Optimized vitality detection in multi-face images with advanced convolutional neural networks

Authentication is always required for the majority of systems in the rapidly evolving globe. Face recognition is a method for identifying or verifying someone based on an image captured by a camera or a single frame from a video. Such complex tasks are beyond the capacity of a computer to do alone. Advanced ideas like deep learning can be applied to the detection and recognition of faces. Face recognition is used in many different contexts, such as user identification, device unlocking, and more. They can also be crucial in identifying multiple locations where multiple people may enter or be present at the same time like student presence in seminar hall and entry cameras, etc. Relying on still photos from printed or digital images for verification poses security risks to users. Using the offered sample photographs as a lead, the multiple face identification and vitality detection methods locate various faces in the image, recognize them, and validate that the person in the frame is alive. This research work uses Encoding Convolutional Neural Network for the face detection, recognition and to verify vitality presence in the biometric system. This model can used in areas such as Indian Senior Pension Scheme for face detection which is essential now a days to monitor whether the person is really present in front of the camera, Human Tracking Systems, National Security Systemsetc.