Review on Human face and Expression Recognition

  • Dr.Dolly Reney Christian College of Engineering & Technology, Bhilai, India
  • Neeta Tripathi Shri Shankaracharya Institute of Engineering & Technology Bhilai, India
Keywords: Facial Expression Recognition, Face Detection, Emotion Detection, human voice.

Abstract

There are the different popular algorithms and techniques available which are used for implementation of face and expression recognition all having respective advantages and disadvantages. Some of the algorithms improve the efficiency of face and expression recognition, under the different varying illumination and expression conditions for input source. The main steps for face recognition are Feature representation and classification. The different authors have described different novel approaches for face and emotion recognition. Present review paper discrebie the different methods and techniques used to identified the person with the facial expression and person emotion with the voice of the person.

References

[1] Bruner, I. S. and Tagiuri, R., 1954. The perception of people. In Handbook of Social Psychology, 2, G. Lindzey, Ed., Addison-Wesley, Reading, MA, 634–654.
[2] Bledsoe, W. W., 1964. The model method in facial recognition. Tech. rep. PRI: 15, Panoramic research Inc., Palo Alto, CA.
[3] Ekman, P. Ed., 1998. Charles Darwin’s The Expression of the Emotions in Man and Animals, Third Edition, with Introduction, Afterwords and Commentaries by Paul Ekman. Harper- Collins/Oxford University Press, New York, NY/London, U.K.
[4] Kelly, M. D., 1970 Visual identification of people by computer. Tech. rep. AI-130, Stanford AI Project, Stanford, CA.
[5] Chellapa, R., Wilson, C. L., and Sirohey, S. 1995 Human and machine recognition of faces: A survey. Proc. IEEE, 83, 705–740.
[6] Nicu, S., Michael, S., Lew, Cohen, I. Garg, A. Huang, T.S., 2002. Emotion recognition using a Cauchy naïve bayes classifier, ICPR,
[7] Cohen, I., Nicu, S., Chen, L., Garg, A., Huang, T., Facial Expression Recognition from Video Sequences: Temporal and Static modeling Computer Vision and Image Understanding (CVIU) special issue on face recognition.
[8] Scherer, K. R., 2007. Component models of emotion can inform the quest for emotional competence. In G. Matthews, M. Zeidner, & R. D. Roberts (Eds.), The science of emotional intelligence: Knowns and unknowns (101–126). New York, NY: Oxford University Press.
[9] Matthews, G., Zeidner, M., and Roberts, R. D., (Eds.), 2007. The science of emotional intelligence: Knowns and unknowns. New York, NY: Oxford University Press.
[10] Joseph, D. L., and Newman, D. A., 2010. Emotional intelligence: An integrative meta-analysis and cascading model. Journal of Applied Psychology, 95, 54–78.
[11] Chavan, P. M., Jadhav, M. C., Mashruwala, J. B., Nehete, A. K., Panjari, P. A., 2013. Real Time Emotion Recognition through Facial Expressions for Desktop Devices, International Journal of Emerging Science and Engineering, 1(7).
[12] Turk, M. and Pentland, A.,1991. Eigenfaces for Recognition, J. Cognitive Neuroscience, 3(1), 71-86.
[13] Kirby, M. and Sirovich, L. 1990. Application of the Karhunen- Loève procedure for the characterisation of human faces, IEEE Trans. Pattern Analysis and Machine Intelligence, 12, 831-835.
[14] R. A. Fisher, 1936. The Use of Multiple Measurements in Taxonomic Problems, 7 (2), 179-188.
[15] W. Zheng, C Zou, and L. Zhao, 2004. Real-time face recognition using Gram-Schmidt orthogonalization for LDA, ICPR’04, Cambridge UK, 403-406.
[16] Wang, J., Cheng, J., 2010. Face Recognition Based On Fusion Of Gabor And 2DPCA Features", in International Symposium on Intelligent Signal Processing and Communication Systems, 1-4.
[17] Nicolas Morizet, Thomas EA, Florence Rossant, 2007. Frederic Amiel, Amara Amara, Revue des algorithmes PCA, LDA et EBGM utilises en reconnaissance 2D du visage pour la biometrie Institut Superieur d'electronique de Paris (ISEP), Departement d'electronique.
