REaL: Real-time Face Detection and Recognition Using Euclidean Space and Likelihood Estimation
This system addresses the problem of real-time face detection and recognition for applications requiring quick results from live image feeds.
This paper proposes REaL, a real-time face detection and recognition system that processes live images. It achieves promising recognition rates and effectively filters out non-human objects from its calculations.
Detecting and recognizing faces accurately has always been a challenge. Differentiating facial features, training images, and producing quick results require a lot of computation. The REaL system we have proposed in this paper discusses its functioning and ways in which computations can be carried out in a short period. REaL experiments are carried out on live images and the recognition rates are promising. The system is also successful in removing non-human objects from its calculations. The system uses a local database to store captured images and feeds the neural network frequently. The captured images are cropped automatically to remove unwanted noise. The system calculates the Euler angles and the probability of whether the face is smiling, has its left eye, and right eyes open or not.