Invariant moments based convolutional neural networks for image analysis

Abstract

The paper proposes a method using convolutional neural network to effectively evaluate the discrimination between face and non face patterns, gender classification using facial images and facial expression recognition. The novelty of the method lies in the utilization of the initial trainable convolution kernels coefficients derived from the zernike moments by varying the moment order. The performance of the proposed method was compared with the convolutional neural network architecture that used random kernels as initial training parameters. The multilevel configuration of zernike moments was significant in extracting the shape information suitable for hierarchical feature learning to carry out image analysis and classification. Furthermore the results showed an outstanding performance of zernike moment based kernels in terms of the computation time and classification accuracy.

Enhancing Convolutional Neural Network using Hu’s Moments

“…Therefore, we have used the degrees 45° and 90° to extract ZMs of the images. Our approach has archived better loss, accuracy, precision, recall and F1 score compared with the work of [18] on the three dataset MNIST hand written digits, MNIST fashion dataset and CIFAR 10 dataset. The use of Zenick moments as initial filters led to feature loss which led to a decrease in loss, accuracy, precision, recall and F1 score.…”

“…We have tested our approach on three datasets which were MNIST handwritten digits dataset, MNIST fashion dataset and CIFAR10 dataset. Finally, we have compared our results with the work of [18] which uses Zernike moments (ZM) as an initial filter to extract invariant features of the images, as motioned above, by implementing their approach on the three datasets. ZM are projections of an image on to the complex Zernike polynomials that are orthogonal over the unit circle.…”

“…On the other hand, adding Hu's moments to the flattening vector led to discriminative and more informative vector therefore a better performance. Table I and Table II shows the results of our approach compared to the results of [18] approach on MNIST handwritten digits dataset. Fig.…”

“…Mahesh et al, [18], [19] and Tahmasbi et al [29] proposed approaches to solve the invariant problem of CNN using Zernike moments. Mahesh et al, [18], [19] proposed a technique which uses Zernike moments in CNN to evaluate the discrimination between face and non-face patterns, and gender classification using facial expression recognition. Their main contribution is the use of Zernike moments as an initial filter, in order to show some unique features of the image that might be helpful to distinguish faces from non-faces image, and gender classifications.…”

“…The main objective and contribution of this work is to enhance CNN regarding the invariant limitation in order to achieve higher accuracy in image classification by using Hu's moments of the image [23]. The Hu's moments of an image are weighted averages of the image's intensities of the pixels, which produce statistics about the image, and these moments are invariant to image transformations [18], [36]. This means that, even if some changes were made to the image or if the shape outline got slightly thicker, it will always produce almost the same moments values [9].…”