What else does your biometric data reveal? A survey on soft biometrics

International audienceRecent research has explored the possibility of extracting ancillary information from primary biometric traits, viz., face, fingerprints, hand geometry and iris. This ancillary information includes personal attributes such as gender, age, ethnicity, hair color, height, weight, etc. Such attributes are known as soft biometrics and have applications in surveillance and indexing biometric databases. These attributes can be used in a fusion framework to improve the matching accuracy of a primary biometric system (e.g., fusing face with gender information), or can be used to generate qualitative descriptions of an individual (e.g., "young Asian female with dark eyes and brown hair"). The latter is particularly useful in bridging the semantic gap between human and machine descriptions of biometric data. In this paper, we provide an overview of soft biometrics and discuss some of the techniques that have been proposed to extract them from image and video data. We also introduce a taxonomy for organizing and classifying soft biometric attributes, and enumerate the strengths and limitations of these attributes in the context of an operational biometric system. Finally, we discuss open research problems in this field. This survey is intended for researchers and practitioners in the field of biometrics

Skin Texture as a Source of Biometric Information

Traditional face recognition systems have achieved remarkable performances when the whole face image is available. However, recognising people from partial view of their facial image is a challenging task. Face recognition systems' performances may also be degraded due to low resolution image quality. These limitations can restrict the practicality of such systems in real-world scenarios such as surveillance, and forensic applications. Therefore, there is a need to identify people from whatever information is available and one of the possible approaches would be to use the texture information from available facial skin regions for the biometric identification of individuals. This thesis presents the design, implementation and experimental evaluation of an automated skin-based biometric framework. The proposed system exploits the skin information from facial regions for person recognition. Such a system is applicable where only a partial view of a face is captured by imaging devices. The system automatically detects the regions of interest by using a set of facial landmarks. Four regions were investigated in this study: forehead, right cheek, left cheek, and chin. A skin purity assessment scheme determines whether the region of interest contains enough skin pixels for biometric analysis. Texture features were extracted from non-overlapping sub-regions and categorised using a number of classification schemes. To further improve the reliability of the system, the study also investigated various techniques to deal with the challenge where the face images may be acquired at different resolutions to that available at the time of enrolment or sub-regions themselves be partially occluded. The study also presented an adaptive scheme for exploiting the available information from the corrupt regions of interest. Extensive experiments were conducted using publicly available databases to evaluate both the performance of the prototype system and the adaptive framework for different operational conditions, such as level of occlusion and mixture of different resolution skin images. Results suggest that skin information can provide useful discriminative characteristics for individual identification. The comparison analyses with state-of-the-art methods show that the proposed system achieved a promising performance

Relatively permanent pigmented or vascular skin marks (RPPVSM) for forensic identification

Performing criminal and victim identification on evidence images can be very challenging at times due to lack of biometric traits. Gunmen, terrorists, and violent riot protesters often cover their faces with masks or clothing, making face recognition impossible. Pedophiles in child sexual abuse images often hide or blur their faces and tattoos to avoid recognition. Though non-facial body parts are commonly observable in the evidence images of these cases, very limited research has been done to use non-facial skin information for criminal and victim identification. This thesis proposes a novel biometric trait named Relatively Permanent Pigmented or Vascular Skin Marks (RPPVSM), which is easily observable on the skin, for forensic identification. In this thesis, legal and operational aspects of RPPVSM for forensic identification are explored. It is required by law that a new science or methodology presented by expert witnesses in U.S. federal trials is scientifically valid and can be properly applied to the facts at issue. Although skin marks have been regularly used in forensic investigation, RPPVSM have not been scientifically studied for personal identification. This thesis aims to address some fundamental questions regarding RPPVSM as a biometric trait, such as how many RPPVSM are sufficient for identification and what their potential error rates are by studying the individuality of RPPVSM patterns in population. This thesis also aims to develop an automated RPPVSM identification system which automatically detects and matches RPPVSM patterns in color images. As preprocessing, a skin segmentation algorithm is developed. The skin segmentation algorithm consists of a clustering operation performed in the YCbCr and normalized RGB color spaces and a histogram analysis to determine the optimal number of clusters in each input image. The RPPVSM detection algorithm consists of preprocessing, RPPVSM candidate detection, and classification. In the preprocessing, blue channel is extracted from the RGB color space and contrast-enhanced. RPPVSM candidates of different sizes are then detected from the preprocessed image by a multi-scale Laplacian of Gaussian (LoG) filtering operation followed by a binary thresholding operation. The detected candidates are then classified as RPPVSM and non-RPPVSM based on contrast, shape, size, texture, and color features using SVM, neural network, and decision tree classifiers. To match RPPVSM patterns, a non-rigid point matching method is employed for registration. Two aligned RPPVSM are considered to be matched if their distance is within a tolerance distance threshold. Since many Asian subjects have only a few RPPVSM on their skin, their RPPVSM patterns are sometimes not unique enough for identification. A fusion system which integrates RPPVSM with vein patterns is proposed to overcome this problem. The proposed RPPVSM identification system and the fusion were evaluated on a total of 3,560 images of backs, chests, arms, and thighs collected from 400 subjects in varying pose, viewpoint, scale, and illumination conditions. The proposed RPPVSM detection algorithm achieved rank-1 and rank-10 identification accuracies of 76.79% and 88.97% respectively, much better than the comparison methods which achieved rank-1 and rank-10 accuracies below 51% and 79% respectively. The fusion improves unimodal vein identification by 1% to 10%, depending on the number of RPPVSM available on the skin. These results signify the potential of the proposed RPPVSM identification system and its fusion for forensic investigation. To the best of our knowledge, this is the first and most comprehensive research on non-facial skin mark patterns for criminal and victim identification.Doctor of Philosophy (SCE