Comparison of Filtering Methods for Extracting Transient Facial Wrinkle Features

Rosdiyana Samad, Phua Jin Hoe, Mahfuzah Mustafa, Nor Rul Hasma Abdullah, Dwi Pebrianti, Nurul Hazlina Noordin


Facial local features comprise an essential information to identify a personal characteristic such as age, gender, identity and expression. One of the facial local features is a wrinkle. Wrinkle is a small furrow or crease in the skin. Recently, wrinkle detection has become a topic of interest in computer vision, where many researchers developed applications like age estimation, face detection, expression recognition, facial digital beauty and etc. However, most of the research focused on permanent wrinkles instead of transient wrinkles. Transient wrinkle can be seen during the movement of facial muscle such as a facial expression. This paper presents a comparison of filtering method for extracting transient wrinkles features. The filters that have been selected are Gabor wavelet and Kirsch operator. The extracted features are the number of wrinkles, the maximum perimeter of wrinkle, the average perimeter of wrinkle, total perimeter of wrinkle, the maximum area of the wrinkle, and the total area of the wrinkle. A total of 60 sets of data extracted from Cohn-Kanade database, images from internet and self-images. These images contain weak and strong transient wrinkles at forehead region. Features selection and analysis has been done to select which feature extraction method produces better wrinkle features that can be used for the classification of wrinkle detection system. The results show that both Gabor and Kirsch methods are successful to extract transient wrinkle features, where both methods scored 100% accuracy in the classification with SVM. However, Gabor method is slightly better than Kirsch method in term of detecting weak wrinkles. The Kirsch method requires an additional noise filtering method to eliminate noise particles after the convolution of Kirsch’s kernel. In conclusion, Gabor method is more applicable to a variety of applications than Kirsch method.


Gabor Wavelet; Feature Extraction; Kirsh Operator; Transient Wrinkles;

Full Text:



W. Xie, L. Shen, and J. Jiang, “A novel transient wrinkle detection algorithm and its application for expression synthesis,” IEEE Transactions on Multimedia, vol. 19, 2017, pp. 279-292.

F. Tsalakanidou and S. Malassiotis. “Real-time 2D+ 3D facial action and expression recognition,” Pattern Recognition, vol. 43, 2010, pp. 1763-1775.

S. E. Choi, Y. J. Lee, S. J. Lee, K. R. Park and J. Kim, “Age estimation using a hierarchical classifier based on global and local facial features,” Pattern Recognition, vol. 44, 2011, pp. 1262-1281.

J. Hayashi, M. Yasumoto, H. Ito, and H. Koshimizu, “Age and gender estimation based on wrinkle texture and color of facial images,” in Proceeding of 16th International Conference on Pattern Recognition, 2002, pp. 405-408.

C.-C. Ng, M. H. Yap, N. Costen, and B. Li, “Wrinkle detection using hessian line tracking,” IEEE Access, vol. 3, 2015, pp. 1079-1088.

C.-C. Ng, M. H. Yap, N. Costen, and B. Li, “Automatic wrinkle detection using hybrid Hessian filter,” in Proceeding of Asian Conference on Computer Vision, 2014, pp. 609-622.

C.-C. Ng, M. H. Yap, N. Costen, and B. Li, “An investigation on local wrinkle-based extractor of age estimation,” in Proceeding of International Conference on Computer Vision Theory and Applications (VISAPP), 2014, pp. 675-681.

W. Zhao, J. S. Park, and S. W. Lee, “Fully automatic face detection and facial feature points extraction using local Gabor filter bank and PCA,” in Proceeding of International Conference on Machine Learning and Cybernetics, 2011, pp. 1789-1792.

R. Samad and H. Sawada, “Edge-Based Facial Feature Extraction using Gabor Wavelet and Convolution Filters,” in Proceedings of the 12th IAPR Conference on Machine Vision Application (MVA2011), 2011, pp. 430-433.

F. Tsalakanidou and S. Malassiotis, “Real-time 2D+ 3D facial action and expression recognition,” Pattern Recognition, vol. 43, 2010, pp. 1763-1775.

R. A. Kirsch, “Computer determination of the constituent structure of biological images,” Comput. Biomed. Res., vol. 4, no. 3, 1971, pp. 315–328.

P. Lucey, J. F. Cohn, T. Kanade, J. Saragih, Z. Ambadar, and I. Matthews, “The extended Cohn-Kanade dataset: A complete dataset for action unit and emotion-specified expression,” in Proceeding of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2010, pp. 94-101.

Bradski, G. and Kaehler, A., “Learning OpenCV 3,” O’Reilly Media, Inc. Sebastopol, California, USA. 2016.

Bradski, G. and Kaehler, A., “Learning OpenCV: Computer vision with the OpenCV library,” 1st Ed. O'Reilly. Sebastopol, California, USA. 2008.

H. Kekre and S. Gharge, “Image segmentation using extended edge operator for mammographic images,” International Journal on Computer Science and Engineering, vol. 2, no. 4, 2010, pp. 1086-109.

R. C. Gonzalez, R. E. Woods and S. L. Eddins, “Digital image processing using MATLAB,” Pearson Prentice Hall, USA. 2004.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

ISSN: 2180-1843

eISSN: 2289-8131