Enhancement of the Adaptive Shape Variants Average Values by Using Eight Movement Directions for Multi-Features Detection of Facial Sketch
This paper aims to detect multi features of a facial sketch by using a novel approach. The detection of multi features of facial sketch has been conducted by several researchers, but they mainly considered frontal face sketches as object samples. In fact, the detection of multi features of facial sketch with certain angle is very important to assist police for describing the criminal’s face, when criminal’s face only appears on certain angle. Integration of the maximum line gradient value enhancement and the level set methods was implemented to detect facial features sketches with tilt angle to 15 degrees. However, these methods tend to move towards non features when there are a lot of graffiti around the shape. To overcome this weakness, the author proposes a novel approach to move the shape by adding a parameter to control the movement based on enhancement of the adaptive shape variants average values with 8 movement directions. The experimental results show that the proposed method can improve the detection accuracy up to 92.74%.
Muntasa, Arif, Hariadi, Mochamad & Purnomo, Mauridhi Hery, Maximum Feature Value Selection of Nonlinear Function Based on Kernel Pca for Face Recognition, Proceeding of The 4th Conference on Information & Communication Technology and Systems, Surabaya, Indonesia, pp. 397-402, 2008.
Wright, John, Yang, Allen Y., Ganesh, Arvind, Sastry, S. Shankar & Ma, Yi, Robust Face Recognition Via Sparse Representation, IEEE Transactions On Pattern Analysis And Machine Intelligence, 31(2), pp 210-227, 2009.
Sinha, P., Balas, B., Ostrovsky, Y. & Russell, R., Face Recognition by Humans: Nineteen Results All Computer Vision Researchers Should Know About, Proc. IEEE, 94(11), pp. 1948-1962, 2006.
Liu, C., Capitalize on Dimensionality Increasing Techniques for Improving Face Recognition Grand Challenge Performance, IEEE Trans. Pattern Analysis and Machine Intelligence, 28(5), pp. 725-737, 2006.
Ahonen, T., Hadid, A. & Pietikainen, M., Face Description with Local Binary Patterns: Application to Face Recognition, IEEE Trans. Pattern Analysis and Machine Intelligence, 28(12), pp. 2037-2041, 2006.
Muntasa, Arif, Hariadi, Mochamad & Purnomo, Mauridhi Hery, A New Formulation of Face Sketch Multiple Features Detection Using Pyramid Parameter Model and Simultaneously Landmark Movement, IJCSNS International Journal of Computer Science and Network Security, 9(9), pp 249-260, 2009.
Muntasa, Arif, Integrating the Maximum Line Gradient Value Improvement and the Level Set Method Variation to Detect Face Sketch Multi-Features, International Journal of Digital Image Processing, 3(7), 2010.
Hidaka, A., Nishida, K. & Kurita, T., Face Tracking by Maximizing Classification Score of Face Detector Based on Rectangle Features, Proc. IEEE Int’l Conf. Computer Vision Systems, 2006.
Liu, Xiaoming, Discriminative Face Alignment, IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(11), pp. 1941-1953, 2009.
Muntasa, Arif, A Novel Approach for Face Sketch Recognition Based on the First Derivative Negative and 2D-DCT with Overlapping Model, International Journal of Image Processing (IJIP), 4(4), pp. 368-376, 2010.
Wang, Xiaogang & Tang, Xiaoou, Face Photo-Sketch Synthesis And Recognition, IEEE Transactions On Pattern Analysis And Machine Intelligence, 31(11), pp. 1955-1967, 2010.
Zhong. J., Gao, X. & Tian, C., Face Sketch Synthesis Using a E-Hmm and Selective Ensemble, Proc. IEEE Int’l Conf. Acoustics, Speech, and Signal Processing, 2007.
Tang, Xiaoou & Wang, Xiaogang, Face Sketch Recognition, IEEE Transactions on Circuits and Systems for Video Technology, 14(1), pp. 50-57, 2004.
Cristinacce, D. & Cootes, T.F., Facial Feature Detection and Tracking with Automatic Template Selection, Proc. 7th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 429-434, 2006.
Cristinacce, D. & Cootes, T.F., Feature Detection and Tracking with Constrained Local Models, Proc. British Machine Vision Conference, 3, pp.929-938, 2006.
Cristinacce, D. & Cootes, T.F., Boosted Active Shape Models, Proc. British Machine Vision Conference, 2, pp.880-889, 2007.
Bagherian, E., Rahmat, R.W. & Udzir, N.I., Extract of Facial Feature Point, IJCSNS International Journal of Computer Science and Network Security, 9(1), pp. 49-53, 2009.
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