Ayman Abaza

A survey on ear biometrics

Recognizing people by their ear has recently received significant attention in the literature. Several reasons account for this trend: first, ear recognition does not suffer from some problems associated with other non-contact biometrics, such as face recognition; second, it is the most promising candidate for combination with the face in the context of multi-pose face recognition; and third, the ear can be used for human recognition in surveillance videos where the face may be occluded completely or in part. Further, the ear appears to degrade little with age. Even though current ear detection and recognition systems have reached a certain level of maturity, their success is limited to controlled indoor conditions. In addition to variation in illumination, other open research problems include hair occlusion, earprint forensics, ear symmetry, ear classification, and ear individuality. This article provides a detailed survey of research conducted in ear detection and recognition. It provides an up-to-date review of the existing literature revealing the current state-of-art for not only those who are working in this area but also for those who might exploit this new approach. Furthermore, it offers insights into some unsolved ear recognition problems as well as ear databases available for researchers.

Automatic Tooth Segmentation Using Active Contour Without Edges

Automating the postmortem identification of deceased individuals based on dental characteristics is receiving increased attention especially with the large number of victims encountered in mass disasters. An automated dental identification system compares the teeth present in multiple digitized dental records in order to access their similarity. The primary step in such a system is the estimation of the contour of each tooth in order to permit efficient feature extraction. Extracting the contour of the teeth is a very challenging task and has received inadequate attention in the literature. In this paper, the task of teeth contour extraction is accomplished using active contour without edges. This technique is based on the intensity of the overall region of the tooth image and, therefore, does not necessitate the presence of a sharp boundary between teeth. Further, this technique can extract the region contour in the presence of additive noise and in the absence of well-defined image gradients. Experimental results indicate the benefits of the proposed approach.

Quality based rank-level fusion in multibiometric systems

Multibiometric systems fuse evidences from multiple biometric sources typically resulting in better recognition accuracy. These systems can consolidate information at various levels. For systems operating in the identification mode, rank level fusion presents a viable option. In this paper, several simple but powerful modifications are suggested to enhance the performance of rank-level fusion schemes in the presence of weak classifiers or low quality input images. These modifications do not require a training phase, therefore making them suitable in a wide range of applications. Experiments conducted on a multimodal database consisting of a few hundred users indicate that the suggested modifications to the highest rank and Borda count methods significantly enhance the rank-1 accuracy. Experiments also reveal that including image quality in the fusion scheme enhances the Borda count rank-1 accuracy by ~40%.

Towards understanding the symmetry of human ears: A biometric perspective

In this paper, an analysis of the symmetry of human ears is presented. Such an analysis is essential in order to understand the possibility of matching the left and right ears of an individual, or to reconstruct portions of the ear that may be occluded in a surveillance video. Ear symmetry is assessed geometrically using symmetry operators and Iannarellis measurements, where the contribution of individual ear regions to the overall symmetry of the ear is studied. Next, to assess the ear symmetry (or asymmetry) from a biométrie recognition system perspective, several experiments were conducted on the WVU Ear Database. Our experiments suggest the existence of some degree of symmetry in the human ears that can perhaps be systematically exploited in the design of commercial ear recognition systems. At the same time, the degree of asymmetry it offers may be used in designing effective fusion schemes that combine the face information with the two ears.

Floating Navigator Echo (FNAV) for In-Plane 2D Translational Motion Estimation

A modification of the classical navigator echo (NAV) technique is presented whereby both 2D translational motion components are computed from a single navigator line. Instead of acquiring the NAV at the center of the k‐space, a kx line is acquired off‐center in the phase‐encoding (ky) direction as a floating NAV (FNAV). It is shown that the translational motion in both the readout and phase‐encoding directions can be computed from this line. The algorithm used is described in detail and verified experimentally. The new technique can be readily implemented to replace classic NAV in MRI sequences, with little to no additional cost or complexity. The new method can help suppress 2D translational motion and provide more accurate motion estimates for other motion‐suppression techniques, such as the diminishing variance algorithm. Magn Reson Med 51:403–407, 2004.

