CardioCam: Leveraging Camera on Mobile Devices to Verify Users While Their Heart is Pumping

With the increasing prevalence of mobile and IoT devices (e.g., smartphones, tablets, smart-home appliances), massive private and sensitive information are stored on these devices. To prevent unauthorized access on these devices, existing user verification solutions either rely on the complexity of user-defined secrets (e.g., password) or resort to specialized biometric sensors (e.g., fingerprint reader), but the users may still suffer from various attacks, such as password theft, shoulder surfing, smudge, and forged biometrics attacks. In this paper, we propose, CardioCam, a low-cost, general, hard-to-forge user verification system leveraging the unique cardiac biometrics extracted from the readily available built-in cameras in mobile and IoT devices. We demonstrate that the unique cardiac features can be extracted from the cardiac motion patterns in fingertips, by pressing on the built-in camera. To mitigate the impacts of various ambient lighting conditions and human movements under practical scenarios, CardioCam develops a gradient-based technique to optimize the camera configuration, and dynamically selects the most sensitive pixels in a camera frame to extract reliable cardiac motion patterns. Furthermore, the morphological characteristic analysis is deployed to derive user-specific cardiac features, and a feature transformation scheme grounded on Principle Component Analysis (PCA) is developed to enhance the robustness of cardiac biometrics for effective user verification. With the prototyped system, extensive experiments involving 25 subjects are conducted to demonstrate that CardioCam can achieve effective and reliable user verification with over 99% average true positive rate (TPR) while maintaining the false positive rate (FPR) as low as 4%

Human Recognition from Video Sequences and Off-Angle Face Images Supported by Respiration Signatures

In this work, we study the problem of human identity recognition using human respiratory waveforms extracted from videos combined with component-based off- angle human facial images. Our proposed system is composed of (i) a physiology- based human clustering module and (ii) an identification module based on facial features (nose, mouth, etc.) fetched from face videos. In our proposed methodology we, first, manage to passively extract an important vital sign (breath), cluster human subjects into nostril motion vs. nostril non-motion groups, and, then, localize a set of facial features, before we apply feature extraction and matching.;Our novel human identity recognition system is very robust, since it is working well when dealing with breath signals and a combination of different facial components acquired in uncontrolled luminous conditions. This is achieved by using our proposed Motion Classification approach and Feature Clustering technique based on the breathing waveforms we produce. The contributions of this work are three-fold. First, we collected a set of different datasets where we tested our proposed approach. Specifically, we considered six different types of facial components and their combination, to generate face-based video datasets, which present two diverse data collection conditions, i.e. videos acquired in head fully frontal position (baseline) and head looking up pose. Second, we propose a new way of passively measuring human breath from face videos and show comparatively identical output against baseline breathing waveforms produced by an ADInstruments device. Third, we demonstrate good human recognition performance when using the pro- posed pre-processing procedure of Motion Classification and Feature Clustering, working on partial features of human faces.;Our method achieves increased identification rates across all datasets used, and it manages to obtain a significantly high identification rate (ranging from 96%-100% when using a single or a combination of facial features), yielding an average of 7% raise, when compared to the baseline scenario. To the best of our knowledge, this is the first time that a biometric system is composed of an important human vital sign (breath) that is fused with facial features is such an efficient manner

Advanced Biometrics with Deep Learning

Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others