Here’s a list of my most relevant publications. You can also find a complete list of all my publication in my Scholar profile.
Published in International Conference on Pattern Recognition (ICPR), 2022
In this paper, we propose to swap facial parts as a way to disentangle the recognition relevance of different face parts, like eyes, nose and mouth. In our method, swapping parts from a source face to a target one is performed by fitting a 3D prior, which establishes dense pixels correspondence between parts, while also handling pose differences.
Published in ACM Transactions on Multimedia Computing Communications and Applications (TOMM), 2022
In this paper, we propose a conceptually simple yet robust solution to tackle adversarial attacks on image classification. Our defense works by first applying a JPEG compression with a random quality factor; compression artifacts are subsequently removed by means of a generative model (AR-GAN).
Published in ACM Transactions on Multimedia Computing Communications and Applications (TOMM), 2022
In this article, we propose a hybrid framework for cross-resolution 3D face recognition which utilizes a Streamed Attention Network (SAN) that combines handcrafted features with Convolutional Neural Networks (CNNs).
Published in IEEE/CVF International Conference on Computer Vision and Pattern Recognition (CVPR), 2022
In this paper, we propose a solution to the task of generating dynamic 3D facial expressions from a neutral 3D face and an expression label.
Published in IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021
In this work, we propose a novel sparse 3D Morphable Model construction pipeline, together with a landmark-free dense 3D face registration techinque.
Published in Computer Vision and Image Understanding (CVIU), 2019
In this work, we propose an approach based on a Conditional Generative Adversarial Network (CGAN) for refining the coarse face reconstruction as obtained with standard 3DMM fitting techniques.
Published in IEEE Transactions on Image Processing (TIP), 2018
In this paper, we present a thorough analysis of several aspects that impact on the use of DCNN for face recognition. The evaluation has been carried out from two main perspectives: the network architecture and the similarity measures used to compare deeply learned features; and the data (source and quality) and their preprocessing (bounding box and alignment). The results obtained on the IARPA Janus Benchmark-A, MegaFace, UMDFaces, and YouTube Faces data sets indicate viable hints for designing, training, and testing DCNNs.
Published in IEEE Transactions on Multimedia (TMM), 2017
In this paper, we propose a new approach for constructing a 3D morphable shape model (called DL-3DMM) including both identity and expression variations, which can reach the accuracy of deformation required in applications where fine details of the face are concerned.
Published in International Conference on Pattern Recognition (ICPR), 2016
In this paper, we propose a new and effective frontalization algorithm based on a 3D Morphable Face Model (3DMM) for frontal rendering of unconstrained face images, to be used for face recognition.