Abstract
Facial expression is one of the major distracting factors for face recognition performance. Pose and illumination variations on face images also influence the performance of face recognition systems. The combination of three variations (facial expression, pose and illumination) seriously degrades the recognition accuracy. In this paper, three experimental protocols are designed in such a way that the successive performance degradation due to the increasing variations (expressions, expressions with illumination effect and expressions with illumination and pose effect) on face images can be examined. The whole experiment is carried out using North-East Indian (NEI) face images with the help of four well-known classification algorithms namely Linear Discriminant Analysis (LDA), K-Nearest Neighbor algorithm (KNN), combination of Principal Component Analysis and Linear Discriminant Analysis (PCA + LDA), combination of Principal Component Analysis and K-Nearest Neighbor algorithm (PCA + KNN). The experimental observations are analyzed through confusion matrices and graphs. This paper also describes the creation of NEI facial expression database, which contains visual static face images of different ethnic groups of the North-East states. The database is useful for future researchers in the area of forensic science, medical applications, affective computing, intelligent environments, lie detection, psychiatry, anthropology, etc.
Similar content being viewed by others
References
Bhowmik MK, Saha K, Saha P, Bhattacharjee D (2014) DeitY-TU face database: its design, multiple cameras capturing, characteristics, and evaluation. Opt Eng 53(10):102106-1–102106-24. doi:10.1117/1.OE.53.10.102106
Cao B, Shan S, Zhang X, Gao W (2004) Baseline evaluations on the CAS-PEAL-R1 face database. In: Proc. 5th Chinese Conf on Biometric Recognition, Guangzhou, China, pp. 370–378. doi: 10.1007/978-3-540-30548-4_42
Dasarathy BV (1990) Nearest neighbor (NN) norms: NN pattern classification techniques. IEEE Computer Society Press, Los Alamitos
Ekman P (1972) Universals and cultural differences in facial expressions of emotion. In: Cole J (ed) Nebraska sym. on motivation. University of Nebraska Press, Lincoln, pp 207–283
Ekman P (1993) Facial expression and emotion. Am Psychol 48:384–392
Gross R (2005) Face databases. In: Li SZ, Jain A (eds) Handbook of face recognition. Springer, New York, pp 301–328
Gross R, Matthews I, Cohn J, Kanade T, Baker S (2010) Multi-PIE. Image Vis Comput 28(5):807–813. doi:10.1016/j.imavis.2009.08.002
Langner O, Dotsch R, Bijlstra G, Wigboldus DHJ, Hawk ST, van Knippenberg A (2010) Presentation and validation ofthe Radboud faces database. Cognit Emot 24(8):1377–1388. doi:10.1080/02699930903485076
Li T, Zhu S, Ogihara M (2006) Using discriminant analysis for multi-class classification: an experimental investigation. Knowl Inf Syst 10(4):453–472. doi:10.1007/s10115-006-0013-y
Lucey P, Cohn JF, Kanade T, Saragih J, Ambadar J, Matthews I (2010) The extended Cohn-Kanade dataset (CK+): a complete dataset for action unit and emotion-specified expression. Proc. IEEE Int Conf on Computer Vision and Pattern Recognition Workshop, San Francisco
Lyons MJ, Akamatsu S, Kamachi M, Gyoban J (1998) Coding facial expressions with Gabor wavelets. Proc. 3rd IEEE Int Conf on Automatic Face and Gesture Recognition, Nara, pp 200–205
Majumder G, Debnath R, Bhowmik MK, Bhattacharjee D, Nasipuri M (2012) Image registration of North-Eastern Indian (NEI) face database. Proc 1st Int Conf on Intelligent Infrastructure, Kolkata, pp 286–290
Martinez AM, Benavente R (1998) The AR face database. CVC Technical Report #24. Computer Vision Center, Barcelona, Spain
Pantic M, Valstar M, Rademaker R, Maat L (2005) Web-based database for facial expression analysis. Proc. IEEE Int Conf on Multimedia and Expo (ICME05), Amsterdam, pp 317–321
Roh M-C, Lee S—W (2007) Performance analysis of face recognition algorithms on Korean face database. Int J Pattern Recognit Artif Intell 21(6):1017–1033. doi:10.1142/S0218001407005818
Sim T, Baker S, Bsat M (2003) The CMU pose, illumination, and expression database. IEEE Trans Pattern Anal Mach Intell 25(12):1615–1618. doi:10.1109/TPAMI.2003.1251154
Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86
Acknowledgment
The work presented here is being conducted in the Biometrics Laboratory of Tripura University, under the research project supported by the Grant No. 12(2)/2011-ESD, dated 29/03/2011, from DeitY, MCIT, Government of India. The first author is grateful to Department of Science and Technology (DST), Government of India for providing her Junior Research Fellowship-Professional (JRF-Professional) under DST INSPIRE fellowship program (No. IF131067). The authors would like to thank anonymous reviewers for their comments/suggestions to improve the quality of the paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Saha, P., Bhowmik, M.K., Bhattacharjee, D. et al. Expressions Recognition of North-East Indian (NEI) Faces. Multimed Tools Appl 75, 16781–16807 (2016). https://doi.org/10.1007/s11042-015-2945-2
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-015-2945-2