Facial Expression Recognition Using PCA and Various Distance Classifiers

  • Debasmita Chakrabarti
  • Debtanu Dutta
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 298)


Information Technology is playing a very big role in today’s world. Our interaction with IT is mainly through advanced human computer interfaces. In this regard we seek to enhance the interface in a way such that it can take into account the human facial expression and respond according to a person’s feelings in a broader sense. We propose here a simple yet efficient way of facial expression recognition using Eigenspaces and dimensionality reduction techniques and multiple classifiers. It is a modified approach to the original Eigenface method by Turk and Pentland (J Cogn Neurosci, 1991) [1] for face recognition, where using the standard JAFFE database we classify each test image as belonging to one of the six basic expression classes—anger, disgust, fear, happiness, sadness or surprise. In the process we put to test four different classifiers—Euclidean distance, Manhattan distance and Cosine distance and Mahalanobis distance classifiers. In this paper we present with experimental evidence the accuracy of our method and the comparative results yielded by each of the classifiers.


Facial expression recognition Eigenspace PCA Classifier 


  1. 1.
    Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn NeurosciGoogle Scholar
  2. 2.
    Kumbhar M, Jadhav A, Patil M (2012) Facial expression recognition based on image feature. Int J Comput Commun Eng 1(2)Google Scholar
  3. 3.
    D’Mello SK, Picard RW, Graesser AC (2007) Towards an affect-sensitive autotutor. IEEE Intell Syst, Spec Issue Intell Educ Syst 22(4)Google Scholar
  4. 4.
    Murthy GRS, Jadon RS (2007) Recognizing facial expressions using eigenspaces. In: Proceedings of IEEE international conference on computational intelligence and multimedia applications, Dec 2007, Sivakasi, Tamilnadu, IndiaGoogle Scholar
  5. 5.
    Kaur M, Vashisht R, Neeru N (2010) Recognition of facial expressions with principal component analysis and singular value decomposition. Int J Comput Appl (0975–8887) 9(12):36–40Google Scholar
  6. 6.
    Lyons M, Kamachi M, Gyoba J (1997) Japanese female facial expressions (JAFFE). Database of digital imagesGoogle Scholar
  7. 7.
    Shih FY, Chuang CF, Wang PSP (2008) Performance comparisons of facial expression recognition in JAFFE database. Int J Pattern Recognit Artif Intell 22(3):445–459CrossRefGoogle Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Department of Computer ScienceSir Gurudas MahavidyalayaKolkataIndia
  2. 2.IBM India Pvt. Ltd.KolkataIndia

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