Race, biometrics, and security in modern Japan : a history of racial government

This thesis is an historical study of biopolitical relations between racism and biometric identification in Japan since the late nineteenth century to the present day. Adopting Foucault’s historical method, it challenges progressive accounts of the history of racism and that of biometrics. During the nineteenth century, practices of biometric identification emerged as constitutive of the knowledge of race wherein imperial power relations between superior and inferior races were enabled. Progressive accounts proclaim that colonial practices of biometrics were not scientific but politically intervened, which has since been discredited and replaced by a ‘true’ science of biometrics as individualisation. Contra progressivist claims on postraciality, the thesis concretely historicises the ways in which subjectification and control of race is conducted through the interplay between the epistemic construction of race and the technology of identification in each historical and geographical context. It analyses three modalities of racial government through biometrics in Japan: biometrics as a biological technology of inscribing race during Japanese colonialism; biometrics as a forensic technology of policing former colonial subjects in post-WWII Japan; and contemporary biometrics as an informatic technology of controlling a newly racialised immigrant population. The thesis concludes that despite a series of de-racialising reforms in the twentieth century, biometrics persist as a biopolitical technology of race. Neither racism nor biometrics as a technology of race is receding but they are continuously transforming in a way that a new mechanism of racial government is made possible. Race evolves, it is argued, not in the sense of social Darwinism but because the concept of race itself changes across time and space wherein a new model of racism is empowered. The thesis contributes to existing literature on the biopolitics of security and biometrics by extending the scope of analysis to a non-Western context, explicating historical relations between racism and biometrics, and problematising biometric rationality at the level of racialised mechanism of knowing and controlling (in)security. It also makes contributions to Foucaultian studies by advancing the analysis of biopolitical racism beyond Foucault’s original formulation and by offering a critique of rationality in the field of biometrics

Towards Security Goals in Summative E-Assessment Security

The general security goals of a computer system are known to include confidentiality, integrity and availability (C-I-A) which prevent critical assets from potential threats. The C-I-A security goals are well researched areas; however they may be insufficient to address all the needs of the summative e-assessment. In this paper, we do not discard the fundamental C-I-A security goals; rather we define security goals which are specific to summative e-assessment security

A schema for cryptographic keys generation using hybrid biometrics

Biometric identifiers refer to unique physical properties or behavioural attributes of individuals. Some of the well known biometric identifiers are voice, finger prints, retina or iris, facial structure etc. In our daily interaction with others directly or indirectly, we implicitly use biometrics to know, distinguish and trust people. Biometric identifiers represent the concept of "who a person is" by gathering vital characteristics that don't correspond to any other person. The human brain to some extent is able to ascertain disparities or variation in certain physical attributes and yet verify the authenticity of a person. But this is difficult to be implemented in electronic systems due to the intense requirements of artificial decision making and hard-coded logic. This paper examines the possibility of using a combination of biometric attributes to overcome common problems in having a single biometric scheme for authentication. It also investigates possible schemes and features to deal with variations in Biometric attributes. The material presented is related to ongoing research by the Computer Communications Research Group at Leeds Metropolitan University. We use this paper as a starting step and as a plan for advanced research. It offers ideas and proposition for implementing hybrid biometrics in conjunction with cryptography. This is work in progress and is in a very preliminary stage

Learning Discriminative Features with Class Encoder

Deep neural networks usually benefit from unsupervised pre-training, e.g. auto-encoders. However, the classifier further needs supervised fine-tuning methods for good discrimination. Besides, due to the limits of full-connection, the application of auto-encoders is usually limited to small, well aligned images. In this paper, we incorporate the supervised information to propose a novel formulation, namely class-encoder, whose training objective is to reconstruct a sample from another one of which the labels are identical. Class-encoder aims to minimize the intra-class variations in the feature space, and to learn a good discriminative manifolds on a class scale. We impose the class-encoder as a constraint into the softmax for better supervised training, and extend the reconstruction on feature-level to tackle the parameter size issue and translation issue. The experiments show that the class-encoder helps to improve the performance on benchmarks of classification and face recognition. This could also be a promising direction for fast training of face recognition models.Comment: Accepted by CVPR2016 Workshop of Robust Features for Computer Visio

Entropy Projection Curved Gabor with Random Forest and SVM for Face Recognition

In this work, we propose a workflow for face recognition under occlusion using the entropy projection from the curved Gabor filter, and create a representative and compact features vector that describes a face. Despite the reduced vector obtained by the entropy projection, it still presents opportunity for further dimensionality reduction. Therefore, we use a Random Forest classifier as an attribute selector, providing a 97% reduction of the original vector while keeping suitable accuracy. A set of experiments using three public image databases: AR Face, Extended Yale B with occlusion and FERET illustrates the proposed methodology, evaluated using the SVM classifier. The results obtained in the experiments show promising results when compared to the available approaches in the literature, obtaining 98.05% accuracy for the complete AR Face, 97.26% for FERET and 81.66% with Yale with 50% occlusion