[18] Feng Jiao, Wen Giao, Lijuan Duan, and Guoqin Cui, 2001 "Detecting adult image using multiple features", In IEEE conference Infotech and Info-net, 378 - 383.
[19] Joachims, T., 1998. Making Large-Scale SVM Learning Practical. LS8- Report, University of Dortmund, LS
[20] Vladimir, V.N., 1995. The Nature of Statistical Learning Theory. Springer, Berlin Heidelberg New York.
[21] Yang, M. H. Kriegman, D. J. and Ahuja, N. 2002. Detecting Faces in Images: A Survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(1).
[22] Kim, S., Georgiou, P., Lee, Narayanan, S., 2007. Real-time emotion detection system using speech: Multi-modal fusion of different timescale features, Proc. of IEEE Multimedia Signal Processing Workshop, Greece. Page 48-51.
[23] Busso, C., Lee S., and Narayanan, S., 2009. Analysis of Emotionally Salient Aspects of Fundamental Frequency for Emotion Detection, IEEE Trans. on Audio, Speech and Language processing, 17(4), 582-596.
[24] Navas, E., Hernáez, I., Luengo, I., 2006. An Objective and Subjective Study of the Role of Semantics and Prosodic Features in Building Corpora for Emotional TTS”, IEEE Trans. on Audio, Speech and Language Processing, 14 (4),490-501.
[25] Tripathi, N., Zadgaonkar, A. S. and Shukla, A., 2005. Emotional Effect on Prosodic Parameter, Journal of Acoustics Society of India (ISSN No. 0973-3302), 33, 368-370.
[26] Luengo, I., Navas, E., Hernáez, I., 2010. Feature Analysis and Evaluation for Automatic Emotion Identification in Speech, IEEE Trans. on Multimedia, 12 (6), 1117-1127.
[27] G. Zhou, J. L. Hansen, and J. F. Kaiser, 1999 “Methods for Stress Classification: Nonlinear Teo and Linear Speech Based Features, Proc. IEEE Int’l Conf. Acoustics and Signal Processing, pp. 2087-2090,.
[28] Christiansen, T. U. and Greenberg, S., 1999. Distinguishing Spectral and Temporal Properties of Speech Using an Information-Theoretic Approach, Proc. IEEE Int’l Conf. Acoustics and Signal Processing, pp. 2087-2090.
[29] Krishnamoorty, P. and Mahadeva, S. R. P., 2009. Temporal and spectral processing methods for processing of degraded speech: A Review, IETE Technical Review, 26(2), 137-148.
[30] Ghosh, P., Sarkar, K. A. and Sreenivas, T. V., 2007. ALCR and ESTL: Novel Temporal Features and their Application to Speech Segmentation, International Conference on Acoustics, Speech, and Signal Processing - ICASSP, 4, pp. IV-1065-IV-1068,.
[31] Farooq, O., and Datta, S., 2001. Mel Filter-Like Admissible Wavelet Packet Structure for Speech Recognition, IEEE Signal Processing Letters, 8(7), 196-198.
[32] Yao, J., Zhang, Y.T., 2001. Bionic Wavelet Transform: A New Time–Frequency Method Based on an Auditory”, IEEE Trans. on Biomedical Engineering, 48(8), 856-863.
[33] Koolagudi, S. G., Rao, S. K., 2012. Emotion recognition from speech: a review”, Intl. Journal of Speech Technology, 15, 99-117.
[34] Lawrence, S., Giles, C. L., Tsoi, Ah C., Back, A. D., 1997. Face Recognition: A Convolutional Neural Network Approach” at IEEE Transactions on Neural Networks, Special Issue on Neural Networks and Pattern Recognition.
[35] Beumer, G.M., Tao, Q., Bazen, A.M., Veldhuis, R.N.J., 2006. A landmark paper in face recognition at 7th International Conference on Automatic Face and Gesture Recognition, FGR, IEEE Computer Society Press,(2), 73- 78 Print ISBN: 0-7695-2503-2.
[36] Bellakhdhar, F., Loukil, K., 2013 “Face recognition approach using Gabor Wavelets, PCA and SVM” at IJCSI International Journal of Computer Science 10(2:3).
Published
2019-02-18
How to Cite
Reney, D., & Tripathi, N. (2019). Review on Human face and Expression Recognition. CSVTU International Journal of Biotechnology, Bioinformatics and Biomedical, 3(3), 31-40. https://doi.org/https://doi.org/10.30732/ijbbb.20180303001
Section
Review Article