Identification Documents Research Articles

Gender Marker Laws and Access to Emergency Rental Assistance for Transgender Renters in the United States During COVID-19

Barriers to changing gender markers on identification documents (IDs) create many economic challenges for transgender and gender nonconforming individuals. In the United States, each state has different laws and processes for changing the gender marker on a state ID. Using Household Pulse Survey data, this paper investigates whether transgender renters residing in states with several hurdles to changing gender on a driver’s license had a more difficult time accessing emergency rental assistance during the COVID-19 pandemic. Descriptive results from baseline logistic regression models suggest that among transgender individuals, residing in states that require proof of sex-change surgery, a court order, or an amended birth certificate to change gender on a driver’s license was associated with a lower likelihood of receiving emergency rental assistance after applying. Gender marker laws have deep consequences for housing policy: state-level considerations for the transgender population should be included in housing policy design.

MobileMRZNet: Efficient and Lightweight MRZ Detection for Mobile Devices

The Machine Readable Zone (MRZ) is a standardized section found on identification documents (IDs) that adhere to the International Civil Aviation Association (ICAO) Document 9303. The MRZ region contains sensitive personal information about the document holder, and a portion of this information is utilized to establish communication between the passive chip within the ID and a mobile device via Near Field Communication (NFC) protocol. This communication is crucial as the data retrieved from the ID's chip is subsequently used in authentication steps such as face or fingerprint recognition. Thus, accurate detection of the personal information within the MRZ region is vital. In this research study, we propose a fast and lightweight approach for MRZ region detection called MobileMRZNet, which is based on the BlazeFace model. The MobileMRZNet architecture is specifically designed for mobile Graphical Processing Units (GPUs) and enables rapid and precise detection of MRZ regions. To train and evaluate the model, a dataset consisting of both simulated and real data was created using Turkish national IDs. The BlazeFace model was reconfigured and trained specifically for MRZ region detection. The detector, based on BlazeFace and trained on augmented real and simulated data, demonstrates excellent generalization capabilities for deployment with real IDs. Both qualitative and quantitative results confirm the superiority of our proposed method. The mean Intersection over Union (IoU) for the first frame, without utilizing any layout alignment for IDs, achieves an accuracy of approximately 81%. For character recognition, the method achieves 100% accuracy after three consecutive frames. The model operates in less than 10 milliseconds on a mobile device, and its size is around 400 KB, making it significantly fast, lightweight, and robust compared to any existing MRZ detection methods.