Research on the Face Image Detection in Coal Mine Environment

2016 International Conference on Electronic Information Technology and Intellectualization (ICEITI 2016) ISBN: 978-1-60595-364-9

Research on the Face Image Detection in Coal

Mine Environment

Xiucai Guo and Mi Liu

ABSTRACT

In recent years, face recognition system used in the coal mine workers’ identity becomes a hot spot. face detection is the key to face recognition, but at the special coal mine working environment, the monitoring image contrast is low, the light intensity of illumination is weak. The miners’ face image detection becomes difficult. In order to solve this problem, The light density strengthen based on homomorphic filtering, the image noise removal based on wavelet transform was put forward. Canny edge detection method combining the extraction of face and mine cap contour method was used to detect human face area. The experiment shows that this method is well implemented in face detection and is helpful for further identification.

INTRODUCTION

detection is the key to face recognition, therefore the research of the coal mine face detection is of great significance[4].

Image Preprocessing Method

Image preprocessing methods include image histogram equalization, gray stretch method, median filter and homomorphic filter and so on. Under the coal mine, as the specific location of the illumination condition, Monitoring exists the problem of low distinguishability.

Under different illumination conditions, facial image homomorphic filter pretreatment method can be used to avoid light to its influence. Homomorphic filtering processing method will gray level transformation and frequency filtering combine the two methods.

Using the method of homomorphic filtering is to change the original image of the light intensity and the characteristics of the reflected light intensity, It not only can reduce the dynamic range of images, but also can increase the contrast. Homomorphic filter pretreatment of face recognition image. The experimental results are shown in Fig1.

Comparing with the original image, the details of the processed image are more clear, contrast is enhanced, the image effect effectively improves .

Wavelet transform for image compression and finally the image is decomposed into half length of low frequency and high frequency components, the low frequency component of frequency subdivision, high frequency component for the time segment[5]. After the wavelet transform, the useless information of face image is compressed, facial features embodied completely. the method of using wavelet transform to reduce the dimensions of the image. Single scaling wavelet transform to the experimental results are shown in Fig 2.

The original figure grayscale spectrum image before homomorphic filtering

spectrum image after homomorphic filtering image after homomorphic filtering

Figure1. the original image of homomorphic filtering and spectrum image contrast diagram. (note: the images from sina)

Face Detection Realization

Face detection technology in accordance with the general image can be divided into two kinds: one kind is based on static image, the other based on video image which detects the face image in the video detection, if it contains face region will mark in the region. The latter one is more difficult than the former. Because it have higher real-time request for algorithm [6]. the former is mainly divided into three categories: face feature recognition method, her face recognition and other space analysis methods [7]. Face detection method based on color images, The color images can provide abundant information, thus images can make use of their own characteristics.

contour is outstanding. When the threshold value is 74, the edge effect is best, after repairs the edge crack, results are shown in Fig 4.

Figure 2. wavelet transform to each detail images. Figure 3. face image pixels proportion.

Mine cap at the top and use the mine cap model detection, use a rectangle to approach cap edge area, and then according to the characteristics and the facial contour area and the distance from mine cap, determines the face region and marked in the artwork, uses the same identification box to select the face area, the result is shown in Fig 5.

CONCLUSION

face detection area result figure

Figure 4. edge detection effect image. Figure 5. The face detection result.

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