A Parallel Framework for Multilayer Perceptron for Human Face Recognition

TL;DR

This paper introduces a parallel training framework for multilayer perceptrons in human face recognition, comparing two architectures and demonstrating that the One-Class-in-One-Network (OCON) approach converges faster than the All-Class-in-One-Network (ACON).

Contribution

The paper proposes and evaluates a parallel training architecture for MLPs, specifically the OCON structure, which improves training speed for face recognition tasks.

Findings

OCON outperforms ACON in training convergence speed.

Parallel training enables simultaneous class learning.

Both architectures are effective for face recognition under various conditions.

Abstract

Artificial neural networks have already shown their success in face recognition and similar complex pattern recognition tasks. However, a major disadvantage of the technique is that it is extremely slow during training for larger classes and hence not suitable for real-time complex problems such as pattern recognition. This is an attempt to develop a parallel framework for the training algorithm of a perceptron. In this paper, two general architectures for a Multilayer Perceptron (MLP) have been demonstrated. The first architecture is All-Class-in-One-Network (ACON) where all the classes are placed in a single network and the second one is One-Class-in-One-Network (OCON) where an individual single network is responsible for each and every class. Capabilities of these two architectures were compared and verified in solving human face recognition, which is a complex pattern recognition task where several factors affect the recognition performance like pose variations, facial expression changes, occlusions, and most importantly illumination changes. Both the structures were implemented and tested for face recognition purpose and experimental results show that the OCON structure performs better than the generally used ACON ones in term of training convergence speed of the network. Unlike the conventional sequential approach of training the neural networks, the OCON technique may be implemented by training all the classes of the face images simultaneously.