Editorial Team Chief Editor : Norizan Mohamad

Editors : Nazatul Azleen Zainal Abidin Mohd Hanapi Abdul Latiff Siti ‘Aisyah Sa’dan

Vol.: 1, Issue: June, 2015A publication by members of Computer Science Department, Faculty of Computer and Mathematical Sciences, UiTM Terengganu

BEYOND WINDOWS

by: Norizan binti Mohamad

Bulletin

Pupil Detection using Gradient-based Edge Detection Technique and Circular Hough Transform

Facial Analysis

The first step in facial analysis is to detect faces in the image. In this work, the focus is on detecting frontal faces following the human expert’s recommendations. For this purpose, Viola & Jones (2004) face detector was employed since the algorithm has been widely adopted due to its robustness. It has been shown that the detec-tion rate of this algorithm is highly satisfactory (Rah-man, Ren, Kehtarnavaz, & Leonardo, 2009) and that the face detector performs reasonably good for frontal faces (Mohamad, Annamalai, & Salleh, 2010b). The outcome captures the locations, sizes and number of dominant face regions detected. The outcome of face regions is de-picted in Figure 1. However, due to its equal number of pixels in both width and length, the size ratio is always 1 for all the detected face regions. As a result, a method that enables us to obtain the actual face width and height automatically is required.

Figure 1: Detected face region

As an alternative, a skin filter has been considered that separates the skin likelihood region from the rest of the regions. A sample of skin regions is shown in Figure 2. However the drawback is that the face region may include the neck or other surrounding objects with similar skin likelihood.

Figure 2: Detected skin region

Introduction Faces provide valuable information compared to oth-er visual objects in an image. Hence, automatic facial analysis is crucial and often employed in the preprocess-ing steps in many computer vision research. The results from the face analysis are very useful in applications such as in person identification, face expression and face recognition applications. Another important and use-ful application is key frame selection in video domain where a key frame containing person of interest is se-lected based on specific facial characteristics. The key frame acts as a pointer that searches the person of inter-est to support efficient visual content retrieval.

Facial analysis involves extracting valuable informa-tion from face images such as its position in the im-age, its characteristics and expressions. In (Mohamad, Annamalai, & Salleh, 2010a), a set of facial characteris-tics were obtained from human experts in the field of broadcasting and photography. The human experts were asked to select the most desirable person image(s) from a sequence of images and describe the characteristics of their selection.

There are two important facial characteristics obtained from the previous study. First, the face must have an ap-propriate size. Second it also needs an appropriate scale. Size is defined as the facial width and height while scale is defined as the proportion of the face to the whole im-age. For the facial size, the constraint is set to 1.6 follow-ing the golden ratio of an ideal facial size (DeBruine, 2009).

The scale, on the other hand, is set to be within one tenth of the image height. Faces below these constraints will be avoided. For both size and scale characteristics, the facial width and height must be known before the face evaluation against the constraints can be made.

 

 

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Next, we consider pupil detection as a method that allows us to obtain facial width and height. Follow-ing (Szlávik & Szirányi, 2004), given the distance between two eyes as D, the face width could be cal-culated as 1.8 times D. Figure 3 shows the geometric face model used for the measurement.

Figure 3: The geometric face model as in (Szlávik & Szirányi, 2004)

Using the geometric face model, the height of the face can be determined by obtaining the height of the visible pupil or eyeball. As for the height of the pupil, it should be one-fourteenth the height of the face (Patnaik, Rajan, & Sanju, 2003) (as shown in Fig-ure 4).

Figure 4: The geometric face model as in (Patnaik et al., 2003)

Edge Detection

Edge plays an important role in an image since it forms the outline of an object. An edge detector provides clues to identify and separate regions with-in objects in the image. If the edges in an image can be identified accurately, all of the objects can be located and basic properties such as area, perimeter and shape can be measured (Nadernejad, 2008).

With its ability to characterize boundaries, an edge detector filters out useless data, noise and frequen-cies while preserving the important structural prop-erties in an image(Juneja & Sandhu, 2009). In addi-tion, edges have important information about the image content(Barkhoda, Tab, & Shahryari, 2009) and thus help in extracting useful information

characteristics of the image (Folorunso, Vincent, & Dansu, 2007). Applying an edge detector to an im-age may significantly reduce the amount of data to be processed and filter out less relevant data.

