Verifying detected facial parts by multidirectional associative memory
Miki Kitabata, Yoshiyasu Takefuji · 1999
We propose a neural network system for verifying whether a mouth or eyes can be extracted from an image area by backpropagation. It is necessary to test the proposed system in a noisy environment. In the paper, the model of a neural network system for recognizing a mouth is based on the function of peripheral vision. In our research, a mouth has distinct properties of brightness in the right corner of the mouth, the left corner of the mouth, the tip of nose, and the nostril. Furthermore we discovered that humans commonly observe these properties of the mouth regardless of the brightness of lighting, different colors of the mouth, or different form of the mouth. By using these features, we designed an associative memory neural network for the verification.