Optimizing feature extraction for the camera mouse using genetic algorithms
Stephen Karungaru, Minoru Fukumi, Takuya Akashi, Norio Akamatsu · Annual Conference on Computers · 2006
In this paper, we propose an optimized online facial feature extraction method for a camera mouse using neural networks and genetic algorithms. The method consists of skin color region segmentation and facial features extraction using a neural network whose search is guided by a genetic algorithm. Skin color detection is carried out using a hybrid method based on a YIQ color space threshold and neural network skin color detection methods. The facial features tracked are the eyes and the mouth. A neural network is first trained to detect the eyes and mouth of fixed size and rotation. The genetic algorithm is used to overcome size and orientation invariance by automatic selection of the neural network test samples. Once a feature is detected, it is tracked in the following frames by inheriting the genetic algorithms parameters from the earlier frame. This work was carried out at 7.5 frames per second. Experiments were performed to verify the effectiveness of this method. From the results, an average system accuracy of 95.8% was achieved.