Estimating facial angles using Radon transform

Analysis of human facial images has become increasingly important due to its numerous applications. In this regard, extracting facial parameters is vital and various studies have been done in this field. Using these methods, different facial parameters such as the location of eyebrows, eye length, and nose angles are extracted. In this article, a robust and automatic method is introduced for determining facial angles from profile view images using the Radon transform. The Radon transform is a type of linear integration along a specific direction and angles play an important role in its performance. The proposed algorithm not only has good precision, but also efficient performance. The precision of this method in angle measurements is 98.51% compared with manual measurement. Compared to various other angle estimation algorithms, the proposed method has higher efficiency.

Anahtar Kelimeler:

Radon transform, sinogram, facial angles, facial color image, profile view

Estimating facial angles using Radon transform

Keywords:

Radon transform, sinogram, facial angles, facial color image, profile view,

PDF

___

Ozkul T, Ozkul MH. Computer simulation tool for rhinoplasty planning. Comput Biol Med 2004; 34: 697–718.
Leong SCL, White PS. A comparison of aesthetic proportions between the healthy Caucasian nose and the aesthetic ideal. J Plast Reconstr Aes 2006; 59: 248–252.
Annino DJ. Chin augmentation and genioplasty. Operative Techniques in Otolaryngology 1999; 10: 224–227.
Zheng L, Shi D. Advanced Radon transform using generalized interpolated Fourier method for straight line detection. Comput Vis Image Und 2011; 115: 152–160.
Magli E, Olmo G, Lo Presti L. Pattern recognition by means of the Radon transform and the continuous wavelet transform. Signal Process 1999; 73: 277–289.
Jadhav DV, Holambe RS. Feature extraction using Radon and wavelet transforms with application to face recogni- tion. Neurocomputing 2009; 72: 1951–1959.
Jadhav DV, Holambe RS. Radon and discrete cosine transforms based feature extraction and dimensionality reduction approach for face recognition. Signal Process 2008; 88: 2604–2609.
Jadhav DV, Holambe RS. Rotation, illumination invariant polynomial kernel Fisher discriminant analysis using Radon and discrete cosine transforms based features for face recognition. Pattern Recogn Lett 2010; 31: 1002–1009.
Lin CT, Fan KW, Yeh CM, Pu HC, Wu FY. High-accuracy skew estimation of document images. Int J Fuzzy Syst 2006; 8: 119–126.
Kavallieratou E, Fakotakis N, Kokkinakis G. Skew angle estimation for printed and handwritten documents using the Wigner-Ville distribution. Image Vision Comput 2002; 20: 813–824.
Liang ZP, Lauterbur PC. Principles of Magnetic Resonance Imaging: A Signal Processing Perspective. New York, NY, USA: Wiley-IEEE Press, 1999.
Fariaby J, Hossini A, Saffari E. Photographic analysis of faces of 20-year-old students in Iran. Brit J Oral Max Surg 2006; 44: 393–396.
Kass M, Witkin A, Terzopoulos D. Snakes: Active contour models. Int J Comput Vision 1987; 1: 321–331.
Perlibakas V. Automatical detection of face features and exact face contour. Pattern Recogn Lett 2003; 24: 2977– 29
Yow KC, Cipolla R. Enhancing human face detection using motion and active contours. In: 3rd Asian Conference on Computer Vision; 8–10 January 1998; Hong Kong, Vol. 1. London, UK: Springer-Verlag. pp. 515–522.
Sathya PD, Kayalvizhi R. Modified bacterial foraging algorithm based multilevel thresholding for image segmenta- tion. Eng Appl Artif Intel 2011; 24: 595–615.