Quaternion discrete orthogonal Hahn moments convolutional neural network for color image classification and face recognition

Color image recognition has recently attracted more researchers' attention. Many methods based on quaternions have been developed to improve the classification accuracies. Some approaches have currently used quaternions with convolutional neural network (CNN). Despite the obtained results, these approaches have some weakness such as the computational complexity. In fact, the large size of the input color images necessitates a high number of layers and parameters during the learning process which can generate errors calculation and hence influence the recognition rate. In this paper, a new architecture called quaternion discrete orthogonal Hahn moments convolutional neural network (QHMCNN) for color image classification and face recognition is proposed to reduce the computational complexity of CNN while improving the classification rate. The quaternion Hahn moments are used to extract pertinent and compact features from images and introduced them in quaternion convolutional neural network. Experimental simulations conducted on various databases are demonstrated the performance of the proposed architecture QHMCNN against other relevant methods in state-of-the-art and the robustness under different noise conditions.