Feature Selection for Pose Invariant Lip Biometrics

For the first time in this paper the authors present results showing the effect of out of plane speaker head pose variation on a lip biometric based speaker verification system. Using appearance DCT based features, they adopt a Mutual Information analysis technique to highlight the class discriminant DCT components most robust to changes in out of plane pose. Experiments are conducted using the initial phase of a new multi view Audio-Visual database designed for research and development of pose-invariant speech and speaker recognition. They show that verification performance can be improved by substituting higher order horizontal DCT components for vertical, particularly in the case of a train/test pose angle mismatch.

Biometrics in ABC: counter-spoofing research

Automated border control (ABC) is concerned with fast and secure processing for intelligence-led identification. The FastPass project aims to build a harmonised, modular reference system for future European ABC. When biometrics is taken on board as identity, spoofing attacks become a concern. This paper presents current research in algorithm development for counter-spoofing attacks in biometrics. Focussing on three biometric traits, face, fingerprint, and iris, it examines possible types of spoofing attacks, and reviews existing algorithms reported in relevant academic papers in the area of countering measures to biometric spoofing attacks. It indicates that the new developing trend is fusion of multiple biometrics against spoofing attacks.

Soft computing in biometrics : a pragmatic appraisal

The ever increasing spurt in digital crimes such as image manipulation, image tampering, signature forgery, image forgery, illegal transaction, etc. have hard pressed the demand to combat these forms of criminal activities. In this direction, biometrics - the computer-based validation of a persons' identity is becoming more and more essential particularly for high security systems. The essence of biometrics is the measurement of person’s physiological or behavioral characteristics, it enables authentication of a person’s identity. Biometric-based authentication is also becoming increasingly important in computer-based applications because the amount of sensitive data stored in such systems is growing. The new demands of biometric systems are robustness, high recognition rates, capability to handle imprecision, uncertainties of non-statistical kind and magnanimous flexibility. It is exactly here that, the role of soft computing techniques comes to play. The main aim of this write-up is to present a pragmatic view on applications of soft computing techniques in biometrics and to analyze its impact. It is found that soft computing has already made inroads in terms of individual methods or in combination. Applications of varieties of neural networks top the list followed by fuzzy logic and evolutionary algorithms. In a nutshell, the soft computing paradigms are used for biometric tasks such as feature extraction, dimensionality reduction, pattern identification, pattern mapping and the like.

Biometrics and sexing criteria of the yellow-faced honeyeater lichenostomus chrysops

Morphometric data on 99 adult and 13 juvenile Yellow-faced Honeyeaters Lichenostomus chrysops that were independently sexed using molecular techniques were analysed to investigate size dimorphism between the sexes. Our results support previous studies that have demonstrated Yellow-faced Honeyeaters are sexually dimorphic in size, with males being the larger sex. Discriminant analyses of morphometric data were used to develop a simple method for sexing adult Yellow-faced Honeyeaters in the hand. As five observers collected the measurements our sexing criteria are conservative and should have wide application for field ornithologists working on the species.

Dynamic biometrics fusion at feature level for video-based human recognition

This paper proposes a novel human recognition method in video, which combines human face and gait traits
using a dynamic multi-modal biometrics fusion scheme. The Fisherface approach is adopted to extract face
features, while for gait features, Locality Preserving Projection (LPP) is used to achieve low-dimensional
manifold embedding of the temporal silhouette data derived from image sequences. Face and gait features are
fused dynamically at feature level based on a distance-driven fusion method. Encouraging experimental results
are achieved on the video sequences containing 20 people, which show that dynamically fused features produce
a more discriminating power than any individual biometric as well as integrated features built on common static
fusion schemes.

Bird biometrics condition and disease database

Contains biometric measurements (tarsus length, bill length, total head length, body mass) of birds caught at various locations in Australia (though mainly South Australia and Victoria), including data on the viral (mainly Avian Influenza Virus) analysis of cloacal and oropharyngeal swabs, serology of sera samples, and additional data on metabolites and parasite prevalence in swabs, blood cells and sera. Approximately 1500 birds (mainly waterbirds) are sampled annually.

Authenticating the identity of computer users with typing biometrics and the fuzzy min-max neural network

In this paper, typing biometrics is applied as an additional security measure to the password-based or Personal Identification Number (PIN)-based systems to authenticate the identity of computer users. In particular, keystroke pressure and latency signals are analyzed using the Fuzzy Min-Max (FMM) neural network for authentication purposes. A special pressure-sensitive keyboard is designed to collect keystroke pressure signals, in addition to the latency signals, from computer users when they type their passwords. Based on the keystroke pressure and latency signals, the FMM network is employed to classify the computer users into two categories, i.e., genuine users or impostors. To assess the effectiveness of the proposed approach, two sets of experiments are conducted, and the results are compared with those from statistical methods and neural network models. The experimental outcomes positively demonstrate the potentials of using typing biometrics and the FMM network to provide an additional security layer for the current password-based or PIN-based methods in authenticating the identity of computer users.

