Face Space Representations in Deep Convolutional Neural Networks

被引：89

作者：

O'Toole, Alice J. ^{[1
]}

Castillo, Carlos D. ^{[2
]}

Parde, Connor J. ^{[1
]}

Hill, Matthew Q. ^{[1
]}

Chellappa, Rama ^{[2
]}

机构：

[1] Univ Texas Dallas, Sch Behav & Brain Sci, Richardson, TX 75083 USA

[2] Univ Maryland, Inst Adv Comp Studies, College Pk, MD 20742 USA

来源：

TRENDS IN COGNITIVE SCIENCES | 2018年 / 22卷 / 09期

关键词：

FUNCTIONAL ARCHITECTURE; RECOGNITION MEMORY; MODEL; SHAPE; NEOCOGNITRON; PERFORMANCE; PERCEPTION; MECHANISM; RESPONSES; CORTEX;

D O I：

10.1016/j.tics.2018.06.006

中图分类号：

B84 [心理学]; C [社会科学总论]; Q98 [人类学];

学科分类号：

03 ; 0303 ; 030303 ; 04 ; 0402 ;

摘要：

Inspired by the primate visual system, deep convolutional neural networks (DCNNs) have made impressive progress on the complex problem of recognizing faces across variations of viewpoint, illumination, expression, and appearance. This generalized face recognition is a hallmark of human recognition for familiar faces. Despite the computational advances, the visual nature of the face code that emerges in DCNNs is poorly understood. We review what is known about these codes, using the long-standing metaphor of a 'face space' to ground them in the broader context of previous-generation face recognition algorithms. We show that DCNN face representations are a fundamentally new class of visual representation that allows for, but does not assure, generalized face recognition.

引用

页码：794 / 809

页数：16

共 50 条

[21] Deep Shading: Convolutional Neural Networks for Screen Space Shading
Nalbach, O.
Arabadzhiyska, E.
Mehta, D.
Seidel, H. -P.
Ritschel, T.
COMPUTER GRAPHICS FORUM, 2017, 36 (04) : 65 - 78
[22] Space Object Classification Using Deep Convolutional Neural Networks
Linares, Richard
Furfaro, Roberto
2016 19TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), 2016, : 1140 - 1146
[23] Race Classification from Face using Deep Convolutional Neural Networks
Wu, Xulei
Yuan, Peijiang
Wang, Tianmiao
Gao, Doudou
Cai, Ying
2018 3RD IEEE INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS AND MECHATRONICS (IEEE ICARM), 2018, : 1 - 6
[24] Understanding unconventional preprocessors in deep convolutional neural networks for face identification
Chollette C. Olisah
Lyndon Smith
SN Applied Sciences, 2019, 1
[25] Understanding unconventional preprocessors in deep convolutional neural networks for face identification
Olisah, Chollette C.
Smith, Lyndon
SN APPLIED SCIENCES, 2019, 1 (11):
[26] Modeling naturalistic face processing in humans with deep convolutional neural networks
Guo Jiahui
Ma Feilong
Castello, Matteo Visconti di Oleggio
Nastase, Samuel A.
Haxby, James V.
Gobbini, M. Ida
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2023, 120 (43)
[27] Face Detection for Crowd Analysis Using Deep Convolutional Neural Networks
Kneis, Bryan
ENGINEERING APPLICATIONS OF NEURAL NETWORKS, EANN 2018, 2018, 893 : 71 - 80
[28] Spectral Representations for Convolutional Neural Networks
Rippel, Oren
Snoek, Jasper
Adams, Ryan P.
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 28 (NIPS 2015), 2015, 28
[29] Deep Multiple Instance Convolutional Neural Networks for Learning Robust Scene Representations
Li, Zhili
Xu, Kai
Xie, Jiafen
Bi, Qi
Qin, Kun
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2020, 58 (05): : 3685 - 3702
[30] Convolutional neural networks for face recognition
Lawrence, S
Giles, CL
Tsoi, AC
1996 IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, PROCEEDINGS, 1996, : 217 - 222

← 1 2 3 4 5 →