Publication

Face Reconstruction Fromdeep Facial Embeddings Using A Convolutional Neural Network

Sébastien Marcel, Hatef Otroshi Shahreza
2022
Conference Papers

Abstract

State-of-the-art (SOTA) face recognition systems generally use deep convolutional neural networks (CNNs) to extract deep features, called embeddings, from face images. The face embeddings are stored in the system's database and are used for recognition of the enrolled system users. Hence, these features convey important information about the user's identity, and therefore any attack using the face embeddings jeopardizes the user's security and privacy. In this paper, we propose a CNN-based structure to reconstruct face images from face embeddings and we train our network with a multi-term loss function. In our experiments, our network is trained to reconstruct face images from SOTA face recognition models (ArcFace and ElasticFace) and we evaluate our face reconstruction network on theMOBIO and LFW datasets. The source code of all the experiments presented in this paper is publicly available so our work can be fully reproduced.

Official source

https://infoscience.epfl.ch/entities/publication/23de263f-24e8-48e3-9c6a-f50e568f4591

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Sébastien Marcel, Hatef Otroshi Shahreza
2022
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/23de263f-24e8-48e3-9c6a-f50e568f4591

About this result

Related concepts (27)

Convolutional neural network

Convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 × 100 pixels.

Artificial neural network

Artificial neural networks (ANNs, also shortened to neural networks (NNs) or neural nets) are a branch of machine learning models that are built using principles of neuronal organization discovered by connectionism in the biological neural networks constituting animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons.

Deep learning

Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.

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