지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2013.12
- 수록면
- 266 - 272 (7page)
이용수
초록· 키워드
오류제보하기
Facial feature extraction and tracking are essential steps in human-robot-interaction (HRI) field such as face recognition, gaze estimation, and emotion recognition. Active shape model (ASM) is one of the successful generative models that extract the facial features. However, applying only ASM is not adequate for modeling a face in actual applications, because positions of facial features are unstably extracted due to limitation of the number of iterations in the ASM fitting algorithm. The unaccurate positions of facial features decrease the performance of the emotion recognition.
In this paper, we propose real-time facial feature extraction and tracking framework using ASM and LK optical flow for emotion recognition. LK optical flow is desirable to estimate time-varying geometric parameters in sequential face images. In addition, we introduce a straightforward method to avoid tracking failure caused by partial occlusions that can be a serious problem for tracking based algorithm. Emotion recognition experiments with k-NN and SVM classifier shows over 95% classification accuracy for three emotions: "joy", "anger", and "disgust".
In this paper, we propose real-time facial feature extraction and tracking framework using ASM and LK optical flow for emotion recognition. LK optical flow is desirable to estimate time-varying geometric parameters in sequential face images. In addition, we introduce a straightforward method to avoid tracking failure caused by partial occlusions that can be a serious problem for tracking based algorithm. Emotion recognition experiments with k-NN and SVM classifier shows over 95% classification accuracy for three emotions: "joy", "anger", and "disgust".
목차
Abstract
1. 서론
2. 관련 연구
3. 얼굴 특징점 추적 프레임워크와 실험구성
4. 실험 결과
5. 결론
References
참고문헌 (12)
참고문헌 신청
1. [학술지(정기간행물)] - T. F. Cootes / 1995 / Active Shape Models-Their Training and Application / Computer Vision Image Understand 61 (9) : 38 ~ 59
2. [학술지(정기간행물)] - T. F. Cootes / 2001 / Active appearance models / IEEE Transactions on Pattern Analysis and Machine Intelligence 23 (6) : 681 ~ 685
3. [학술대회논문] - M. Zhou / 2010 / AAM based Face Tracking with Temporal Matching and Face Segmentation / IEEE Conference on Computer Vision and Pattern Recognition : 701 ~ 708
4. [학술지(정기간행물)] - J. K. Kearney / 1987 / Optical Flow Estimation : An Error Analysis of Gradient-based Methods with Local Optimization / IEEE Transactions on Pattern Analysis and Machine Intelligence 9 (2) : 229 ~ 244
5. [학술대회논문] - M. Stephen / 2008 / Locating facial features with an extended active shape model / Proceedings of the 10th European Conference on Computer Vision 4 : 504 ~ 513
2. [학술지(정기간행물)] - T. F. Cootes / 2001 / Active appearance models / IEEE Transactions on Pattern Analysis and Machine Intelligence 23 (6) : 681 ~ 685
3. [학술대회논문] - M. Zhou / 2010 / AAM based Face Tracking with Temporal Matching and Face Segmentation / IEEE Conference on Computer Vision and Pattern Recognition : 701 ~ 708
4. [학술지(정기간행물)] - J. K. Kearney / 1987 / Optical Flow Estimation : An Error Analysis of Gradient-based Methods with Local Optimization / IEEE Transactions on Pattern Analysis and Machine Intelligence 9 (2) : 229 ~ 244
5. [학술대회논문] - M. Stephen / 2008 / Locating facial features with an extended active shape model / Proceedings of the 10th European Conference on Computer Vision 4 : 504 ~ 513
이 논문의 저자 정보
이 논문과 함께 이용한 논문
음성 신호와 얼굴 표정을 이용한 감정인식 및 표현 기법
주종태
,
문병현
,
서상욱
외 2명
한국지능시스템학회 학술발표 논문집
2007
.04
얼굴표정을 이용한 감정인식 및 표현 기법
주종태
,
박경진
,
고광은
외 2명
한국지능시스템학회 학술발표 논문집
2007
.04
모빌리티 기반 로봇의 감정표현 방법에 대한 탐색
한훈
,
정주영
대한기계학회 춘추학술대회
2022
.11
음성감정 신호로부터 전이학습기반 딥러닝을 이용한 감정인식
조아현
,
곽근창
대한전기학회 학술대회 논문집
2021
.11
감정 적응을 이용한 감정 인식 학습 방법
김원구
전기전자학회논문지
2020
.12
최근 본 자료
댓글(0)
0
UCI(KEPA) : I410-ECN-0101-2020-559-001258471