JP7396509B2 - 機械学習プログラム、機械学習方法および推定装置 - Google Patents

機械学習プログラム、機械学習方法および推定装置 Download PDF

Info

Publication number
JP7396509B2
JP7396509B2 JP2022551068A JP2022551068A JP7396509B2 JP 7396509 B2 JP7396509 B2 JP 7396509B2 JP 2022551068 A JP2022551068 A JP 2022551068A JP 2022551068 A JP2022551068 A JP 2022551068A JP 7396509 B2 JP7396509 B2 JP 7396509B2
Authority
JP
Japan
Prior art keywords
image
model
machine learning
subject
intensity
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
JP2022551068A
Other languages
English (en)
Japanese (ja)
Other versions
JPWO2022064660A5 (https=
JPWO2022064660A1 (https=
Inventor
淳哉 斎藤
昭嘉 内田
健太郎 村瀬
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Fujitsu Ltd
Original Assignee
Fujitsu Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Fujitsu Ltd filed Critical Fujitsu Ltd
Publication of JPWO2022064660A1 publication Critical patent/JPWO2022064660A1/ja
Publication of JPWO2022064660A5 publication Critical patent/JPWO2022064660A5/ja
Application granted granted Critical
Publication of JP7396509B2 publication Critical patent/JP7396509B2/ja
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/174—Facial expression recognition
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/174—Facial expression recognition
    • G06V40/176—Dynamic expression
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/09—Supervised learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/774—Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/70—Labelling scene content, e.g. deriving syntactic or semantic representations
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/174—Facial expression recognition
    • G06V40/175—Static expression
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30196—Human being; Person
    • G06T2207/30201—Face

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Computation (AREA)
  • Artificial Intelligence (AREA)
  • Software Systems (AREA)
  • Human Computer Interaction (AREA)
  • Computing Systems (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • Databases & Information Systems (AREA)
  • Medical Informatics (AREA)
  • Computational Linguistics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Data Mining & Analysis (AREA)
  • Molecular Biology (AREA)
  • General Engineering & Computer Science (AREA)
  • Mathematical Physics (AREA)
  • Image Analysis (AREA)
  • Image Processing (AREA)
JP2022551068A 2020-09-25 2020-09-25 機械学習プログラム、機械学習方法および推定装置 Active JP7396509B2 (ja)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/JP2020/036456 WO2022064660A1 (ja) 2020-09-25 2020-09-25 機械学習プログラム、機械学習方法および推定装置

Publications (3)

Publication Number Publication Date
JPWO2022064660A1 JPWO2022064660A1 (https=) 2022-03-31
JPWO2022064660A5 JPWO2022064660A5 (https=) 2023-05-15
JP7396509B2 true JP7396509B2 (ja) 2023-12-12

Family

ID=80846435

Family Applications (1)

Application Number Title Priority Date Filing Date
JP2022551068A Active JP7396509B2 (ja) 2020-09-25 2020-09-25 機械学習プログラム、機械学習方法および推定装置

Country Status (5)

Country Link
US (1) US20230237845A1 (https=)
EP (1) EP4220546A4 (https=)
JP (1) JP7396509B2 (https=)
CN (1) CN116018613A (https=)
WO (1) WO2022064660A1 (https=)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP7746917B2 (ja) * 2022-05-13 2025-10-01 富士通株式会社 訓練データ生成プログラム、訓練データ生成方法及び訓練データ生成装置
JP2025067257A (ja) * 2023-10-12 2025-04-24 株式会社Ridge-i 情報処理装置、画像評価方法及び画像評価プログラム、教師データ生成装置、教師データ生成方法、教師データ生成プログラム

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2014119879A (ja) 2012-12-14 2014-06-30 Nippon Hoso Kyokai <Nhk> 顔表情評価結果平滑化装置および顔表情評価結果平滑化プログラム
CN109657586A (zh) 2018-12-10 2019-04-19 华中师范大学 一种基于排序卷积神经网络的人脸表情分析方法及系统

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2017210462A1 (en) 2016-06-01 2017-12-07 Ohio State Innovation Foundation System and method for recognition and annotation of facial expressions
JP2018036734A (ja) 2016-08-29 2018-03-08 日本放送協会 表情変化検出装置及びプログラム
KR102564854B1 (ko) * 2017-12-29 2023-08-08 삼성전자주식회사 정규화된 표현력에 기초한 표정 인식 방법, 표정 인식 장치 및 표정 인식을 위한 학습 방법
JP2020057111A (ja) * 2018-09-28 2020-04-09 パナソニックＩｐマネジメント株式会社 表情判定システム、プログラム及び表情判定方法
CN110188615B (zh) * 2019-04-30 2021-08-06 中国科学院计算技术研究所 一种人脸表情识别方法、装置、介质及系统
CN110765873B (zh) * 2019-09-19 2022-08-16 华中师范大学 一种基于表情强度标签分布的面部表情识别方法与装置
CN111582067B (zh) * 2020-04-22 2022-11-29 西南大学 人脸表情识别方法、系统、存储介质、计算机程序、终端

