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One of the popular methods for recognition of facial expressions such as happiness, sadness and surprise is based on deformation of facial features. Motion vectors which show these deformations can be specified by the optical flow. In this method, for detecting emotions, the resulted set of motion vectors are compared with standard deformation template that caused by facial expressions. In this paper, a new method is introduced to compute the quantity of likeness in order to make decision based on the importance of obtained vectors from an optical flow approach. For finding the vectors, one of the efficient optical flow method developed by Gautama and VanHulle[17] is used. The suggested method has been examined over Cohn-Kanade AU-Coded Facial Expression Database, one of the most comprehensive collections of test images available. The experimental results show that our method could correctly recognize the facial expressions in 94% of case studies. The results also show that only a few number of image frames (three frames) are sufficient to detect facial expressions with rate of success of about 83.3%. This is a significant improvement over the available methods., {"references":["D. Darwin, \"The Expression of Emotion in Man and Animals,\" John\nMurray, 1872, reprinted by University of Chicago Press, 1965.","E.W. Young and H.D. Ellis (Eds.), \"Handbook of Research on\nProcessing,\" Elsevier Science Publishers, 1989.","Cohn-Kanade AU-Coded Facial Expression Database. Available:\nhttp://vasc.ri.cmu.edu/idb/html/face/facial_expression/","G.J. Edwards, T.F. Cootes, and C.J. Taylor, \"Face Recognition Using\nActive Appearance Models,\" Proc. European Conf. Computer Vision,\nvol. 2, pp. 581-695, 1998.","H. Hong, H. Neven, and C. von der Malsburg, \"Online Facial Expression\nRecognition Based on Personalized Galleries,\" Proc. Int'l Conf.\nAutomatic Face and Gesture Recognition, pp. 354-359, 1998.","M.J. Black and Y. Yacoob, \"Recognizing Facial Expressions in Image\nSequences Using Local Parameterized Models of Image Motion,\" lnt'l J.\nComputer Vision, vol. 25, no. 1, pp. 23-48, 1997.","J.F. Cohn, A.J. Zlochower, J.J. Lien, and T. Kanade, \"Feature-Point\nTracking by Optical Flow Discriminates Subtle Differences in Facial\nExpression,\" Proc. Int'l Conf. Automatic Face and Gesture Recognition,\npp. 396-401, 1998.","J.N. Bassili, \"Emotion recognition: The role of facial movement and the\nrelative importance of upper and lower areas of face,\" Journal of\nPersonality and Social Psychology, Vol. 37, 2049-2059, 1979.","Y. Yacoob, L. Davis, \"Recognizing human facial expressions from long\nimage sequences using optical flow,\" IEEE Trans. Pattern Anal.\nMachine Intell. 16 (6), 636-642. 1994.\n[10] M. Barlett, P. Viola, T. Sejnowski, L. Larsen, J. Hager, P. Ekman,\n\"Classifying facial action,\" In: Touretzky, D., Mozer, M., Hasselmo, M.\n(Eds.), Advances in Neural Information Processing Systems. MIT Press,\nCambridge, MA. 1996.\n[11] T. Otsuka, J. Ohya, \"Recognizing multiple persons facial expressions\nusing HMM based on automatic extraction of significant frames from\nimage sequence,\" In: Proc. Int. Conf. On Image Processing, pp. 546-\n549. 1997.\n[12] X. Chen, T, Huang, \"Facial expression recognition: A clustering-based\napproach,\" In: Pattern Recognition Letters, Elsevier Science, pp 1295-\n1302, 2003.\n[13] P. Ekman, W. Friesen, \"Unmasking the face,\" Prentice-Hall, 1975.\n[14] Y. Zhang, Q. Ji, \"Facial Expression Understanding in Image Sequences\nUsing Dynamic and Active Visual Information Fusion,\" Proceedings of\nthe Ninth IEEE International Conference on Computer Vision (ICCV\n2003), 2003.\n[15] C. Morimoto, D. Koons, A. Amir, and M. Flicker, \"Framerate pupil\ndetector and gaze tracker,\" In Proc. of the IEEE ICCV99 Frame-Rate\nWorkshop, (Kerkyra, Greece), Sep. 1999.\n[16] ]Z. Zhu, Q. Ji, K. Fujimura, and K. Lee, \"Combining Kalman filtering\nand mean shift for real time eye tracking under active IR illumination,\"\nIn Proc. Int-l Conf. Pattern Recognition, Aug. 2002.