• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1689-1694
• /
• 2005

Face detection plays an important role in face recognition, video surveillance, and human computer interface. In this paper, we present a face detection system using eye detection with progressive thresholding from a digital camera. The face candidate is detected by using skin color segmentation in the YCbCr color space. The face candidates are verified by detecting the eyes that is located by iterative thresholding and correlation coefficients. Preprocessing includes histogram equalization, log transformation, and gray-scale morphology for the emphasized eyes image. The distance of the eye candidate points generated by the progressive increasing threshold value is employed to extract the facial region. The process of the face detection is repeated by using the increasing threshold value. Experimental results show that more enhanced face detection in real time.

• Journal of the Institute of Convergence Signal Processing
• /
• v.7 no.3
• /
• pp.95-101
• /
• 2006

A face detection plays an important role in face recognition, video surveillance, and human computer interaction. In this paper, we propose a progressive threshold method to detect human faces in real time. The consecutive face images are acquired from camera and transformed into YCbCr color space images. The skin color of the input images are separated using a skin color filter in the YCbCr color space and some candidated face areas are decided by connected component analysis. The intensity equalization is performed to avoid the effect of many circumstances and an arbitrary threshold value is applied to get binary images. The eye area can be detected because the area is clearly distinguished from others in the binary image progressive threshold method searches for an optimal eye area by progressively increasing threshold from low values. After progressive thresholding, the eye area is normalized and verified by back propagation algorithm to finalize the face detection.

• Journal of Broadcast Engineering
• /
• v.9 no.1
• /
• pp.43-53
• /
• 2004

In this paper, we propose a robust deinterlacing algorithm which combines edge dependent interpolation (EDI) and global motion compensation (GMC). Generally, EDI algorithm shows a visually better performance than any other deinterlacing algorithm using one field. However, due to the restriction of information in one field, a high duality progressive image from Interlaced sources cannot be acquired by intrafield methods. On the contrary, since algorithms based on motion compensation make use of not only spatial information but also temporal information, they yield better results than those of using one field. However, performance of algorithms based on motion compensation depends on the performance of motion estimation. Hence, the proposed algorithm makes use of mixing process of EDI and GMC. In order to obtain the best result, an adaptive thresholding algorithm for detecting the failure of GMC is proposed. Experimental results indicate that the proposed algorithm outperforms the conventional approaches with respect to both objective and subjective criteria.