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The collection of data from human subjects for biometrics research in the United States requires the development of a data collection protocol that is reviewed by a Human Subjects Institutional Review Board (IRB). The IRB reviews the protocol for risks and approves it if it meets the criteria for approval specified in the relevant Federal regulations (45 CFR 46). Many other countries operate similar mechanisms for the protection of human subjects. IRBs review protocols for safety, confidentiality, and for minimization of risk associated with identity disclosure. Since biometric measurements are potentially identifying, IRB scrutiny of biometrics data collection protocols can be expected to be thorough. This paper discusses the intricacies of IRB best practices within the worldwide biometrics community. This is important because research decisions involving human subjects are made at a local level and do not set a precedent for decisions made by another IRB board. In many cases, what one board approves is not approved by another board, resulting in significant inconsistencies that prove detrimental to both researchers and human subjects. Furthermore, the level of biometrics expertise may be low on IRBs, which can contribute to the unevenness of reviews. This publication will suggest possible best practices for designing and seeking IRB approval for human subjects research involving biometrics measurements. The views expressed are the opinions of the authors.

Prior research has shown that manually-segmented eyebrows can be used for recognition purposes. However, eyebrow recognition is not as useful without an automated segmentation algorithm. We propose a method to automatically outline the eyebrows in a face using active shape models. We train several models using the images from the Face Recognition Grand Challenge and find that including more landmark points around the eyebrows and including the eyes in the model are beneficial. Our eyebrow active shape model gives a 38.6% improvement over eyebrow segmentation obtained using an open-source face active shape model. When comparing the automatically segmented regions with manual segmentation, we achieve 87% true overlap score with a 12% false overlap score.

Iris biometrics systems rely on analysis of a visual presentation of the human iris, which must be extracted from the periocular region. Topical cosmetics can greatly alter the appearance of the periocular region, and can occlude portions of the iris texture. In this paper, the presence of topical cosmetics is shown to negatively impact the authentic distribution of iris match scores, causing an increase in the false non-match rate at a fixed false match rate.

Iris recognition technology has the potential to broaden to include non-ideal imaging situations, as well as scale to national-level deployments. Hence, the study of population factors, subject intrinsics, and sensing contexts that can individually or collectively impact iris recognition performance assumes increased importance. This presentation summarizes recent research on a number of such "quality variables" and motivates the need for large data sets exhibiting a variety of such non-idealities to adequately characterize performance.

Many security applications require accurate identification of people, and research has shown that iris biometrics can be a powerful identification tool. However, in order for iris biometrics to be used on larger populations, error rates in the iris biometrics algorithms must be as low as possible. Furthermore, these algorithms need to be tested in a number of different environments and configurations. In order to facilitate such testing, we have collected more than 100,000 iris images for use in iris biometrics research. Using this data, we have developed a number of techniques for improving recognition rates. These techniques include fragile bit masking, signal-level fusion of iris images, and detecting local distortions in iris texture. Additionally, we have shown that large degrees of dilation and long lapses of time between image acquisitions negatively impact performance.

This is the first study to compare the PCA and ICP approaches to 3D face recognition, and to propose a local region approach coping with expression variation in 3D face recognition. A new algorithm for 3D face recognition is proposed for handling expression variation. It uses a surface registration-based technique for 3D face recognition. The proposed method uses a fully automatic approach to use to initialize the 3D matching. Results are presented for gallery and probe datasets of 355 subjects imaged in 3D, with significant time lapse between gallery and probe images of a given subject yielding 3,205 3D models. We find that an ICP-based method performs better than a PCA-based method. The evaluation results show that our proposed new algorithm substantially improves performance in the case of varying facial expression. We also examined subject factors in the proposed method on 3D face models by age and gender.

As early as the 1970's it was determined that gait, or the "manner of walking" is an identifying feature of a human being. Since then, extensive research has been done in the field of computer vision to determine how accurately a subject can be identified by gait characteristics. This has necessarily led to the study of how various data collection conditions, such as terrain type, varying camera angles, or a carried briefcase, may affect the identifying features of gait. However, little or no research has been done to question whether such conditions may be inferred from gait analysis. For example, is it possible to determine characteristics of the walking surface simply by looking at statistics derived from the subject's gait? The question to be addressed is whether significant concealed weight distributed on the subject's torso can be discovered through analysis of his gait. Individual trends in subjects in response to increasing concealed weight will be explored, with the objective of finding universal trends that would have obvious security purposes.

Face recognition systems generally require location of face landmarks (often eyes) as an essential preprocessing step. Eye location estimates can be assessed in absolute terms (e.g., proximity to known eye location) and also in application-specific terms (e.g., performance of a system that employs the location). This paper assesses an automatic commercial eye-finding system in absolute and application-specific terms, using two face recognition systems and a database of thousands of images. Experimental results highlight the sensitivity of both systems to accurate eye location estimation as well as quality face lighting and time lapse.

This study considers face recognition using multiple imaging modalities. Face recognition is performed using a PCA-based algorithm on each of three individual modalities: normal 2D intensity images, range images representing 3D shape, and infra-red images representing the pattern of heat emission. The algorithm is separately tuned for each modality. For each modality, the gallery consists of one image of each of the same 127 persons, and the probe set consists of 297 images of these subjects, acquired with one or more week's time lapse. In this experiment, we find a rank-one recognition rate of 71% for infra-red, 91% for 2D, 92% for 3D. We also consider the multi-modal combination of each pair of modalities, and find a rank-one recognition rate of 97% for 2D plus infra-red, 98% for 3D plus infra-red, and 99% for 3D plus 2D. The combination of all three modalities yields a rank-one recognition rate of 100%. We conclude that multi-modal face recognition appears to offer great potential for improved accuracy over using a single 2D image. Larger and more challenging experiments are needed in order to explore this potential.© (2004) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Global face recognition methods based on projection (such as principal component analysis) exhibit a well-known sensitivity to face pitch and yaw variations. This paper introduces and tests a new approach to the normalization of "off-frontal" face images and applies it in a principal component analysis (PCA) framework. Our proposed normalization employs two affine transformations of triangular face regions to register selected facial features. Experiments with the technique demonstrate performance improvements over a traditional normalization method when the proposed normalization step is employed. This experiment reports the results based on an image data set containing 665 images of 269 subjects photographed with noticeable face yaw, plus up to 1100 additional training images of subjects with frontal face pose.

This paper explores alternative models of the interpretation tree (IT), whose search is one of the dominant paradigms for object recognition. Recurrence relations for the unpruned size of eight different types of search tree are introduced. Since exhaustive search of the IT in most recognition systems is impractical, pruning of various types is employed. It is therefore useful to see how much of the IT will be explored in a typical recognition problem. Probabilistic models of the search process have been proposed in the literature and used as a basis for theoretical bounds on search tree size, but experiments on a large number of images suggest that for 3-D object recognition from range data, the error probabilities (assumed to be constant) display significant variation. Hence, the theoretical bounds on the interpretation tree's size can serve only as rough estimates of the computational burden incurred during object recognition.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.