Alibaba and Forty Thieves Algorithm and novel Prioritized Prewitt Pattern(PPP)-based Convolutional Neural Network (CNN) using hyperspherically compressed weights for facial emotion recognition

•A novel Prioritized Prewitt Pattern (PPP)-based CNN is proposed.•AFTAO is used to select the ideal hyper-parameters for CNN data training.•A novel method of compressing the weights on a hypersphere using Riemannian metrics and conjugate gradient-based optimization is proposed. The visual representations created using the self-distillation paradigm of Bootstrap Your Emotion Latent (BYEL) are empirically found to be less evenly distributed than those created using proposed technique. This proposed work promotes the compression of weights on a hypersphere by minimizing the hyperspherical energy of network weights using a novel method of optimizing manifolds through Riemannian metrics and the Conjugate gradient technique. The proposed work demonstrates how regularising the networks of the BYEL architecture reduces the hyperspherical energy of neurons by directly optimising a measure of uniformity alongside the standard loss. This leads to more uniformly distributed representation and better performance for downstream tasks. The Alibaba and Forty Thieves Algorithm-based Optimization (AFTAO) methodology is used to select the most precise collection of hyperparameters for a novel Prioritized Prewitt Pattern (PPP)-based Convolutional Neural Network (CNN) that results in a higher accuracy for all the six datasets used for facial emotion recognition.