Extended feed forward neural networks with random weights for face recognition
Face recognition is always a hot topic in the field of pattern recognition and computer vision. Generally, images or features are often converted into vectors in the process of recognition. This method usually results in the distortion of correlative information of the elements in the vectorization of an image matrix. This paper designs a classifier called two dimensional neural network with random weights (2D-NNRW) which can use matrix data as direct input, and can preserve the image matrix structure. Specifically, the proposed classifier employs left and right projecting vectors to replace the usual high dimensional input weight in the hidden layer to keep the correlative information of the elements, and adopts the idea of neural network with random weights (NNRW) to learn all the parameters. Experiments on some famous databases validate that the proposed classifier 2D-NNRW can embody the structural character of the face image and has good performance for face recognition.
face recognition classifier matrix data neural network with random weights (nnrw)