Improved Authentication and Drowsiness Detection from Facial Features using Deep Learning Framework in Real Time Environments

The automobile industry has seen a rise in global popularity in recent years. Unfortunately, though, automobile crime is growing exponentially. The majority of cars are currently operated via mechanical keys, safety cards, passwords, and pattern recognition systems. However, with advancements in IoT and embedded technologies, vehicle security systems have been constantly improving. There are numerous recognized techniques for biometric identification and validation available. Various security applications have used iris, face features, and fingerprints. A reliable biometric method is face recognition. For car security as well as alarm devices since it is based upon information about the features of a human face and can function in a variety of settings. The creation of a car guard as well as alarm system employing biometric security based on systems for facial recognition is suggested in this survey research. And, extend the framework to predict drowsiness based on eye features monitoring. The sensitivity of various eye monitoring systems that have been presented up to this point to changing illumination conditions is a frequent issue. We can implement the hybrid system for combining both face recognition and drowsiness detection. The Driver Face Recognition-Anti Theft System is a security measure that can be implemented in a driving setting. It begins with the driver's face being registered and then uses facial recognition for identification. And to use the HAAR Cascade algorithm to recognize faces and the higher accuracy Convolutional Neural Network technology to track the states of the eyes. If it is detected that a driver is acting strangely while having their eyes closed, an automatic alarm may go off, alerting the nearby residents utilize the Python Framework to put this survey to the test while driving.