Ear recognition has its advantages in identifying non-cooperative individuals in unconstrained environments. Ear detection is a major step within the ear recognition algorithmic process. While conventional approaches for ear detection have been used in the past, Faster Region-based Convolutional Neural Network (Faster R-CNN) based detection methods have recently achieved superior detection performance in various benchmark studies, including those on face detection. In this work, we propose an ear detection system that uses Faster R-CNN. The training of the system is performed on two stages: First, an AlexNet model is trained for classifying ear vs. non-ear segments. Second, the unified Region Proposal Network (RPN) with the AlexNet, that shares the convolutional features, are trained for ear detection. The proposed system operates in real-time and accomplishes 98 % detection rate on a test set, composed of data coming from different ear datasets. In addition, the system's ear detection performance is high even when the test images are coming from un-controlled settings with a wide variety of images in terms of image quality, illumination and ear occlusion.

Measurement of partial expiratory flow-volume curves has become an important technique in diagnosing lung disease, particularly in children and in the elderly. The objective of this study was to investigate the feasibility of predicting abnormal spirometry using the partial flow-volume curve generated during a voluntary cough. Here, abnormal spirometry is defined as less than the lower limit of normal (LLN) predicted by standard reference equations [1]. Cough airflow signals of 107 subjects (56 male, 51 female) were previously collected [2] from patients performing spirometry in a pulmonary function clinic. A variety of features were extracted from the airflow signal. A support vector machine (SVM) classifier was developed to predict abnormal spirometry. Airflow signal features and SVM parameters were selected using a genetic algorithm. The ability of the classifier to distinguish between normal and abnormal spirometry based on cough flow was evaluated by comparing the classifiers decisions with the LLN for the given subject's spirometry, including forced expiratory volume in one second (FEV1), forced vital capacity (FV C), and their ratio (FEV1=FV C%). Findings indicated that it was possible to classify patients whose spirometry results were less than the LLN with an overall accuracy of 76% for FEV1, 65% for FV C, and 76% for the ratio FEV1=FV C%. Accuracies were determined by repeated double cross-validation [3]. This study demonstrates the potential of using airflow measured during voluntary coughing to identify test subjects with abnormal spirometry.

Automating the process of postmortem identification of deceased individuals based on dental characteristics is receiving increased attention. With the large number of victims encountered in mass disasters (e.g., Asian tsunami), automating the identification process would enhance the scalability of this biometric. However, archiving and retrieving dental records from large databases is a challenging task and has received inadequate attention in the literature. This paper concerns itself with the task of efficient fast retrieving dental records from a database in order to assist the forensic expert in identifying deceased individuals in a rapid manner. The proposed method is an appearance-based technique that consolidates the evidence presented by individual teeth in a dental record, i.e., it `moves' from tooth-to-tooth in order to render a record-to-record matching score. The proposed method is shown to reduce the searching time of record-to-record matching by a factor of hundred. Experimental results indicate that the proposed approach requires significantly less time compared to the other approaches suggested in the literature thereby underscoring its relevance in real-time applications.

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.