얼굴 표정인식을 위한 2D-DCT 특징추출 방법
Feature Extraction Method of 2D-DCT for Facial Expression Recognition
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초록
본 논문에서는 2D-DCT와 EHMM 알고리즘을 이용하여 과적합에 강인한 얼굴 표정인식 방법을 고안하였다. 특히, 본 논문에서는2D-DCT 특징추출을 위한 윈도우 크기를 크게 설정하여 EHMM의 관측벡터를 추출함으로써, 표정인식 성능 향상을 도모하였다. 제안 방법의 성능평가는 공인 CK 데이터베이스와 JAFFE 데이터베이스를 이용하여 수행되었고, 실험 결과로부터 특징추출 윈도우의 크기가 커질수록 표정 인식률이 향상됨을 확인하였다. 또한, CK 데이터베이스를 이용하여 표정 모델을 생성하고 JAFFE 데이터베이스 전체 샘플을 테스트한 결과, 제안 방법은 87.79%의 높은 인식률을 보였으며, 기존의 히스토그램 특징 기반의 표정인식 접근법보다 46.01∼50.05%의 향상된인식률을 보였다.
Abstract
This paper devices a facial expression recognition method robust to overfitting using 2D-DCT and EHMM algorithm. In particular,this paper achieves enhanced recognition performance by setting up a large window size for 2D-DCT feature extraction and extractingthe observation vectors of EHMM. The experimental results on the CK facial expression database and the JAFFE facial expressiondatabase showed that the facial expression recognition accuracy was improved according as window size is large. Also, the proposedmethod revealed the recognition accuracy of 87.79% and showed enhanced recognition performance ranging from 46.01% to 50.05% incomparison to previous approaches based on histogram feature, when CK database is employed for training and JAFFE database isused to test the recognition accuracy.
참고문헌 (11)
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