Brain Inspired Face Recognition: A Computational Framework

TL;DR

This paper introduces a brain-inspired computational face recognition model combining HOG, LBP, and PCA features, classified by MLPs, achieving competitive results against deep learning methods on challenging datasets.

Contribution

It proposes a novel, brain-inspired face recognition framework using simple features and classifiers, demonstrating competitive performance without deep learning.

Findings

Achieved high accuracy on datasets with extreme variations.

Outperformed several CNN and deep learning methods.

Validated the effectiveness of simple, brain-inspired computational processes.

Abstract

This paper presents a new proposal of an efficient computational model of face recognition which uses cues from the distributed face recognition mechanism of the brain, and by gathering engineering equivalent of these cues from existing literature. Three distinct and widely used features: Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP), and Principal components (PCs) extracted from target images are used in a manner which is simple, and yet effective. The HOG and LBP features further undergo principal component analysis for dimensionality reduction. Our model uses multi-layer perceptrons (MLP) to classify these three features and fuse them at the decision level using sum rule. A computational theory is first developed by using concepts from the information processing mechanism of the brain. Extensive experiments are carried out using ten publicly available datasets to validate our proposed model's performance in recognizing faces with extreme variation of illumination, pose angle, expression, and background. Results obtained are extremely promising when compared with other face recognition algorithms including CNN and deep learning-based methods. This highlights that simple computational processes, if clubbed properly, can produce competing performance with best algorithms.