To prove the high efficiency of bimodal feature layer fusion, AlexNet and VGG-19 network models were selected in the experimental part for extracting finger vein and face image features as inputs to the feature fusion module. The extensive experiments show that the recognition accuracy of both models exceeds 98.4%, demonstrating the high efficiency of the bimodal feature fusion.

To investigate the associations between corneal curvature (CC) and other anterior segment biometrics in young myopic adults. In this retrospective multi-center study, 7893 young myopic adults were included. CC and other anterior segment biometrics were measured by Scheimpflug imaging (Pentacam). CC was defined as SimK at central 3 mm area, and other anterior segment biometrics included white-to-white corneal diameter (WTW), central corneal thickness (CCT), corneal volume (CV) at 3 mm, 5 mm, and 7 mm area, anterior corneal astigmatism (ACA), posterior corneal astigmatism (PCA), anterior corneal eccentricity (ACE) and asphericity (ACAP), posterior corneal eccentricity (PCE) and asphericity (PCAP), anterior chamber depth (ACD), and anterior chamber volume (ACV). Univariate regression analyses were used to assess the associations between CC and other anterior segment biometrics, and multivariate regression analyses were further performed to adjusted for age, gender and spherical equivalent. CC was higher in patients of female gender and higher myopia (all P  < 0.05). Eyes in higher CC quartiles had lower WTW, thinner CCT, lower CV at 3 mm and 5 mm, lower ACD, and lower ACV (all P  < 0.001), but had larger ACA, larger PCA, less PCE and less PCAP (all P  < 0.001), compared to eyes in lower CC quartiles. The trends of CV at 7 mm, ACE and ACAP were inconsistent in different CC quartiles. After adjusting for age, gender and spherical equivalent with multivariate linear regression, CC was positively correlated to CV at 7 mm (β s  = 0.069), ACA (β s  = 0.194), PCA (β s  = 0.187), ACE (β s  = 0.072), PCAP (β s  = 0.087), and ACD (β s  = 0.027) (all P  < 0.05), but was negatively correlated to WTW (β s  = − 0.432), CCT (β s  = − 0.087), CV-3 mm (β s  = − 0.066), ACAP (β s  = − 0.043), PCE (β s  = − 0.062), and ACV (β s  = − 0.188) (all P  < 0.05). CC was associated with most of the other anterior segment biometrics in young myopic adults. These associations are important for better understanding of the interactions between different anterior segment structures in young myopic patients, and are also useful for the exploration of the pathogenesis of myopia.