A Comprehensive Study of Face Recognition Using Deep Learning

Koichi Ito, Hiroya Kawai, Takafumi Aoki

研究成果: 書籍の章/レポート/Proceedings会議への寄与査読

2 被引用数 (Scopus)

抄録

With the advent of deep learning, the performance of face recognition has been dramatically improved. On the other hand, there are few reports that discuss why the performance has been improved. In this paper, through comprehensive ex-periments, we analyze which regions are important in CNN-based face recognition. We employ the major four CNNs, AlexNet, ResNet, and EfficientNet, to be able to perform face recognition and three CNN visualization methods, Grad-CAM, Grad-CAM++, and Score-CAM, to visualize the regions in the face image that are emphasized by each face recognition method.

本文言語英語
ホスト出版物のタイトル2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ1762-1768
ページ数7
ISBN(電子版)9789881476890
出版ステータス出版済み - 2021
イベント2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Tokyo, 日本
継続期間: 2021 12月 142021 12月 17

出版物シリーズ

名前2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings

会議

会議2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021
国/地域日本
CityTokyo
Period21/12/1421/12/17

フィンガープリント

「A Comprehensive Study of Face Recognition Using Deep Learning」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル