Human Face Classification using Genetic Algorithm

The paper presents a precise scheme for the development of a human face classification system based human emotion using the genetic algorithm (GA). The main focus is to detect the human face and its facial features and classify the human face based on emotion, but not the interest of face recognition. This research proposed to combine the genetic algorithm and neural network (GANN) for classification approach. There are two way for combining genetic algorithm and neural networks, such as supportive approach and collaborative approach. This research proposed the supportive approach to developing an emotion-based classification system. The proposed system received frontal face image of human as input pattern and detected face and its facial feature regions, such as, mouth (or lip), nose, and eyes. By the analysis of human face, it is seen that most of the emotional changes of the face occurs on eyes and lip. Therefore, two facial feature regions (such as lip and eyes) have been used for emotion-based classification. The GA has been used to optimize the facial features and finally the neural network has been used to classify facial features. To justify the effectiveness of the system, several images were tested. The achievement of this research is higher accuracy rate (about 96.42%) for human frontal face classification based on emotion.

@article{Setu2016,
  title     = {Human Face Classification using Genetic Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {9},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Tania Akter Setu and Md. Mijanur Rahman},
  doi       = {10.14569/IJACSA.2016.070944},
  url       = {https://doi.org/10.14569/IJACSA.2016.070944}
}