Autoencoder Based Face Verification System
This work addresses the need for less labeled data in face recognition systems, but it is incremental as it builds on existing autoencoder and deep learning techniques.
The paper tackles the problem of reducing dependency on labeled data for face verification by using autoencoder pre-training, achieving comparable results to state-of-the-art methods on benchmark datasets like LFW and YTF.
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.