Spartan Face Mask Detection and Facial Recognition System

According to the World Health Organization (WHO), wearing a face mask is one of the most effective protections from airborne infectious diseases such as COVID-19. Since the spread of COVID-19, infected countries have been enforcing strict mask regulation for indoor businesses and public spaces. While wearing a mask is a requirement, the position and type of the mask should also be considered in order to increase the effectiveness of face masks, especially at specific public locations. However, this makes it difficult for conventional facial recognition technology to identify individuals for security checks. To solve this problem, the Spartan Face Detection and Facial Recognition System with stacking ensemble deep learning algorithms is proposed to cover four major issues: Mask Detection, Mask Type Classification, Mask Position Classification and Identity Recognition. CNN, AlexNet, VGG16, and Facial Recognition Pipeline with FaceNet are the Deep Learning algorithms used to classify the features in each scenario. This system is powered by five components including training platform, server, supporting frameworks, hardware, and user interface. Complete unit tests, use cases, and results analytics are used to evaluate and monitor the performance of the system. The system provides cost-efficient face detection and facial recognition with masks solutions for enterprises and schools that can be easily applied on edge-devices.

Connecting Gender Identity and Freedom of Conscience in Recent Romanian Constitutional Case-Law

The Constitutional Court of Romania has recently ruled unconstitutional a new provision amending the Law regarding national education, meant to prohibit “any activity of disseminating the theory or opinion of gender identity, understood as the theory or opinion that gender is a concept different from biologic sex and that the two are not always the same”. This provision was found in breach of several constitutional principles, including freedom of conscience and freedom of expression. This decision makes for a brief ingression into the legal nature of gender identity and that of freedom of conscience, allowing for the former to serve as a means to clarifying the scope of application of the latter. Since gender identity recognition is not a “world view”, but a reflection of diversity which is integral to a plural, democratic society, the only way the said provision breached freedom of conscience involved its interior dimension: the freedom of thought of pupils and students. But it did not even involve an interference with the right to manifest a “conviction”, as far as pupils, students and also teachers are concerned. Nonetheless, it breached their freedom of expression.

Enhanced Local Patterns Using Deep Learning Techniques for ECG Based Identity Recognition System

Electrocardiogram (ECG) signals have distinct features of the electrical activity of the heart which are unique among individuals and have recently emerged as a potential biometric tool for human identification. The paper attempts to address the problem of ECG identification based on non-fiducial approach using unsupervised classifier and a Deep Learning approaches. This work investigates the ability of local binary pattern to extract the significant pattern/feature that describes the heartbeat activity for each person’s ECG and the use of staked autoencoders and deep belief network to further enhance the extracted features and classify them based on their heartbeat activity. The proposed approach is validated using experimental datasets from two publicly available databases MIT-BIH Normal Sinus Rhythm and ECG-ID and the results demonstrate the effectiveness of this approach for ECG-based human authentication.