1. Seunghui Cha1, Wookhyun Kim1
Advanced Science and Technology Letters Vol.92 (Education 2015), pp.72-76
Analyze the learner's concentration using detection of facial feature points
Seunghui Cha1, Wookhyun Kim1
1 Dept. of Computer Engineering, Yeungnam University, 280 Daehak-Ro, Gyeongsan, Gyeongbuk, , Korea,
Abstract. The system proposed in this paper is to analyze the concentration of learning. It is the system to detect and analyze facial feature in the image data for analysis of leaner’s concentration. The concentration is important to the learner because the system analyzes and reflects the current situation when they study. We compare and analyze differences of feature points after detecting feature points of the face movement for the concentration analysis.
Keywords: analysis of the concentration, learner's concentration, feature point, analysis of the image, movement of face
1 Introduction
There has been considerable research on face recognition. There is face recognition, eye recognition, as well as movement recognition, such as hand in human recognition. In addition, there is car recognition, license plate recognition, and several object recognitions. In particular, facial recognition research continues and development continues in the facial image and security field. The research related to the facial image and the learning field however, is incomplete leaving much room for further study. In this paper, we propose a method to analyze the concentration of learning through face detection and feature extraction from learner's facial images. We convert it to coordinate after taking a variety of facial images which focus on learning. We examine whether the head is bowed or turned, eyes are closed or opened, whether or not the face is facing front. According to the change of coordinates we determine whether the learner is state of concentration or non-concentration.
2 Related works
The concentration is the opposite word of non-concentration, distraction and dispersion. The learner's concentration is important. Studies related to the concentration are divided into researches improving and measuring the concentration. Extraction and recognition are techniques to extract and recognize a face or an object
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2. Fig. 1. Coodinates of feature points
Advanced Science and Technology Letters
Vol.92 (Education 2015)
in a digital image. These include the Viola Jones algorithm [1], Linear Discriminant Analysis (LDA) [2], Principal Component Analysis (PCA) and Independent
(a) (b) (c)
J(Ax  d)  I(x)
(d) (e) (f)
Fig. 1. Coodinates of feature points
Component Analysis (ICA) [3] in extraction algorithms. In this paper, we use the Viola Jones algorithm to find the face. We use feature points to track or recognize the object. These include the Harris Corner algorithm [4], Shi & Tomasi algorithm [5] and the SIFT algorithm [6]. These researches have been used to find feature points. In this paper, we extract feature points by using the Shi & Tomasi algorithm.
3 Analyze the learner's concentration using detection of facial feature points
In equation (1), a point x in the first image I moves to point (Ax + d) in the second image J, where A = I + D and I is the 2 x 2 identity matrix.
In equation (2), w is the given feature window. w(x) is a weighting function. w could be a Gaussian-like function to emphasize the central area of the window.
(1)
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3. Fig. 2. Changes of Y coordinate in the face
Advanced Science and Technology Letters Vol.92 (Education 2015)
(2)
Changes of the facial length
Front Face Under Face l g n e h t
300
200
100
frame
Fig. 2. Changes of Y coordinate in the face
Changes of the facial center
Front Face Side Face
500
ce nt er
400
300
200
100
frame
Fig. 3. Changes of X coordinate in the face
Changes of eyes
Eye Open Eye Close 40 w d i e h t 30 20 10
frame
Fig. 4. Changes of Y coordinate in the eye
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4. 4 Experimental result 5 Conclusion References
Advanced Science and Technology Letters
Vol.92 (Education 2015)
We examine changes of facial features, the front face, face facing downward, the turned face, closed eyes, opened eyes, in faces of the learner in the video image. After detecting the face region in order to find the feature points of the face, we extract feature points from the extracted face. We set base values of the face and the eye in the coordinate of extracted feature points and we determine the focused state and the non-focused state in comparison with base values. Figure 1 (a), (b), (c) each show the length, the center of the front face and the height of opened eyes that we extract base values of the three. The bowed face is determined by comparing Y coordinates values of the front face and bowed face. Figure 1 (d), (e), (f) each show values of feature points of the state of the bowed face, the turned face, closed eyes. If all conditions do not meet base values it is the front face and the learner is determined the focused state on learning.
4 Experimental result
Figure 2, figure 3 and figure 4 show changes of facial features of people to coordinate chart. Figure 7 shows the coordinate values of the front face and the bowed face, when the front face and the bowed face that changes Y-axis values. Figure 2 shows the varying changes in the Y-axis value when you bow your head and the front that shows the coordinates of the front of the head in a bowed state charts. Figure 3 illustrates coordinate values of the front and side face and changing vales of the Y-axis show that learner’s head is turned to the side. Depending on which way the leaner’s head is turned for example, to the right side or the left, the location of coordinate values ascends or descends. Figure 4 shows the coordinates state of closed eyes and opened eyes. The bowed face really shows that the length of face is smaller than the base value, the turned face appears to be less than or greater than the base center value of the face and closed eyes show the smaller than the height of base eyes.
5 Conclusion
In this paper, we detect facial points feature detection for concentration analysis of learners. First, we detect the face to find feature points and then if feature points of the face are out of base values determine non-focused state. Whether the learner’s face bows or turns from side to side or their eyes open or close based on analysis of data then we confirm whether learners is state of concentrate or non-concentrate.
References
Viola, Paul A. and Jones, Michael J.: Rapid Object Detection using a Boosted Cascade of Simple Features. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume: 1, pp.511–518, 2001.
Martinez, A. M. and Kak, A.C.: PCA versus LDA. IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, pp , Feb
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5. Advanced Science and Technology Letters Vol.92 (Education 2015)
Nguyen, H. and Zheng, R.: Binary Independent Component Analysis With or Mixture. IEEE Trans. Signal Processing, vol. 59, pp , July 2011.
Harris, C., & Stephens, M.: A COMBINED CORNER AND EDGE DETECTOR. In Proceedings of The Fourth Alvey Vision Conference, pp , 1988.
Shi, J., and Tomasi, C.: Good Features to Track. IEEE Conference on Computer Vision and Pattern Recognition, pp , Jun, 1994.
Lowe, D.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, Volume 60, Issue 2, pp , November 2004.
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