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.

翻译：本研究的主要目标是提出一种替代方法，旨在减少对标注数据的依赖。所提出的方法采用自编码器预训练技术，用于两阶段的人脸图像识别任务。首先，使用大量无标注训练数据集以无监督方式训练自编码器；随后，利用预训练自编码器的初始化参数训练深度学习模型。该深度学习训练过程采用有监督方式，使用相对有限的标注训练数据集。在评估阶段，人脸图像嵌入作为深度神经网络层的输出生成。训练过程在CelebA数据集上执行，评估则使用基准人脸识别数据集，如Labeled Faces in the Wild (LFW) 和 YouTube Faces (YTF)。实验结果表明，通过使用预训练自编码器参数初始化深度神经网络，可获得与现有最优方法相当的性能。