\n[17] T. Gautama, M. M. VanHulle, \"A phase-based approach to the\nestimation of the optical flow field using spatial filtering,\" IEEE Trans,\nNeural Networks, VOL. 13(5), September 2002.\n[18] J.L. Barron , D.J. Fleet, and S. Beauchemin, \"Performance of optical\nflow techniques,\" International Journal of Computer Vision, vol. 12(1),\npp. 43-77, 1994.\n[19] D.J. Fleet, and A.D. Jepson, \"Computation of Component Image\nVelocity from Local Phase Information,\" Int. J. Comput. Vision, vol.\n5(1), pp. 77-104, 1990.\n[20] B.D. Lucas, and T. Kanade, \"An Iterative Image Registration Technique\nwith an Application to Stereo Vision,\" 7th International Joint\nConference on Artificial Intelligence (IJCAI), pp. 674-679, 1981."]}

Both image steganography and image encryption have advantages and disadvantages. Steganograhy allows us to hide a desired image containing confidential information in a covered or host image while image encryption is decomposing the desired image to a non-readable, non-comprehended manner. The encryption methods are usually much more robust than the steganographic ones. However, they have a high visibility and would provoke the attackers easily since it usually is obvious from an encrypted image that something is hidden! The combination of steganography and encryption will cover both of their weaknesses and therefore, it increases the security. In this paper an image encryption method based on sinc-convolution along with using an encryption key of 128 bit length is introduced. Then, the encrypted image is covered by a host image using a modified version of JSteg steganography algorithm. This method could be applied to almost all image formats including TIF, BMP, GIF and JPEG. The experiment results show that our method is able to hide a desired image with high security and low visibility., {"references":["G. Simmons, \"The Prisoners' Problem and the Subliminal Channel\",\nCRYPTO83 Advances in Cryptology, pp. 51{67, Aug 22 -24, 1984}.","R. B. Wolfgang and E. J. Delp., \"A Watermark For Digital Images\",\nProceedings of the IEEE Inter-national Conference on Image Processing,\nLausanne, Switzerland, pp. 219-222, September 1996.","B. Schneier, \"Applied Cryptography\", New York, John Wiley & Sons,\n1996, pp. 9.","R. van Schyndel, A. Tirkel and C. Osborne, \"A Digital Watermark\",\nProceedings of the IEEE International Conference on Image Processing,\npp. 86-90, 1994.","Andreas Westfeld, Andreas Pfitzmann, \"Attacks on Steganographic\nSystem\", third international workshop, LNCS 1768, Springer-Verlag\nBerlin Heidelberg 2000. pp. 61-76.","Robert Krenn, \"Steganography and Steganalysis\", chapter 1.","A.Westfeld, \"F5ÔÇöA Steganographic Algorithm, high Capacity Despite\nBetter Steganalysis\", Technische Universit┬¿at Dresden, Institute for\nSystem Architecture, D-01062 Dresden, Germany.","Peter Wayner, \"Disappearing Cryptography\", 2ndEdition, by Elsevier\nScience (USA), 2002.","F. Stenger, A.R. Naghsh-Nilchi, J. Niebsch, R. Ramlau, \"Sampling\nMethods for Approximation Method for Approximate Solution of PDE\",\nVol. 313, pp. 199-249 of Contemporary Mathematics, American\nMathematical Society, 2002.\n[10] F. Stenger, \"Numerical Methods Based on Sinc and Analytic Functions\",\nSpringer Verlag,, 1993.\n[11] Naghsh-Nilchi, A. R., Khanche, M. E., \"Image Compression and\nEncryption using Sinc-Convolution\", Sheik Bahaee Engineering Science\nJournal, Vol. 3, No. 1, 2003 (in Farsi).\n[12] Haidari, B., \"Using 128 bit Key for Image Encryption using Sinc-\nConvolution\", M.S. thesis, Azad University of Najaf-Abad, 2005 (in\nFarsi).\n[13] G. Goth, \"Steganalysis Gets Past the Hype\", IEEE Computer Society,\nVol. 6, No. 4, April 2005\n[14] B. Koukuntla, R. Kumar, A. Lach, \"Steganography \", CSCIE2005.\n[15] Lisa M. Marvel,Charles T. Retter , \"A Methodology For Data Hiding\nUsing Images\", IEEE 1998.\n[16] N. Rasouli , \" Image Steganography with JPEG Format \" , MSc Thesis ,\nIslamic Azad University Unit of Najaf Abad, 2004."]}