NDLI: A Mixed Classifier Based on Combination of HMM and KNN

Content Provider	IEEE Xplore Digital Library
Author	Qingmiao Wang Shiguang Ju
Copyright Year	2008
Description	Author affiliation: Sch. of Comput. Sci. & Commun. Eng., Jiangsu Univ., Zhenjiang (Qingmiao Wang; Shiguang Ju)
Abstract	Facial expression is an important communication method. Facial expression recognition has been studied in many application domains. In this paper, we study hidden Markov model (HMM) and K nearest neighbor (KNN) classifiers, and put forward a combined approach for facial expression recognition. The basic idea of this approach is to employ the HMM and KNN classifiers in a sequential way. First, the HMM classifier is used to calculate the probabilities of six expressions. From two most possible results of classification by HMM, the KNN classifier is used to make a final decision while the difference between the maximum probability and the second is less than the threshold obtained from HMM and training samples. The experiments show that the performance of this method exceeds that of solely HMM-based or KNN-based method.
Starting Page	38
Ending Page	42
File Size	363365
Page Count	5
File Format	PDF
ISBN	9780769533049
DOI	10.1109/ICNC.2008.680
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face recognition Biological system modeling Humans Artificial neural networks Probability Fingerprint recognition HMM KNN Mixed Classifier Nearest neighbor searches Support vector machines Hidden Markov models Support vector machine classification Facial Expression Recognition
Content Type	Text
Resource Type	Article

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