Multiple faces detection in real time using neural networks
Stephen Karungaru, Minoru Fukumi, Takuya Akashi, Norio Akamatsu · 2007
Abstract:- In this paper, a real time face detection method using several small size neural networks and a genetic algorithm with adaptive search area control is proposed. Neural networks and genetic algorithms may not be suitable for real time application because of their long processing times. However, in this paper, we show how fast speeds can be achieved using small effective neural networks and a genetic algorithm with a small population size that requires few generations to converge. We subdivide the face into several regions, each connected to an individual neural network. This guarantees small size networks and also offers the ability to learn different face regions features using different coding methods. The genetic algorithm is used during the real time search. It extracts possible faces from face candidates that are then tested using the neural networks. The face candidate area is then adaptively reduced depending on the location of the top six face samples. We then performed real time simulation using an inexpensive USB camera to prove the effectiveness of our proposal. We achieved between 98 and 96 % accuracy for one or multiple faces respectively at 15 to 8 frames per second.