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1. A Trustworthy Authentication Against Visual Master Face Dictionary Attacks (Trauma)

   https://doi.org/10.1109/ICIP51287.2024.10648241  

   Mohzary, Muhammad; Choi, Baek-Young; Song, Sejun (October 2024, IEEE)

   Facial Recognition Systems (FRS) have become one of the most viable biometric identity authentication approaches in supervised and unsupervised applications. However, FRSs are known to be vulnerable to adversarial attacks such as identity theft and presentation attacks. The master face dictionary attacks (MFDA) leveraging multiple enrolled face templates have posed a notable threat to FRS. Federated learning-based FRS deployed on edge or mobile devices are particularly vulnerable to MFDA due to the absence of robust MF detectors. To mitigate the MFDA risks, we propose a trustworthy authentication system against visual MFDA (Trauma). Trauma leverages the analysis of specular highlights on diverse facial components and physiological characteristics inherent to human faces, exploiting the inability of existing MFDAs to replicate reflective elements accurately. We have developed a feature extractor network that employs a lightweight and low-latency vision transformer architecture to discern inconsistencies among specular highlights and physiological features in facial imagery. Extensive experimentation has been conducted to assess Trauma’s efficacy, utilizing public GAN-face detection datasets and mobile devices. Empirical findings demonstrate that Trauma achieves high detection accuracy, ranging from 97.83% to 99.56%, coupled with rapid detection speeds (less than 11 ms on mobile devices), even when confronted with state-of-the-art MFDA techniques. 

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2. Evaluation of Local Binary Pattern Algorithm for User Authentication with Face Biometric

   https://doi.org/10.1109/icmla51294.2020.00170  

   Gwyn, Tony; Atay, Mustafa; Kaushik, Roy; Esterline, Albert (December 2020, 19th IEEE International Conference on Machine Learning and Applications (ICMLA))

   null (Ed.)

   In the ever-changing world of computer security and user authentication, the username/password standard is becoming increasingly outdated. Using the same username and password across multiple accounts and websites leaves a user open to vulnerabilities, and the need to remember multiple usernames and passwords feels very unnecessary in the current digital age. Authentication methods of the future need to be reliable and fast, while maintaining the ability to provide secure access. Augmenting traditional username-password standard with face biometric is proposed in the literature to enhance the user authentication. However, this technique still needs an extensive evaluation study to show how reliable and effective it will be under different settings. Local Binary Pattern (LBP) is a discrete yet powerful texture classification scheme, which works particularly well with image classification for facial recognition. The system proposed here strives to examine and test various LBP configurations to determine their image classification accuracy. The most favorable configurations of LBP should be examined as a potential way to augment the current username and password standard by increasing their security with facial biometrics. 

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3. Video Surveillance Framework Based on Real-Time Face Mask Detection and Recognition

   https://doi.org/10.1109/INISTA52262.2021.9548475  

   Nasiri, Ehsan; Milanova, Mariofanna; Nasiri, Ardalan (August 2021, International Conference on INnovations in Intelligent SysTems and Applications (INISTA), 2021)

   In this paper we proposed a real-time face mask detection and recognition for CCTV surveillance camera videos. The proposed work consists of six steps: video acquisition and keyframes selection, data augmentation, facial parts segmentation, pixel-based feature extraction, Bag of Visual Words (BoVW) generation, face mask detection, and face recognition. In the first step, a set of keyframes are selected using histogram of gradient (HoG) algorithm. Secondly, data augmentation is involved with three steps as color normalization, illumination correction (CLAHE), and poses normalization (Angular Affine Transformation). In third step, facial parts are segmented using clustering approach i.e. Expectation Maximization with Gaussian Mixture Model (EM-GMM), in which facial regions are segmented into Eyes, Nose, Mouth, Chin, and Forehead. Then, Pixel-based Feature Extraction is performed using Yolo Nano approach, which performance is higher and lightweight model than the Yolo Tiny V2 and Yolo Tiny V3, and extracted features are constructed into Codebook by Hassanat Similarity with K-Nearest neighbor (H-M with KNN) algorithm. For mask detection, L2 distance function is used. The final step is face recognition which is implemented by a Kernel-based Extreme Learning Machine with Slime Mould Optimization (SMO). Experiments conducted using Python IDLE 3.8 for the proposed Yolo Nano model and also previous works as GMM with Deep learning (GMM+DL), Convolutional Neural Network (CNN) with VGGF, Yolo Tiny V2, and Yolo Tiny V3 in terms of various performance metrics. 

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4. FaceSense: Sensing Face Touch with an Ear-worn System

   https://doi.org/10.1145/3478129  

   Kakaraparthi, Vimal; Shao, Qijia; Carver, Charles J.; Pham, Tien; Bui, Nam; Nguyen, Phuc; Zhou, Xia; Vu, Tam (September 2021, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   Face touch is an unconscious human habit. Frequent touching of sensitive/mucosal facial zones (eyes, nose, and mouth) increases health risks by passing pathogens into the body and spreading diseases. Furthermore, accurate monitoring of face touch is critical for behavioral intervention. Existing monitoring systems only capture objects approaching the face, rather than detecting actual touches. As such, these systems are prone to false positives upon hand or object movement in proximity to one's face (e.g., picking up a phone). We present FaceSense, an ear-worn system capable of identifying actual touches and differentiating them between sensitive/mucosal areas from other facial areas. Following a multimodal approach, FaceSense integrates low-resolution thermal images and physiological signals. Thermal sensors sense the thermal infrared signal emitted by an approaching hand, while physiological sensors monitor impedance changes caused by skin deformation during a touch. Processed thermal and physiological signals are fed into a deep learning model (TouchNet) to detect touches and identify the facial zone of the touch. We fabricated prototypes using off-the-shelf hardware and conducted experiments with 14 participants while they perform various daily activities (e.g., drinking, talking). Results show a macro-F1-score of 83.4% for touch detection with leave-one-user-out cross-validation and a macro-F1-score of 90.1% for touch zone identification with a personalized model. 

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5. Face Recognition Using Popular Deep Net Architectures: A Brief Comparative Study

   https://doi.org/10.3390/fi13070164  

   Gwyn, Tony; Roy, Kaushik; Atay, Mustafa (July 2021, Future Internet)

   null (Ed.)

   In the realm of computer security, the username/password standard is becoming increasingly antiquated. Usage of the same username and password across various accounts can leave a user open to potential vulnerabilities. Authentication methods of the future need to maintain the ability to provide secure access without a reduction in speed. Facial recognition technologies are quickly becoming integral parts of user security, allowing for a secondary level of user authentication. Augmenting traditional username and password security with facial biometrics has already seen impressive results; however, studying these techniques is necessary to determine how effective these methods are within various parameters. A Convolutional Neural Network (CNN) is a powerful classification approach which is often used for image identification and verification. Quite recently, CNNs have shown great promise in the area of facial image recognition. The comparative study proposed in this paper offers an in-depth analysis of several state-of-the-art deep learning based-facial recognition technologies, to determine via accuracy and other metrics which of those are most effective. In our study, VGG-16 and VGG-19 showed the highest levels of image recognition accuracy, as well as F1-Score. The most favorable configurations of CNN should be documented as an effective way to potentially augment the current username/password standard by increasing the current method’s security with additional facial biometrics. 

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