Most common methods to perform edge detection fall into two categories i.e. gradient-based and lapla-cian-based edge detection (Juneja & Sandhu, 2009). Gradient-based methods detect edges by looking for the maximum and minimum in the first derivative of the image. The laplacian-based method searches for zero crossings in the second derivative of the im-age to find edges. An edge has the one-dimensional shape of a slope and calculating the derivative of the image can highlight its location. A pixel loca-tion is declared an edge location if the value of the gradient exceeds some threshold.

Gradient-based method determines the level of var-iance between different pixels. The edge-detection operator is calculated by forming a matrix centered on a pixel chosen as the center of the matrix area. If the value of this matrix area is above a given thresh-old, then the middle pixel is classified as edge. Ex-amples of gradient-based edge detectors are Rob-erts, Prewitt and Sobel operators.

The purpose of employing the edge detector is to prepare the image for further analysis and imple-mentation. During an evaluation of five most com-monly used edge detectors (Sobel, LoG, Canny, Rothwell, and Edison), (SongWang, Ge, & Liu, 2006) showed that the performance of these edge detec-tors is very similar.

In (Juneja & Sandhu, 2009), the Canny’s edge detec-tion algorithm has been shown to perform better than Prewitt, Sobel, Roberts and Laplacian of Gauss-ian method. The best detector was judged by study-ing the edge maps relative to each other through statistical evaluation.

Pupil Detection Process

The results after applying each method are shown next.

 

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Figure 5: During edge detection task

Figure 6: During Hough Transform .. to be continued.

Original image (Caltech database)(Fei-Fei, Fergus, & Perona., 2004)

Sobel edge detector

Laplacian edge detector

Canny edge detector

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References

Barkhoda, W., Tab, F. A., & Shahryari, O.-K. (2009). Fuzzy Edge Detection Based on Pixel’s Gradient and Standard Deviation Values. Paper presented at the Proceed-ings of the International MultiConference on Computer Science and Information Technology. DeBruine, L. (2009). 5 Elements of Attractiveness. The Oprah Winfrey Show. http://www.oprah.com/relationships/5-Elements-of-Attractiveness-The-Science-of-Sex-Appeal/3 Fei-Fei, L., Fergus, R., & Perona., P. (2004). Learning Generative Visual Models from Few Training Examples: an Incremental Bayesian Approach Tested on 101 Object Categories. . IEEE CVPR 2004, Workshop on Generative-Model Based Vision. Folorunso, O., Vincent, O. R., & Dansu, B. M. (2007). Image Edge Detection: A Knowledge Management Technique for Visual Scene Analysis. Information Man-agement and Computer Security, 15(1), 23-32. Juneja, M., & Sandhu, P. S. (2009). Performance Evaluation of Edge Detection Tech-niques for Images in Spatial Domain. International Journal of Computer Theory and Engineering, 1, No. 5, 614-621. Mohamad, N., Annamalai, M., & Salleh, S. S. (2010a). Acquiring Attribute Filters for Image Processing Through Iterative QDA Approach. Unpublished Work. Mohamad, N., Annamalai, M., & Salleh, S. S. (2010b). Determining the Fittest Frontal View Face AdaBoost Classifier for Adoption in Personage Detector. 4th In-ternational Symposium on Information Technology (ITSim ‘10). Nadernejad, E. (2008). Edge Detection Techniques: Evaluations and Comparisons. Applied Mathematical Sciences, 2, No. 31, 1507-1520. Patnaik, V. V. G., Rajan, K. S., & Sanju, B. (2003). Anatomy of ‘A Beautiful Face & Smile’. J Anat. Soc. India, 52(1), 74-80. Rahman, M., Ren, J., Kehtarnavaz, N., & Leonardo, E. (2009). Hybrid Real-Time Face Detection for Mobile Devices. International Conference on Image Processing (ICIP’09). SongWang, Ge, F., & Liu, T. (2006). Evaluating Edge Detection through Boundary Detection EURASIP Journal on Applied Signal Processing (pp. 1-15): Hindawi Pub-lishing Corporation. Szlávik, Z., & Szirányi, T. (2004). Face Analysis Using CNN-UM. Paper presented at the Proceedings IEEE International Workshop on Cellular Neural Networks and their Applications (CNNA 2004), Budapest, Hungary. Viola, P., & Jones, M. (2004). Robust Real-Time Face Detection International Jour-nal of Computer Vision (Vol. 57 (2), pp. 137-154): Kluwer Academic Publishers.

Referencehttp://www.codethinked.com/The-Programmer-Dress-Code

We welcome any feedback, suggestions, questions or comments from the readers. Email them to: norizanm@tganu.uitm.edu.my

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Grady Booch - invented UML *Unified Modeling Language)

Alan Kay- pioneer in object-ori-ented programming