Acceptance / Rejection Rule Based Algorithm for Multiple Face Detection in Color Images

(1)

Acceptance / Rejection Rule Based

Algorithm for Multiple Face Detection in

Color Images

MAHESH GOYANI*

Department of Computer Engineering, Sardar Patel University, Vallabh Vidya Nagar – 388 120, Anand, Gujarat, India

maheshgoyani@gcet.ac.in maheshgoyani.co.in

BRIJESH JOSHI

brijesh_joshi001@yahoo.com GITAM SHIKKENAWIS

gitam365@yahoo.co.in Abstract:

Face recognition has been grown as a prime security idea since last decade. Face detection is the basic step in face recognition. In this paper, we have discussed the approach to detect faces from the 2D color images with single or multiple faces. Proposed algorithm work in three steps, skin color segmentation, morphological operations and last step is face rejection or acceptance. The beauty of this algorithm is that it is scale independent and orientation invariant. Skin color segmentation works as a preprocessing step to reduce the processing space. Accuracy of this algorithm is checked against various images with dynamic condition, which shows accuracy range of 90% to 100 %.

Key Words: Face detection, Hue, Saturation, Connected Component Analysis, Morphological operations. 1. Introduction

Many applications in Human Computer Interaction consider the human face as basic entity for authentication [1]. Face detection is the essential step towards face recognition. Because of human gender, race, face geometry, and age etc. factors, face detection and recognition is not the easy task. These areas are still open for the researchers to contribute in improvement of these techniques. Many techniques have been evolved in last decade for the face detection. Skin color works as very good information for the face detection [2].

(2)

2. Materials and Methods

Figure 1: Proposed face detection algorithm

Fig. 1. Proposed Algorithm

System overview of proposed algorithm is shown in figure 1. Images for experiments are taken from internet to check the reliability of system against large range of intensity variation in input image. Our algorithm gives the robust results for single face and multiple face images with different face size and with different face orientations. We have tested system for standard face database too (CVL face database is used for practice). As images are taken from the internet, size and intensity of the images are different. As a first step of processing, we are resizing the image with scale of 400 X 600 pixels resolution to reduce the processing time. We have carried out the entire experiment on the data set of 118 images which contains more than 1000 faces. Images are having differently scaled and oriented faces in it. Accuracy of our algorithm is more than 90 %.

2.1. Skin color segmentation.

Skin color is most commonly used information in face detection in color images [6], [7], [8]. Color is very useful cue to extract skin region and it is only available in color images. It allows face detection without considering much about geometry and texture feature of face [9]. We have chosen skin color as a processing feature because color is almost invariant to scale, orientation and partial occlusion [5]. Apart from this, skin color processing is quick operation and gives good results against cluttered background. In RGB space, the skin color region is not well distinguished in all 3 channels (Figure 2). A simple observation of its histogram shows that it is uniformly spread across a large spectrum of values (Figure 4). In HSV space, the H (Hue) and S (Saturation) channel shows significant discrimination of skin color regions [6] (Figure 3 and Figure 5).

Fig. 2. R, G and B channel color distribution for Skin color

Fig. 3. H and S channel color distribution for Skin color Morphological

Operations

Watershed Segmentation

Apply Structuring Element Input image preprocessing

RGB to HSV Color Space Transformation

Skin Color Segmentation

Connected Component Analysis Face Rejection Rules Face Acceptance

(3)

Figure 2 and 3 shows the skin color distribution for RGB and HS channels respectively. RGB channels do not provide much information, but HS channel gives good discrimination of skin color in plane. Color histogram is global statistical measure of an image [8]. Figure 4 and 5 shows histogram distribution of RGB and HS channels for test image, it clarifies the reason for RGB to HSV color space transformation.

Fig. 4. Skin signature estimation for R, G and B Channel for test image

Fig. 5. Skin signature estimation for H and S Channel for test image

HSV color model is most compatible with human perception system [9]. RGB color space is represented by cube and HSV color space is defined by cone. So RGB to HSV color space transformation is non linear. H and S components give chromatic information and V gives illumination information. H and S are independent of V, so with different illumination and same color, RGB gives different chromaticity value but HSV model exhibit the same value which gives robustness to the HSV color model selection. The transformation is shown below:

Term H is very significant in environment with red color component. The reddish portion of image appears, as brightest area after applying H. Skin color has dominant red color property and hence this color space provides boundary to the skin color and non-skin color regions. Figure 6(a), 6(b) and 6(c) shows RGB color space image, image in H plane and image in S plane respectively.

Fig. 6. (a). Original RGB Color Image, (b). Hue component, (c). Saturation Component

We have defined a set of bounding rules for H and S plane to detect the skin region. Two thresholds for each plane are found for good approximation of ROI. Tsu and Tsl is upper and lower threshold respectively for

(4)

saturation plane. Same way Thu and Thl define the upper and lower bound respectively for the Hue plane. Intersections of these two planes give us ROI. Figure 7 shows the extracted ROI plane after applying below bounding rules.

 Pixel (i , j),

Rule 1: Tsl < Saturation (i, j) < Tsu, Tsl < Tsu,

Tsl, Tsu Є [0, 1]

Rule 2: Thl < Hue (i, j) < Thu Thl < Thu,

Thl, Thu Є [0, 1]

Fig. 7. ROI plane Extraction after applying bounding rules

Figure 8 shows the skin color region in RGB and Gray plane. This area is extracted by applying above two bounding rules. Generally precise shape based face detection techniques are applied after skin color segmentation [9]. Skin color region is converted in to binary based on the gray value of the ROI. Individual pixel comparison is necessary.

