We introduce an algorithm based on the morphological shared-weight neural network. Which extract the features and then classify them. This type of network can work effectively, even if the gray level intensity and facial expression of the images are varied. The images are processed by a morphological shared weight neural network to detect and extract the features of face images. For the detection of the edges of the image we are using sobel operator. We are using back propagation algorithm for the purpose of learning and training of the neural network system. Being nonlinear and translation-invariant, the morphological operations can be used to create better generalization during face recognition. Feature extraction is performed on grayscale images using hit-miss transforms that are independent of gray-level shifts. The recognition efficiency of this modified network is about 98%.
Rani, Pratibha; Sirohi, Anshu; and Singh, Manish Kumar
"A Modified Neural Network system based on Morphological operations for detection of images with variation in Gray level intensity and facial expressions,"
International Journal of Image Processing and Vision Science: Vol. 1:
1, Article 11.
Available at: https://www.interscience.in/ijipvs/vol1/iss1/11