An algorithm for two-dimensional pattern detection by combining Echo State Network-based weak classifiers

Pattern detection is one of the essential technologies in computer vision. To solve pattern detection problems, the system needs a vast amount of computational resources. To train a multilayer perceptron or convolutional neural network, the gradient descent method is commonly used. The method consumes computational resources. To reduce the amount of computation, we propose a two-dimensional pattern detection algorithm based on Echo State Network (ESN). The training rule of ESN is based on one-shot ridge regression, which enables us to avoid the gradient descent. ESN is a kind of recurrent neural network (RNN), which is often used to embed temporal signals inside the network, rarely used for the embedding of static patterns. In our prior work (Kage, 2023), we found that static patterns can be embedded in an ESN network by associating the training patterns with its stable states, or attractors. By using the same training procedure as our prior work, we made sure that we can associate each training patch image with the desired output vector. The resulting performance of a single ESN classifier is, however, relatively poor. To overcome this poor performance, we introduced an ensemble learning framework by combining multiple ESN weak classifiers. To evaluate the performance, we used CMU-MIT frontal face images (CMU DB). We trained eleven ESN-based classifiers by using six CMU DB training images and evaluated the performance by using a CMU DB test image. We succeeded in reducing false positives in the CMU DB test image down to 0.0515 %.