Classification of voluntary cough sound and airflow patterns for detecting abnormal pulmonary function

BackgroundInvoluntary cough is a classic symptom of many respiratory diseases. The act of coughing serves a variety of functions such as clearing the airways in response to respiratory irritants or aspiration of foreign materials. It has been pointed out that a cough results in substantial stresses on the body which makes voluntary cough a useful tool in physical diagnosis.MethodsIn the present study, fifty-two normal subjects and sixty subjects with either obstructive or restrictive lung disorders were asked to perform three individual voluntary coughs. The objective of the study was to evaluate if the airflow and sound characteristics of a voluntary cough could be used to distinguish between normal subjects and subjects with lung disease. This was done by extracting a variety of features from both the cough airflow and acoustic characteristics and then using a classifier that applied a reconstruction algorithm based on principal component analysis.ResultsResults showed that the proposed method for analyzing voluntary coughs was capable of achieving an overall classification performance of 94% and 97% for identifying abnormal lung physiology in female and male subjects, respectively. An ROC analysis showed that the sensitivity and specificity of the cough parameter analysis methods were equal at 98% and 98% respectively, for the same groups of subjects.ConclusionA novel system for classifying coughs has been developed. This automated classification system is capable of accurately detecting abnormal lung function based on the combination of the airflow and acoustic properties of voluntary cough.

Design and evaluation of photometric image quality measures for effective face recognition

The performance of an automated face recognition system can be significantly influenced by face image quality. Designing effective image quality index is necessary in order to provide real-time feedback for reducing the number of poor quality face images acquired during enrollment and authentication, thereby improving matching performance. In this study, the authors first evaluate techniques that can measure image quality factors such as contrast, brightness, sharpness, focus and illumination in the context of face recognition. Second, they determine whether using a combination of techniques for measuring each quality factor is more beneficial, in terms of face recognition performance, than using a single independent technique. Third, they propose a new face image quality index (FQI) that combines multiple quality measures, and classifies a face image based on this index. In the authors studies, they evaluate the benefit of using FQI as an alternative index to independent measures. Finally, they conduct statistical significance Z-tests that demonstrate the advantages of the proposed FQI in face recognition applications.

Fast learning ear detection for real-time surveillance

Fully automated image segmentation is an essential step for designing automated identification systems. This paper investigates the problem of real-time image segmentation in the context of ear biometrics. The proposed approach is based on Haar features arranged in a cascaded Adaboost classifier. This method, widely known as Viola-Jones in the context of face detection, has a limitation of an extremely long training time, approximately a month. We efficiently implement a modified training / learning method, which significantly reduces training time. This approach is trained about 80 times faster than the original method, and achieves ~ 95% accuracy based on four different test sets (> 2000 profile images for app. 450 persons). The developed ear detection system is very fast and can be used in a real-time surveillance scenario. Experimental results show that the proposed ear detection is robust in the presence of partial occlusion, noise and multiple ears with various resolutions.

Human Ear Recognition

Currently, there are no commercially available ear recognition systems. However, the future holds tremendous potential for incorporating ear images with face images in a multibiometric configuration, even as researchers continue to refine the technology. For example, assigning an ear image to one of several predefined categories could allow for rapid retrieval of candidate identities from a large database. In addition, the use of ear thermograms could help mitigate the problem of occlusion due to hair and accessories. As the technology matures, both forensic and biometric domains will benefit from this biometric.

Automatic Construction of Dental Charts for Postmortem Identification

Identification of deceased individuals based on dental characteristics is receiving increased attention, especially with the large volume of victims encountered in mass disasters. An important problem in automated dental identification is automatic classification of teeth into four classes (molars, premolars, canines, and incisors). An equally important problem is the construction of a dental chart, which is a data structure that guides tooth-to-tooth matching. Dental charts are the key for avoiding illogical comparisons that inefficiently consume the limited computational resources and may mislead decision making. Labeling of the teeth is a challenging task which has received inadequate attention in the literature. We tackle this composite problem using a two-stage approach. The first stage utilizes low computational cost, appearance-based features for assigning an initial class. The second stage applies a string matching technique, based on teeth neighborhood rules, to validate initial teeth-classes and, hence, to assign each tooth a number corresponding to its location in the dental chart. Based on a large test dataset of 507 bitewing and periapical films that contain 2027 teeth, the proposed approach achieves classification accuracy of 87%. Experimental results indicate that the proposed approach works very fast, and achieves high performance compared to other approaches suggested in the literature.