Towards person authentication by fusing visual and thermal face biometrics

In this chapter we focus on face appearance-based biometrics. The cheap and readily available hardware used to acquire data, their non-invasiveness and the ease of employing them from a distance and without the awareness of the user, are just some of the reasons why these continue to be of great practical interest. However, a number of research challenges remain. Specifically, face biometrics have traditionally focused on images acquired in the visible light spectrum and these are greatly affected by such extrinsic factors such as the illumination, camera angle (or, equivalently, head pose) and occlusion. In practice, the effects of changing pose are usually least problematic and can oftentimes be overcome by acquiring data over a time period, e.g., by tracking a face in a surveillance video. Consequently, image sequence or image set matching has recently gained a lot of attention in the literature [137–139] and is the paradigm adopted in this chapter as well. In other words, we assume that the training image set for each individual contains some variability in pose, but is not obtained in scripted conditions or in controlled illumination. In contrast, illumination is much more difficult to deal with: the illumination setup is in most cases not practical to control and its physics is difficult to accurately model. Thermal spectrum imagery is useful in this regard as it is virtually insensitive to illumination changes, as illustrated in Fig. 6.1. On the other hand, it lacks much of the individual, discriminating facial detail contained in visual images. In this sense, the two modalities can be seen as complementing each other. The key idea behind the system presented in this chapter is that robustness to extreme illumination changes can be achieved by fusing the two. This paradigm will further prove useful when we consider the difficulty of recognition in the presence of occlusion caused by prescription glasses.

On Person Authentication by Fusing Visual and Thermal Face Biometrics

Recognition algorithms that use data obtained by imaging faces in the thermal spectrum are promising in achieving invariance to extreme illumination changes that are often present in practice. In this paper we analyze the performance of a recently proposed face recognition algorithm that combines visual and thermal modalities by decision level fusion. We examine (i) the effects of the proposed data preprocessing in each domain, (ii) the contribution to improved recognition of different types of features, (iii) the importance of prescription glasses detection, in the context of both 1-to-N and 1-to-1 matching (recognition vs. verification performance). Finally, we discuss the significance of our results and, in particular, identify a number of limitations of the current state-of-the-art and propose promising directions for future research.

Biometrics studies of caste development in Nasutitermes coxipoensis (Isoptera; Termitidae)

A biometric study of caste development was carried out using Nasutitermes sp., by measuring the height, width, and length of the head, the length and width of the pronotum and mesonotum, and the length of the posterior tibia. These measurements were taken on 200 individuals, including larvae, nymphs, workers, pre-soldiers and soldiers. For instar separation, it was verified that the Principal Component Analysis (P.C.A.) was the most efficient methodology. Results of this analysis showed that Nasutitermes coxipoensis follows the general pattern of development presented by other Nasutitermes.

Effect of forced-molting methods and rearing temperatures on the performance and organ biometrics of laying hens

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Automatic clothes segmentation for soft biometrics

[EN]During the last decade, researchers have verified that clothing can provide information for gender recognition. However, before extracting features, it is necessary to segment the clothing region. We introduce a new clothes segmentation method based on the application of the GrabCut technique over a trixel mesh, obtaining very promising results for a close to real time system. Finally, the clothing features are combined with facial and head context information to outperform previous results in gender recognition with a public database.

Biometrics, fatty acid composition and stable isotopic ratios of five seabird species from Kongsfjorden, Svalbard

Marine birds are important predators in the marine ecosystem, and dietary studies can give useful information about their feeding ecology, food webs and oceanographic variability. The aim of this study was to increase our understanding of the diet and trophic level of the seabirds breeding in Kongsfjorden, Svalbard. We have used fatty acids and stable isotopes, both of which integrate diet information over space and time, to determine trophic relationships in marine food webs. Fatty acid compositions of muscle from Little auk (Alle alle), Brünnich's guillemot (Uria lomvia), Black-legged kittiwake (Rissa tridactyla), Northern fulmar (Fulmarus glacialis) and Glaucous gull (Larus hyperboreus) were determined and compared with their prey species. Canonical analysis (CA) showed that fatty acid composition differed among the five seabird species. Little auk, Black-legged kittiwake and Northern fulmar had high levels of the Calanus markers 20:1n9 and 22:1, indicating that these seabirds are a part of the Calanus food chain. Brünnich's guillemot differed from the other species with much lower levels of 20:1n9 and 22:1. Brünnich's guillemot is a pursuit diver feeding on fish and amphipods deeper in the water column, below 30 m. Glaucous gull also differed from the other seabird species, with a larger variation in the fatty acid composition indicating a more diverse diet. Trophic level analysis placed Little auk at the lowest trophic level, Brünnich's guillemot and Black-legged kittiwake at intermediate levels and Glaucous gull and Northern fulmar at the highest trophic level.