Fig. 8. ROI in Skin color and in Gray scale

 Skin_Pixel (ROI (GrayScaleImage)),

BW (i, j) = 255, if Ig(Skin_Pixel) > Tb (4) = 0, otherwise

Where, Ig is the intensity of the gray pixel and Tb is the threshold employed for binarization. Figure 9 shows the result of binarization.

(5)

Morphological operations work with intensity images, so skin color image needs to be converted in gray scale image. Binary outcome of previous step is used to mask original image, to extract out grayscale image of ROI. Intensity thresholding is performed to break up dark regions into many smaller regions so that they can be cleaned up by morphological opening. The threshold is set low enough so that it doesn’t chip away parts of a face but only create holes in it.

Morphological opening is performed to remove very small objects from the image while preserving the shape and size of larger objects in the image. The definition of a morphological opening of an image is erosion followed by dilation, using the same structuring element for both operations. Watershed algorithm is applied to keep the faces as single connected regions in anticipation of a second much larger morphological opening [10]. Otherwise, the mask image will contain many cavities and holes in the faces. Morphological opening is performed to remove small to medium objects that are safely below the size of a face [11]. A disk shaped structuring element of radius 3 is used.

Fig. 10. Results after morphological operations

2.3. Face rejection / acceptance rule

After doing connected component analysis, we are getting many regions on the image as shown in figure 10. It includes face and non face regions. We need some rules to reject the non face regions. Generally face height is multiple of its width by some factor, say f. We have set the range of this factor f as it is not always fixed. We are calculating the bounding box around each connected component.

1. Decide rectangle bonding box for all possible connected component 2. Accept it as a face if it satisfies all the following rules.

2.1. Height to Width ration satisfies the range of f.

2.2. If area of bounding box is greater than area threshold Ta.

Fig. 11. Final Result for Test Image

In figure 10(c), seven connected components are detected, but only face satisfies the acceptance-repentance rules. Two components satisfy the rule 2.1 but fail to satisfy the rule 2.2, as they are having smaller area. Results and Discussions

Some of the resulting images of the experiment are shown in this section. In test image 1, our algorithm has missed classified 2 objects. Neck of the person has same property as face skin and hence, those areas which have passed the acceptance test of area and height to width ratio have been classified as a face region.

(6)

Fig. 12. Result of Test Image 1 Fig. 13. Result of Test Image 2

To show that our algorithm is rotation and scale invariant, we have cropped some faces, scaled it, rotated it and pasted it in test image. Test image 4 shows such combination. Though many faces are of uneven size and orientation, there is no degradation in results.

Table 1 shows the total summary of discussed images with accuracy and false positive rate. We have examined 252 faces of 9 test images, out of which we are getting 236 correct faces and 4 false positive. Result itself shows the accuracy of our algorithm. Some of the face of test image 3 could not be detected because they are too small to pass the acceptance test either based on area or based on height to width ration.

Table 1: Results of Experiment

Test Image No.

Faces in Image

Correctly Detected Faces

Not Detected faces

False Positive

Accuracy (%)

1 30 28 02 02 93.33

2 17 16 - - 94.12

3 83 74 09 - 89.15

4 40 39 01 - 97.50

5 23 23 - - 100.0

6 34 31 02 01 91.18

(7)

References

[1] Saman Cooray, Noel O’Connor: A hybrid technique for face detection in color images

[2] Paola Campadelli, Francesco Cusmai, Raffaella Lanzarotti: Acolor based method for face detection

[3] WANG Tao, BU Jia-Jun, CHEN Chun (2003): A color based face detection system using multiple templates, Journal of Zhejiang University Science, vol. 4, No. 2, p.p. 162-165, China.

[4] Sanjay Kr. Singh, D.S.Chauhan, Mayank Vatsa, Richa Singh: A robust skin color based face detection algorithm [5] Vladimir Vezhnevets, Vassili Sazonov, Alla Andreeva: A survey on pixel-based skin color detection techniques

[6] Xiaoyun Deng, Chip-Hong Chang, Erwin Brandle (2004): A new method for eye extraction from facial image, IEEE International Workshop on Electronic Design, Test and Applications, pp. 29-34.

[7] Wen-Bing Hong, Chih-Yuan Chen, Yi Chang, Chun-Hai Fan (2004): Driver fatigue detection based on eye tracking and dynamic template matching, IEEE International Conference on Networking, Sensing & Control, pp. 7-12.

[8] _{Rei-Lien Hsu}_，_{Mohamed Abdel-Mottaleb Anil K. Jain (2002): Face detection in color images, IEEE Transactions on Pattern}

Analysis and Machine Intelligence, Volume 24, Issue5, PP. 696- 706.

[9] Nusirwan Anwar bin Abdul Rahman, Kit Chong Wei and John See: RGB-H-CbCr skin color model for human face detection [10] Quan Huynh-Thu, Mitsuhiko Meguro, Masahide Kaneko: Skin-color-based image segmentation and its application in face detection [11] Jianzhong Fang and Guoping Qiu: A Color histogram based approach to human face detection, School of Computer Science, The

University of Nottingham.

[12] Krzysztof M. Kryszczuk and Andrzej Drygajło: Color correction for face fetection based on human visual perception metaphor, Signal processing institute, Federal Institute of Technology, Lausame.

[13] Alberto Albiol, Luis Torres, Edward J. Delp: An unsupervised color image segmentation algorithm for face detection applications, University of Catalonia and Polytechnic University of Valencia, Spain