@article{MAKHILLJEAS201813916070,
    title = {Partition Based Feature Extraction Technique for Facial Expression
Recognition Using Multi-Stage Hidden Markov Model},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {9},
    pages = {2651-2658},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.2651.2658},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.2651.2658},
    author = {Mayur,Narendra and},
    keywords = {JAFFE,Gabor wavelets transform,PCA,local binary patterns,SVM,FAPs,Cohn-Kanade database,Markov process,static modelling},
    abstract = {Partition based feature extraction is widely used in the pattern recognition and computer vision. This
method is robust to some changes like occlusion, background, etc. In this study, partition based technique is
used for feature extraction and extension of HMM is used as a classifier. The new introduced multi-stage HMM
consists of two layers. In which bottom layer represents the atomic expression made by eyes, nose and lips.
Further upper layer represents the combination of these atomic expressions such as smile, fear, etc. Six basic
facial expressions are recognised, i.e., anger, disgust, fear, joy, sadness and surprise. Our proposed system is
able to get overall accuracy of 82% using JAFFE database.}
    }