Development of a Non-contact Two-Tier Biometric Security System for the DSWD 4Ps using Iris recognition and Speech Recognition

There has been a steep rise of contactless payment during COVID-19. The rapid improvements of miniaturized sensors and biometric recognition systems for face identification, fingerprint, iris, and voice are conducive and fit during this rise of COVID-19. Thus, non-contact interactions are the most effective way to fight against the spread of the virus and any other diseases. One of the most used is iris scanners and speech recognition. The study promotes contactless payments to address the accompanying issues in cash aid distribution particularly in the DSWD 4Ps, where it has a two-Tier biometric security system which is iris recognition and speech recognition. This can provide the same type of service and securities as a normal ATM while removing the worry of getting different kinds of viruses and diseases. Testing the iris recognition system, a False acceptance ratio of 13% and 3% of False Rejection rates were achieved. While for the testing of speech recognition (security questions), a False Acceptance Ratio of 0% and False Rejection Ratio of 12.12% were achieved. Lastly, testing of speech recognition (navigation)a False Acceptance Ratio of 0% and False Rejection Ratio of 3.62% were achieved. Giving the system an 84% accuracy for the iris recognition, 87.88% for the security questions, and 96.36% for the navigation. © 2021 IEEE.

Preliminary Diagnosis for Flu using Facial Feature Recognition and Thermal Camera

COVID-19 signs are similar to flu and their symptoms can range from no signs and symptoms (asymptomatic) to severe signs and symptoms (symptomatic). Fever, cough, fatigue, runny or stuffy nose, body aches, conjunctivitis, and headache are some of the symptoms that COVID-19 and flu have in common. Current technologies only take advantage of thermal sensors to identify the individual with possible flu-like symptoms. Adding extra layer of security in preventing the spread of diseases for people with flu-like symptoms must be set up at key locations using a video camera. The study can detect facial features such as reddish eyes, runny nose, dark circles around eyes and measure temperature using a non-contact thermal camera. Gathered image datasets were used for model training. Initial testing with the system revealed that closer distance and better illumination yielded better results upon consultation with a doctor using the comparison of 100 LUX and 1000 LUX lighting conditions. Validations were done using live video feeds where PCA and SVM were used for feature extraction and classification respectively. Support Vector Machine was used to evaluate subject whether they exhibited flu-like symptoms or not and compare the system output with doctors diagnosis. Error rates of 26.67% and 50% were achieved for False Acceptance and False Rejection Rates respectively along with 0% error rates for the temperature detection system. © 2021 IEEE.