• Journal of Institute of Control, Robotics and Systems
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• v.22 no.4
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• pp.266-270
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• 2016

In this paper, we propose a technique of drowsiness detection using machine vision. The drowsiness of vehicle driver is often the primary cause of motor vehicle accidents. Therefore, the checking of eye images for detecting drowsiness status of driver is critical for preventing these accidents. In our suggested method, we analyze the changes of histogram and edge of eye region images which are acquired using CCD camera. We developed a drowsiness detection system using the histogram and edge change information. The experimental results show that our proposed method enhances the accuracy of detecting drowsiness nearly to 98%, and can be used for preventing vehicle accidents due to the drowsiness of drivers.

• Journal of Sensor Science and Technology
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• v.8 no.1
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• pp.63-68
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• 1999

We proposed a method of eye pattern detection in the 2-D image which was obtained by CCD video camera. To detect face region and eye pattern, we proposed pattern search network and batch SVD algorithm which had the statistical equivalence of PCA. We also used HMM to improve the accuracy of detection. As a result, we acknowledged that the proposed algorithm was superior to PCA pattern detection algorithm in computational cost and accuracy of defection. Furthermore, we evaluated that the proposed algorithm was possible in real-time face pattern detection with 2 frame images per second.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.478-484
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• 2009

Automatic speech recognition(ASR) is attractive technique in trend these day that seek convenient life. Although many approaches have been proposed for ASR but the performance is still not good in noisy environment. Now-a-days in the state of art in speech recognition, ASR uses not only the audio information but also the visual information. In this paper, We present a novel lip detection method for visual speech recognition in mobile environment. In order to apply visual information to speech recognition, we need to extract exact lip regions. Because eye-detection is more easy than lip-detection, we firstly detect positions of left and right eyes, then locate lip region roughly. After that we apply K-means clustering technique to devide that region into groups, than two lip corners and lip center are detected by choosing biggest one among clustered groups. Finally, we have shown the effectiveness of the proposed method through the experiments based on samsung AVSR database.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.12
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• pp.71-83
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• 1996

This paper describes facial component detection and skew correction algorithm for face recognition. We use a priori knowledge and models about isolated regions to detect eye location from the face image captured in natural office environments. The relations between human face components are represented by several rules. We adopt an edge detection algorithm using sobel mask and 8-connected labelling algorith using array pointers. A labeled image has many isolated components. initially, the eye size rules are used. Eye size rules are not affected much by irregular input image conditions. Eye size rules size, and limited in the ratio between gorizontal and vertical sizes. By the eye size rule, 2 ~ 16 candidate eye components can be detected. Next, candidate eye parirs are verified by the information of location and shape, and one eye pair location is decided using face models about eye and eyebrow. Once we extract eye regions, we connect the center points of the two eyes and calculate the angle between them. Then we rotate the face to compensate for the angle so that the two eyes on a horizontal line. We tested 120 input images form 40 people, and achieved 91.7% success rate using eye size rules and face model. The main reasons of the 8.3% failure are due to components adjacent to eyes such as eyebrows. To detect facial components from the failed images, we are developing a mouth region processing module.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.1
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• pp.41-49
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• 2022

This paper proposes a novel lane detection algorithm based on inverse perspective transformation and machine learning in lightweight embedded system. The inverse perspective transformation method is presented for obtaining a bird's-eye view of the scene from a perspective image to remove perspective effects. This method requires only the internal and external parameters of the camera without a homography matrix with 8 degrees of freedom (DoF) that maps the points in one image to the corresponding points in the other image. To improve the accuracy and speed of lane detection in complex road environments, machine learning algorithm that has passed the first classifier is used. Before using machine learning, we apply a meaningful first classifier to the lane detection to improve the detection speed. The first classifier is applied in the bird's-eye view image to determine lane regions. A lane region passed the first classifier is detected more accurately through machine learning. The system has been tested through the driving video of the vehicle in embedded system. The experimental results show that the proposed method works well in various road environments and meet the real-time requirements. As a result, its lane detection speed is about 3.85 times faster than edge-based lane detection, and its detection accuracy is better than edge-based lane detection.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.3
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• pp.542-548
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• 2006

In this paper, we propose a face region detection/verification method using wavelet packet analysis and structural statistic for frontal human color image. The method extracts skin color lesions from input images, first. and then applies spatial restrictive conditions to the region, and determines whether the region is face candidate region or not. In second step, we find eye region in the face candidate region using structural statistic for standard korean faces. And in last step, the face region is verified via wavelet packet analysis if the face torture were satisfied to normal texture conditions.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.5
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• pp.175-182
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• 2010

The paper presents a novel algorithm for face and iris detection with the application for driver iris monitoring. The proposed algorithm consists of the following major steps: Skin-color segmentation, facial features segmentation, and iris positioning. For the skin-segmentation we applied a multi-layer perceptron to approximate the statistical probability of certain skin-colors, and filter out those with low probabilities. The next step segments the face region into the following categories: eye, mouth, eye brow, and remaining facial regions. For this purpose we propose a novel segmentation technique based on estimation of facial class probability density functions (PDF). Each facial class PDF is estimated on the basis of salient features extracted from a corresponding facial image region. Then pixels are classified according to the highest probability selected from four estimated PDFs. The final step applies the circular Hough transform to the detected eye regions to extract the position and radius of the iris. We tested our system on two data sets. The first one is obtained from the Web and contains faces under different illuminations. The second dataset was collected by us. It contains images obtained from video sequences recorded by a CCD camera while a driver was driving a car. The experimental results are presented, showing high detection rates.

• Journal of Biomedical Engineering Research
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• v.16 no.2
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• pp.121-128
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• 1995

Information about retinal blood vessels can be used in grading disease severity or as part of the process of automated diagnosis of diseases with ocular menifestations. In this paper, we address the problem of detecting retinal blood vessels and optic disk (papilla) in eye-fundus images. We introduce an algorithm for feature extraction based on Fuzzy Clustering algorithm (fuzzy c-means). A method of finding the optic disk (papilla) is proposed in the eye-fundus images. Additionally, the inrormations such as position and area of the optic disk are extracted. The results are compared to those obtained from other methods. The automatic detection of retinal blood vessels and optic disk in the eye-rundus images could help physicians in diagnosing ocular diseases.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.531-535
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• 1999

Automatic human face detection in a complex background is one of the difficult problems. In this paper, we propose an effective and robust automatic face detection approach that can locate the face region in natural scene images when the system is used as a pre-processor of a face recognition system . We use two natural and powerful visual cues, the skin color and the eyes. In the first step of the proposed system, the method based on the human skin color space by selecting flesh tone regions using normalized r-g space in color images. In the next step, we extract eye features by calculating moments and using geometrical face model. Experimental results demonstrate that the approach can efficiently detect human faces and satisfactory deal with the problems caused by bad lighting condition, skew face orientation.

• Journal of Korea Multimedia Society
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• v.7 no.1
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• pp.27-34
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• 2004

This thesis proposes a face detection algorithm using the color and edge informations in color image. The proposed algorithm segments skin color by Cb and Cr in YCbCr coordinates. Then face candidate regions are made after morphological filtering and labeling. For the regions, the Sobel vortical operation and horizontal projection are performed in the Y luminance components. The peak value indicates the eye location. Similarly, the chin location is detected by the Sobel horizontal operation and horizontal projection. The computer simulation shows that the proposed method gains similar detection rates of previous method and prevent facial region from including neck by detection of chin.