Local receptive fields based extreme learning machine for face recognition

In the recent years, many face recognition methods were developed and applied in the image processing applications, such as artificial neural networks (ANN), convolutional neuron network (CNN), Gaussian mixtures and so on. However, each of them is suffering from some issues like the local minima, intensive human intervention rate, and slow convergence. CNN generally uses the back propagation (BP) learning procedure, which has very long training periods. In addition, back-propagation generally tends to reach a local minimum. While each of the above methods needs many iterations to reduce the error rate in the training section, we apply extreme learning machine, which it runs throw pooling map with Local receptive fields, in this case, the proposed method can get the result just in one iteration. In addition, ELM proposed to alleviate these drawbacks of the back-propagation method. The Extensive experiments were conducted on three face datasets namely Caltech, UFI, and CBCL. The obtained results are encouraging and compared with several other results previously reported. Testing accuracy in Caltech face dataset is 98.15%, also in the CBCL dataset is 98.34% and in UFI face dataset is 66.11%. Based on the above result the extreme learning machine based on local receptive fields can have more advantage among previous methods used for face recognition.