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Electrocardiography (ECG) is the process of recording the electrical activity of the human heart over time using electrodes that are placed over the skin. While the primary usage of electrocardiograms, the recorded signals, has been focused on the check of signs of heart-related diseases, recent studies have moved also toward their usage for human authentication. Thus, an ECG signal can be unique enough to be used independently as a biometric modality. In addition to its inherent liveness detection, it is easy to collect and can be easily captured either via sensors attached to the human body (fingertips, chest, wrist) or even passively using wireless sensors. In this paper, we propose a novel approach that exploits the spectro-temporal dynamic characteristics of the ECG signal to establish personal recognition system using both short-time Fourier transform (STFT) and generalized Morse wavelets (CWT). This process results in enriching the information extracted from the original ECG signal that is inserted in a 2D convolutional neural network (CNN) which extracts higher level and subject-specific ECG-based features for each individual. To validate our proposed CNN model, we performed nested cross-validation using eight different ECG databases. These databases are considered challenging since they include both normal and abnormal heartbeats as well as a dynamic number of subjects. Our proposed algorithms yield superior performance when compared to other state-ofart approaches discussed in the literature, i.e. the STFT-based one achieves an average identification rate, equal error rate (EER), and area under curve (AUC) of 97.86%, 0.0268, and 0.9933 respectively, whereas the CWT achieves comparable to STFT results in 97.5%, 0.0386, and 0.9882 respectively. 

Arrhythmia is an abnormal heart rhythm that occurs due to the improper operation of the electrical impulses that coordinate the heartbeats. It is one of the most well-known heart conditions (including coronary artery disease, heart failure etc.) that is experienced by millions of people around the world. While there are several types of arrhythmias, not all of them are dangerous or harmful. However, there are arrhythmias that can often lead to death in minutes (e.g, ventricular fibrillation and ventricular tachycardia) even in young people. Thus, the detection of arrhythmia is critical for stopping and reversing its progression and for increasing longevity and life quality. While a doctor can perform different heart-monitoring tests specific to arrhythmias, the electrocardiogram (ECG) is one of the most common ones used either independently or in combination with other tests (to only detect, e.g. echocardiogram, or trigger arrhythmia and, then, detect, e.g. stress test). We propose a machine learning approach that augments the traditional arrhythmia detection approaches via our automatic arrhythmia classification system. It utilizes the texture of the ECG signal in both the temporal and spectro-temporal domains to detect and classify four types of heartbeats. The original ECG signal is first preprocessed, and then, the R-peaks associated with heartbeat estimation are identified. Next, 1D local binary patterns (LBP) in the temporal domain are utilized, while 2D LBPs and texture-based features extracted by a grayscale co-occurrence matrix (GLCM) are utilized in the spectro-temporal domain using the short-time Fourier transform (STFT) and Morse wavelets. Finally, different classifiers, as well as different ECG lead configurations are examined before we determine our proposed time-frequency SVM model, which obtains a maximum accuracy of 99.81%, sensitivity of 98.17%, and specificity of 99.98% when using a 10 cross-validation on the MIT-BIH database. 

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. 

Online underground forums have been widely used by cybercriminals to trade the illicit products, resources and services, which have played a central role in the cybercrim-inal ecosystem. Unfortunately, due to the number of forums, their size, and the expertise required, it's infeasible to perform manual exploration to understand their behavioral processes. In this paper, we propose a novel framework named iDetector to automate the analysis of underground forums for the detection of cybercrime-suspected threads. In iDetector, to detect whether the given threads are cybercrime-suspected threads, we not only analyze the content in the threads, but also utilize the relations among threads, users, replies, and topics. To model this kind of rich semantic relationships (i.e., thread-user, thread-reply, thread-topic, reply-user and reply-topic relations), we introduce a structured heterogeneous information network (HIN) for representation, which is capable to be composed of different types of entities and relations. To capture the complex relationships (e.g., two threads are relevant if they were posted by the same user and discussed the same topic), we use a meta-structure based approach to characterize the semantic relatedness over threads. As different meta-structures depict the relatedness over threads at different views, we then build a classifier using Laplacian scores to aggregate different similarities formulated by different meta-structures to make predictions. To the best of our knowledge, this is the first work to use structural HIN to automate underground forum analysis. Comprehensive experiments on real data collections from underground forums (e.g., Hack Forums) are conducted to validate the effectiveness of our developed system iDetector in cybercrime-suspected thread detection by comparisons with other alternative methods. 

Performing a direct match between images from different spectra (i.e., passive infrared and visible) is challenging because each spectrum contains different information pertaining to the subject’s face. In this work, we investigate the benefits and limitations of using synthesized visible face images from thermal ones and vice versa in cross-spectral face recognition systems. For this purpose, we propose utilizing canonical correlation analysis (CCA) and manifold learning dimensionality reduction (LLE). There are four primary contributions of this work. First, we formulate the cross-spectral heterogeneous face matching problem (visible to passive IR) using an image synthesis framework. Second, a new processed database composed of two datasets consistent of separate controlled frontal face subsets (VIS-MWIR and VIS-LWIR) is generated from the original, raw face datasets collected in three different bands (visible, MWIR and LWIR). This multi-band database is constructed using three different methods for preprocessing face images before feature extraction methods are applied. There are: (1) face detection, (2) CSU’s geometric normalization, and (3) our recommended geometric normalization method. Third, a post-synthesis image denoising methodology is applied, which helps alleviate different noise patterns present in synthesized images and improve baseline FR accuracy (i.e. before image synthesis and denoising is applied) in practical heterogeneous FR scenarios. Finally, an extensive experimental study is performed to demonstrate the feasibility and benefits of cross-spectral matching when using our image synthesis and denoising approach. Our results are also compared to a baseline commercial matcher and various academic matchers provided by the CSU’s Face Identification Evaluation System.