Author Guidelines for 8 s12

Skin Color Based Face Detection Using Multi-Layer Perceptron

Taro Hosei

Faculty of Computer and Information Sciences

Hosei University

E-mail:

Supervisor: Prof. Toru Wakahara

Abstract

This paper addresses the very complex, challenging problem of human face detection in color images with complex background and a variety of lighting conditions. We propose a new, skin-color-based face detection method using a multi-layer perceptron (MLP) as applied to a two-class classification problem between “skin-color” and “non-skin color.” The proposed method has two key ideas. One is effective skin color detection using MLP trained in a combined HLS and YCbCr color space. The other is a hierarchical, sequential process of face detection: segmentation and grouping of skin regions, face candidate selection, and verification of face candidates. This process utilizes not only image processing techniques such as connected component analysis, morphological operation, and contour tracing but also the top-down knowledge about facial characteristics such as the aspect ratio of a face and the existence of eyes as holes in a skin region. Experimental results using our personal photo collection demonstrate that the proposed method achieves a high and encouraging face detection rate of 80.79% while suppressing a false positive detection rate.

1. Introduction

Human face detection is a crucial step in a wide variety of personal identification applications such as human computer interface, face recognition, image retrieval using face images, face image database management and video surveillance [1].

Various approaches to face detection are proposed. For recent surveys on face detection, see the excellent one [2]. Conventional techniques are classified into tow major approaches: appearance-based methods versus feature-based methods. The former includes template matching using deformable models and probabilistic/statistical pattern matching using principal component analysis [3], eigenface [4], neural network, support vector machines, and hidden markov model. The latter utilizes facial features, texture, and skin color [5] together with the rules about the relationships between facial features to verify the existence of a face.

Our approach belongs to the feature-based methods, in particular, focusing on skin color information. The key idea of the proposed method is application of multi-layer perceptron to a two-class classification problem between “skin color” and “non-skin color” in a combined color space of HLS and YCrCb. Moreover, we propose an effective, sequential procedure of face detection making full use of the empirical knowledge about facial characteristics such as the aspect ratio of a face and the existence of eyes ;as holes in a skin region.

Experiment results using our personal collection of photos (both indoors and outdoors) demonstrate that the proposed method is robust against a wide variation of facial color, position, scale, orientation, expression, and lighting conditions.

Figure 1. Total flow of face detection in the proposed method.

2. Face image database

We use our personal photo collection. Our collection includes 152 photos of friends and landscape. Each of these images has zero or more faces with various directions in complex backgrounds. 177 faces are contained in this photograph collection in total. All images we used have 640×480 pixels in a PPM color format.

One example of our photo collection is shown in Figure 1. Figure 1 shows the total flow of the proposed method of face detection explained in the following sections.

7. Conclusion

We described a novel face detection method for color images, using MLP and sequential procedure of face determination. Experimental results demonstrate that the proposed method achieves a high and encouraging face detection rate while suppressing a false positive detection rate, and it is robust against a wide variation of face. It is easy to customize for the purpose because the control of the performance is possible by decreasing the step of the face area determination.

References

[1] A. Jain , R. Bolle, and S. Pankanti, Eds., BIOMETRICS - Personal Identification in Networked Society -, Kluwer Academic Publishers, 1999.

[2] M.-H. Yang, D. Kriegman, and N. Ahuja, “Detecting faces in images: a survey,” IEEE Trans. Pattern Anal. Machine Intell., vol. 24, pp. 34-58, 2002.

[3] B. Menser and F. Muller, “Face detection in color images using principal components analysis” in Proc. Image Processing and its Applications, vol. 2, pp. 620-624, 1999.

[4] B. Moghaddam and A. Pentland, “Probabilistic, view-based, and modular models for human face recognition,” in Handbook of Image and Video Processing, pp. 837-852, Academic Press, 2000.

[5] R.-L. Hsu, M. Abdel-Mottaleb, and A. K. Jain, “Face detection in color images,” IEEE Trans. Pattern Anal. Machine Intell., vol. 24, pp. 696-706, May 2002.

[6] S. Madhvanath, G. Kim, and V. Govindaraju, “Chaincode contour processing for handwritten word recognition,” IEEE Trans. Pattern Anal. Machine Intell., vol. 21, pp. 928-932, 1999.

[7] K. Miki, “Face detection using skin color and correlogram,” in Graduation Theses 2003: Extended Abstracts, Faculty of Computer and Information Sciences, Hosei University, pp.423-426, 2004.