Inmaculada Tomeo-Reyes

A biomechanical approach to iris normalization

The richness of the iris texture and its variability across individuals make it a useful biometric trait for personal authentication. One of the key stages in classical iris recognition is the normalization process, where the annular iris region is mapped to a dimensionless pseudo-polar coordinate system. This process results in a rectangular structure that can be used to compensate for differences in scale and variations in pupil size. Most iris recognition methods in the literature adopt linear sampling in the radial and angular directions when performing iris normalization. In this paper, a biomechanical model of the iris is used to define a novel nonlinear normalization scheme that improves iris recognition accuracy under different degrees of pupil dilation. The proposed biomechanical model is used to predict the radial displacement of any point in the iris at a given dilation level, and this information is incorporated in the normalization process. Experimental results on the WVU pupil light reflex database (WVU-PLR) indicate the efficacy of the proposed technique, especially when matching iris images with large differences in pupil size.

Efficient high-rate key management technique for wireless body area networks

Wireless body area network (WBAN) is an emerging technology that focuses on healthcare monitoring in indoor and outdoor areas. WBAN technology allows medical sensors to collect vital physiological data and transfer it from a source to a destination via low-energy communication. To be able to encrypt and decrypt healthcare data, it is important for medical sensing and health-related devices to generate and extract the same secret keys at both end points. Recent studies show that two medical sensing devices can generate and share secret keys using their wireless channel properties, such as the received signal strength indicator (RSSI). However, existing approaches have low bit rate values and the key entropy is insufficient. These limitations pose a major threat to WBANs and must be addressed. In this paper, we first provide an overview of existing studies related to key extraction between two devices. We then describe the basic principles of wireless channel properties and the key parameters needed to generate secret keys. Finally, we propose a practical scheme to generate secret keys while avoiding information reconciliation and privacy amplification. The proposed scheme can generate 128 symmetric secret keys in a short time frame, and allows to secure the communication between sensor devices and improve the quality of services in WBANs.

Effect of Pupil Dilation and Constriction on the Distribution of Bit Errors within the Iris

Texture information in the iris image is not uniform in discriminatory information content for biometric identity verification. The bits in an iris code obtained from the image differ in their consistency from one sample to another for the same identity. In this work, errors in bit strings are systematically analysed in order to investigate the effect of light-induced and drug-induced pupil dilation and constriction on the consistency of iris texture information. The statistics of bit errors are computed for client and impostor distributions as functions of radius and angle. Under normal conditions, a V-shaped radial trend of decreasing bit errors towards the central region of the iris is obtained for client matching, and it is observed that the distribution of errors as a function of angle is uniform. When iris images are affected by pupil dilation or constriction the radial distribution of bit errors is altered. A decreasing trend from the pupil outwards is observed for constriction, whereas a more uniform trend is observed for dilation. The main increase in bit errors occurs closer to the pupil in both cases.

IEEE 802.15.6 standard in wireless body area networks from a healthcare point of view

The first standard supporting communication in wireless body area networks (WBANs) is IEEE 802.15 Task Groups 6 (TG6). IEEE 802.15.6 is a standard for short-range, low power, and highly reliable wireless communication in, on and around the human body. It supports a wide range of applications in body area networks (BANs) such as healthcare services. In WBANs, nodes are partitioned into a physical (PHY) layer and a medium access control (MAC) layer. In this paper, the MAC and PHY layers are investigated. The different types of communication supported by this standard, such as narrowband (NB), ultra-wideband (UWB), and human body communication (HBC), are further defined here. The security aspect of the standard is also discussed and investigated. Finally, using the standard and existing literature in WBAN, open issues and challenges are identified as a source of future study.

Isolating Iris Template Ageing in a Semi-Controlled Environment

The ageing of the iris and its influence on recognition performance is a hotly debated topic. Template ageing in iris biometrics is often attributed to changes during acquisition rather than the natural ageing of the iris. In this paper, we attempt to isolate template ageing effects in iris recognition by using a semi-controlled environment (selecting a subset from a larger database) and controlling as many factors as possible in the biometric tool-chain. This includes manual segmentation and the use of a non-linear biomechanical model to alleviate the influence of pupillary dilation. Despite our efforts, we still find iris template ageing to be apparent in the results.

White Blood Cell Nuclei Segmentation Using Level Set Methods and Geometric Active Contours

A new method for segmenting white blood cells nuclei in microscopic images is presented. Challenges to accurate segmentation include intra-class variation of the nuclei cell boundaries, non-uniform illumination, and changes in the cell topology due to its orientation and stage of maturity. In this research, level set methods and geometric active contours are used to segment the nucleus of white blood cells from the cytoplasm and the cell wall. Level set methods use morphological operations to estimate an initial cell boundary and are fully automated. Geometric active contours are less computationally complex and adapt better to the curve topology of the cell boundary than parametric active contours, which have been previously used for white blood cell segmentation. Segmentation performance is compared with other segmentation methods using the Berkeley benchmark database and the proposed method is shown to be superior using various indices.

Decision fusion from parts and samples for robust iris recognition

Fusion techniques can be used in biometrics to achieve higher accuracy. When biometric systems are in operation and the threat level changes, controlling the trade-off between detection error rates can reduce the impact of an attack. In a fused system, varying a single threshold does not allow this to be achieved, but systematic adjustment of a set of parameters does. In this paper, fused decisions from a multi-part, multi-sample sequential architecture are investigated for that purpose in an iris recognition system. A specific implementation of the multi-part architecture is proposed and the effect of the number of parts and samples in the resultant detection error rate is analysed. The effectiveness of the proposed architecture is then evaluated under two specific cases of obfuscation attack: miosis and mydriasis. Results show that robustness to such obfuscation attacks is achieved, since lower error rates than in the case of the non-fused base system are obtained.

Automatic segmentation of HEp-2 cell Fluorescence microscope images using level set method via geometric active contours

A method for segmenting HEp-2 cells in Indirect Immuno Fluorescence microscope (IIF) images is implemented and evaluated. Challenges to accurate segmentation include the complexity of the data acquired at multiple wavelengths, overlapping cells, and variations in staining. Level set methods via geometric active contours are used to solve this problem. Level set methods use morphological operations to estimate an initial cell boundary and are fully automated. Geometric active contours are able to adapt to the curve topology of the cell boundary. Segmentation performance is evaluated using six indices: boundary displacement error, global consistency error, variation of information, Jaccard distance error, rand index and F-index.