|Title:||An ablation study on part-based face analysis using a Multi-input Convolutional Neural Network and Semantic Segmentation||Authors:||Abate, Andrea F.
|UNESCO Clasification:||3307 Tecnología electrónica||Keywords:||Deep Learning
|Issue Date:||2023||Journal:||Pattern Recognition Letters||Abstract:||Face-based recognition methods usually need the image of the whole face to perform, but in some situations, only a fraction of the face is visible, for example wearing sunglasses or recently with the COVID pandemic we had to wear facial masks. In this work, we propose a network architecture made up of four deep learning streams that process each one a different face element, namely: mouth, nose, eyes, and eyebrows, followed by a feature merge layer. Therefore, the face is segmented into the part of interest by means of ROI masks to keep the same input size for the four network streams. The aim is to assess the capacity of different combinations of face elements in recognizing the subject. The experiments were carried out on the Masked Face Recognition Database (M2FRED) which includes videos of 46 participants. The obtained results are 96% of recognition accuracy considering the four face elements; and 92%, 87%, and 63% of accuracy for the best combination of three, two, and one face elements respectively.||URI:||http://hdl.handle.net/10553/124407||ISSN:||0167-8655||DOI:||10.1016/j.patrec.2023.07.010||Source:||Pattern Recognition Letters[ISSN 0167-8655],v. 173, p. 45-49, (Septiembre 2023)|
|Appears in Collections:||Artículos|
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