On Acquisition and Analysis of a Dataset Comprising of Gait, Ear and Semantic data

In outdoor scenarios such as surveillance where there is very little control over the environments, complex computer vision algorithms are often required for analysis. However constrained environments, such as walkways in airports where the surroundings and the path taken by individuals can be controlled, provide an ideal application for such systems. Figure 1.1 depicts an idealised constrained environment. The path taken by the subject is restricted to a narrow path and once inside is in a volume where lighting and other conditions are controlled to facilitate biometric analysis. The ability to control the surroundings and the flow of people greatly simplifes the computer vision task, compared to typical unconstrained environments. Even though biometric datasets with greater than one hundred people are increasingly common, there is still very little known about the inter and intra-subject variation in many biometrics. This information is essential to estimate the recognition capability and limits of automatic recognition systems. In order to accurately estimate the inter- and the intra- class variance, substantially larger datasets are required [40]. Covariates such as facial expression, headwear, footwear type, surface type and carried items are attracting increasing attention; although considering the potentially large impact on an individuals biometrics, large trials need to be conducted to establish how much variance results. This chapter is the first description of the multibiometric data acquired using the University of Southampton's Multi-Biometric Tunnel [26, 37]; a biometric portal using automatic gait, face and ear recognition for identification purposes. The tunnel provides a constrained environment and is ideal for use in high throughput security scenarios and for the collection of large datasets. We describe the current state of data acquisition of face, gait, ear, and semantic data and present early results showing the quality and range of data that has been collected. The main novelties of this dataset in comparison with other multi-biometric datasets are: 1. gait data exists for multiple views and is synchronised, allowing 3D reconstruction and analysis; 2. the face data is a sequence of images allowing for face recognition in video; 3. the ear data is acquired in a relatively unconstrained environment, as a subject walks past; and 4. the semantic data is considerably more extensive than has been available previously. We shall aim to show the advantages of this new data in biometric analysis, though the scope for such analysis is considerably greater than time and space allows for here

Feature-domain super-resolution framework for Gabor-based face and iris recognition

The low resolution of images has been one of the major limitations in recognising humans from a distance using their biometric traits, such as face and iris. Superresolution has been employed to improve the resolution and the recognition performance simultaneously, however the majority of techniques employed operate in the pixel domain, such that the biometric feature vectors are extracted from a super-resolved input image. Feature-domain superresolution has been proposed for face and iris, and is shown to further improve recognition performance by capitalising on direct super-resolving the features which are used for recognition. However, current feature-domain superresolution approaches are limited to simple linear features such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), which are not the most discriminant features for biometrics. Gabor-based features have been shown to be one of the most discriminant features for biometrics including face and iris. This paper proposes a framework to conduct super-resolution in the non-linear Gabor feature domain to further improve the recognition performance of biometric systems. Experiments have confirmed the validity of the proposed approach, demonstrating superior performance to existing linear approaches for both face and iris biometrics

Image quality assessment for iris biometric

Iris recognition, the ability to recognize and distinguish individuals by their iris pattern, is the most reliable biometric in terms of recognition and identification performance. However, performance of these systems is affected by poor quality imaging. In this work, we extend previous research efforts on iris quality assessment by analyzing the effect of seven quality factors: defocus blur, motion blur, off-angle, occlusion, specular reflection, lighting, and pixel-counts on the performance of traditional iris recognition system. We have concluded that defocus blur, motion blur, and off-angle are the factors that affect recognition performance the most. We further designed a fully automated iris image quality evaluation block that operates in two steps. First each factor is estimated individually, then the second step involves fusing the estimated factors by using Dempster-Shafer theory approach to evidential reasoning. The designed block is tested on two datasets, CASIA 1.0 and a dataset collected at WVU. (Abstract shortened by UMI.)

Multimodal biometric authentication based on voice, fingerprint and face recognition

openNew decison module to combine the score of voice, fingerprint and face recognition in a multimodal biometric system.New decison module to combine the score of voice, fingerprint and face recognition in a multimodal biometric system

Periocular Biometrics: A Modality for Unconstrained Scenarios

Periocular refers to the region of the face that surrounds the eye socket. This is a feature-rich area that can be used by itself to determine the identity of an individual. It is especially useful when the iris or the face cannot be reliably acquired. This can be the case of unconstrained or uncooperative scenarios, where the face may appear partially occluded, or the subject-to-camera distance may be high. However, it has received revived attention during the pandemic due to masked faces, leaving the ocular region as the only visible facial area, even in controlled scenarios. This paper discusses the state-of-the-art of periocular biometrics, giving an overall framework of its most significant research aspects

Multimodal person recognition for human-vehicle interaction

Next-generation vehicles will undoubtedly feature biometric person recognition as part of an effort to improve the driving experience. Today's technology prevents such systems from operating satisfactorily under adverse conditions. A proposed framework for achieving person recognition successfully combines different biometric modalities, borne out in two case studies

Enhanced iris recognition: Algorithms for segmentation, matching and synthesis

This thesis addresses the issues of segmentation, matching, fusion and synthesis in the context of irises and makes a four-fold contribution. The first contribution of this thesis is a post matching algorithm that observes the structure of the differences in feature templates to enhance recognition accuracy. The significance of the scheme is its robustness to inaccuracies in the iris segmentation process. Experimental results on the CASIA database indicate the efficacy of the proposed technique. The second contribution of this thesis is a novel iris segmentation scheme that employs Geodesic Active Contours to extract the iris from the surrounding structures. The proposed scheme elicits the iris texture in an iterative fashion depending upon both the local and global conditions of the image. The performance of an iris recognition algorithm on both the WVU non-ideal and CASIA iris database is observed to improve upon application of the proposed segmentation algorithm. The third contribution of this thesis is the fusion of multiple instances of the same iris and multiple iris units of the eye, i.e., the left and right iris at the match score level. Using simple sum rule, it is demonstrated that both multi-instance and multi-unit fusion of iris can lead to a significant improvement in matching accuracy. The final contribution is a technique to create a large database of digital renditions of iris images that can be used to evaluate the performance of iris recognition algorithms. This scheme is implemented in two stages. In the first stage, a Markov Random Field model is used to generate a background texture representing the global iris appearance. In the next stage a variety of iris features, viz., radial and concentric furrows, collarette and crypts, are generated and embedded in the texture field. Experimental results confirm the validity of the synthetic irises generated using this technique