@inproceedings{5e75f48d380d496fbb1b9bca1d5cb192,
title = "Is CNN Really Looking at Your Face?",
abstract = "Face recognition has dramatically improved its performance with the advent of deep learning, especially Convolutional Neural Networks (CNNs), while they have raised a new issue: the difficulty in interpreting the results. The question is what CNN looks at in a face image to identify a person. To answer this question, this paper presents a simple and novel analysis of deep face recognition based on facial parts. We evaluate the recognition accuracy of face images with specific regions masked using face segmentation labels. Our analysis clarifies what CNNs really need in face images for face recognition. The paper concludes with an application of face recognition models to general visualization methods and the problems contained in some classical face image datasets.",
keywords = "Biometrics, CNN, Face parsing, Face recognition",
author = "Hiroya Kawai and Takashi Kozu and Koichi Ito and Chen, {Hwann Tzong} and Takafumi Aoki",
note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 6th Asian Conference on Pattern Recognition, ACPR 2021 ; Conference date: 09-11-2021 Through 12-11-2021",
year = "2022",
doi = "10.1007/978-3-031-02375-0_39",
language = "English",
isbn = "9783031023743",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "525--539",
editor = "Christian Wallraven and Qingshan Liu and Hajime Nagahara",
booktitle = "Pattern Recognition - 6th Asian Conference, ACPR 2021, Revised Selected Papers",
address = "Germany",
}