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2014119879A (ja) 2012-12-14 2014-06-30 Nippon Hoso Kyokai <Nhk> 顔表情評価結果平滑化装置および顔表情評価結果平滑化プログラム
CN109657586A (zh) 2018-12-10 2019-04-19 华中师范大学 一种基于排序卷积神经网络的人脸表情分析方法及系统

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
SU Lumei, et al.，Early Facial Expression Recognition using Early RankBoost，2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG)，米国，2013年，Retrieved from the Internet:<URL: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6553740>
YANG Peng, et al.，RankBoost with l1 regularization for Facial Expression Recognition and Intensity Estimation，2009 IEEE 12th International Conference on Computer Vision (ICCV)，米国，2009年，pp.1018-1025，Retrieved from the Internet:<URL:https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5459371&tag=1>

Also Published As

Publication number Publication date
EP4220546A1 (en) 2023-08-02
WO2022064660A1 (ja) 2022-03-31
JPWO2022064660A1 (https=) 2022-03-31
EP4220546A4 (en) 2023-10-25
US20230237845A1 (en) 2023-07-27
CN116018613A (zh) 2023-04-25

Similar Documents

Publication Publication Date Title
Panwar et al. Modeling EEG data distribution with a Wasserstein generative adversarial network to predict RSVP events
Monteiro et al. A multiple hold-out framework for Sparse Partial Least Squares
JP7014100B2 (ja) 拡張装置、拡張方法及び拡張プログラム
US10691971B2 (en) Method and apparatus for recognizing object
Zhang et al. Age progression/regression by conditional adversarial autoencoder
US10482352B2 (en) Information processing device and non-transitory computer readable medium
JP6270182B2 (ja) 属性要因分析方法、装置、およびプログラム
CN110263880A (zh) 脑疾病分类模型的构建方法、装置及智能终端
US20250390752A1 (en) Human characteristic normalization with an autoencoder
JP7396509B2 (ja) 機械学習プログラム、機械学習方法および推定装置
Wismüller et al. Large-scale nonlinear Granger causality: A data-driven, multivariate approach to recovering directed networks from short time-series data
JP7276018B2 (ja) 学習方法、推定方法および学習プログラム
CN116152645B (zh) 一种融合多种表征平衡策略的室内场景视觉识别方法及系统
Mathew et al. Autism spectrum disorder using convolutional neural networks
US20250046456A1 (en) Systems and methods for source modality latent domain learning and few-shot domain adaptation
Anwaar et al. Face image synthesis with weight and age progression using conditional adversarial autoencoder
Yu et al. Meta-learning an in-context transformer model of human higher visual cortex
Guo et al. Age transformation based on deep learning: a survey
JP6947460B1 (ja) プログラム、情報処理装置、及び方法
JP7700863B2 (ja) 推定方法、推定装置および推定プログラム
US20230046705A1 (en) Storage medium, determination device, and determination method
JP7841381B2 (ja) 学習プログラム、識別プログラム、学習方法および識別方法
Tassopoulou et al. Deep kernel learning with temporal gaussian processes for clinical variable prediction in alzheimer’s disease
CN110991294A (zh) 一种快速构建的人脸动作单元识别方法及系统
US20200294669A1 (en) Learning method, estimating method, and storage medium

Legal Events

Date Code Title Description
A521 Request for written amendment filed

Free format text: JAPANESE INTERMEDIATE CODE: A523

Effective date: 20230220

A621 Written request for application examination

Free format text: JAPANESE INTERMEDIATE CODE: A621

Effective date: 20230220

TRDD Decision of grant or rejection written
A01 Written decision to grant a patent or to grant a registration (utility model)

Free format text: JAPANESE INTERMEDIATE CODE: A01

Effective date: 20231031

A61 First payment of annual fees (during grant procedure)

Free format text: JAPANESE INTERMEDIATE CODE: A61

Effective date: 20231113

R150 Certificate of patent or registration of utility model

Ref document number: 7396509

Country of ref document: JP

Free format text: JAPANESE INTERMEDIATE CODE: R150