Autoencoder Based Face Verification System

TL;DR

This paper introduces an autoencoder-based face verification system that reduces reliance on labeled data by pre-training with unlabeled images, then fine-tuning with limited labeled data, achieving competitive results on standard benchmarks.

Contribution

The novel approach combines unsupervised autoencoder pre-training with supervised face recognition training, improving data efficiency and maintaining high accuracy.

Findings

Achieves comparable accuracy to state-of-the-art methods.

Reduces dependency on large labeled datasets.

Effective on benchmark face recognition datasets.

Abstract

The primary objective of this work is to present an alternative approach aimed at reducing the dependency on labeled data. Our proposed method involves utilizing autoencoder pre-training within a face image recognition task with two step processes. Initially, an autoencoder is trained in an unsupervised manner using a substantial amount of unlabeled training dataset. Subsequently, a deep learning model is trained with initialized parameters from the pre-trained autoencoder. This deep learning training process is conducted in a supervised manner, employing relatively limited labeled training dataset. During evaluation phase, face image embeddings is generated as the output of deep neural network layer. Our training is executed on the CelebA dataset, while evaluation is performed using benchmark face recognition datasets such as Labeled Faces in the Wild (LFW) and YouTube Faces (YTF). Experimental results demonstrate that by initializing the deep neural network with pre-trained autoencoder parameters achieve comparable results to state-of-the-art methods.