Real Time Facial Mask Detection and Gender Identification

This work presents a real time system that focuses on facial detection, mask detection and gender identification. The proposed framework uses advanced computer vision techniques to capture video input, classify faces using pre trained Haar cascades or deep learning models, classify on mask status and gender classification using CNNs. With video frames being processed efficiently by the system, its public safety and demographic analysis benefits can be immediate. This integration of making facial recognition work with mask compliance monitoring and gender classification is a powerful indication of the use of artificial intelligence in public health, security, and analytics scenarios. The model is trained on a dataset with images of people with and without masks, detecting the presence of a mask in real-time for applications Gender identification is the task of predicting a person's gender based on their facial features. The system predicts whether the person is male or female based on visual cues from their face, commonly used in marketing, security, and personalized systems. Both tasks use deep learning to analyze facial features efficiently. The future will see emotion recognition, multi-threading for speed, and edge computing for running on resource constrained devices.