A Comparative Study Between GSM and CNN to Develop Gesture Detection Based Alert System for Women Safety - Chandana D.; Nandana B.; Nishana S.; Poornima M.; Sangeetha Sanil; Anjali Surendran (International Journal of Electrical Power and Machine Systems, 2024)
Women’s safety is a pressing issue in today’s world, and technology can play a crucial role in addressing it. This project introduces a facial expression recognition device that uses Convolution Neural Network (CNN) technology and develop it’s comparison with an expression system with use of GSM is done. Unlike traditional methods relying on manual activation or dedicated devices, this system reacts instantly to threatening situations by recognizing predefined gestures, ensuring swift alerts without drawing attention. The device collects a large dataset of labelled facial images, which may include various facial expressions, emotions, and other facial features. The facial analysis software maps out the facial features, converts them mathematically, and stores them as ’faceprints’ or other representations. Through the use of supervised learning techniques, the model is taught to identify certain face features and the labels or categories that correspond with them. As the device’s camera captures a face, the CNN springs into action, comparing features with what it has learned to pinpoint potential threats. The de-vice’s dynamic alert system, specifically designed for women’s safety, unleashes auditory cues upon detecting a potential threat, ensuring immediate user notification. In order to respond quickly in emergency situations, users can also assume control by manually triggering alarms. The device facilitates seamless communication with selected contacts, allowing women to notify others about detected threats promptly. The synergy of CNN technology, user control, and communication features creates a powerful tool for women’s safety. In comparative study it is found that alert system developed with aid of CNN is more effective and reliable with compare to alert system developed with aid of GSM modules.
Keywords: Convolution Neural Network (CNN); Facial expression recognition; GSM modules; threat detection; women's safety