• KIPS Transactions on Software and Data Engineering
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• v.4 no.8
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• pp.309-314
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• 2015

One of typical methods for extracting facial features from face images may be snake. Although snake is simple and fast, performance is very much affected by the initial contour and the shape of object to be extracted. In this paper, the enhanced snake is proposed to extract better facial features from 6 lip and mouth images as snake point is added to the midpoint of snake segment. It is shown that RSD of the proposed method is about 2.8% to 5.8% less than that of Greedy snake about 6 test face images. Since lesser RSD is especially obtained for contours with highly concavity, the contour is more accurately extracted.

• Journal of KIISE:Software and Applications
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• v.27 no.7
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• pp.731-741
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• 2000

This paper proposes a method to detect a facial region and extract facial features which is crucial for visual recognition of human faces. In this paper, we extract the MER(Minimum Enclosing Rectangle) of a face and facial components using projection analysis on both edge image and binary image. We use an active contour model(snakes) for extraction of the contours of eye, mouth, eyebrow, and face in order to reflect the individual differences of facial shapes and converge quickly. The determination of initial contour is very important for the performance of snakes. Particularly, we detect Minimum Enclosing Rectangle(MER) of facial components and then determine initial contours using general shape of facial components within the boundary of the obtained MER. We obtained experimental results to show that MER extraction of the eye, mouth, and face was performed successfully. But in the case of images with bright eyebrow, MER extraction of eyebrow was performed poorly. We obtained good contour extraction with the individual differences of facial shapes. Particularly, in the eye contour extraction, we combined edges by first order derivative operator and zero crossings by second order derivative operator in designing energy function of snakes, and we achieved good eye contours. For the face contour extraction, we used both edges and grey level intensity of pixels in designing of energy function. Good face contours were extracted as well.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.7
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• pp.935-943
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• 1993

In this paper, robust recognition system for tilted facial image was developed. At first, standard facial image and lilted facial image are captured by CCTV camera and then transformed into binary image. The binary image is processed in order to obtain contour image by Laplacian edge operator. We trace and delete outermost edge line and use inner contour lines. We label four inner contour lines in order among the inner lines, and then we extract left and right eye with known distance relationship and with two eyes coordinates, and calculate slope information. At last, we rotate the tilted image in accordance with slope information and then calculate the ten distance features between element and element. In order to make the system invariant to image scale, we normalize these features with distance between left and righ eye. Experimental results show 88% recognition rate for twenty five face images when tilted degree is considered and 60% recognition rate when tilted degree is not considered.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.496-498
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• 2001

Snake 모델(active contour model)은 초기값을 설정해주면 자동으로 임의의 물체의 윤곽을 찾아내는 알고리즘으로 영상에서 특정 영역을 분할하여 할 때 많이 이용되고 있다. 본 논문에서는 칼라 영상에서 얼굴과 얼굴의 특징점을 찾는 방법으로 이 알고리즘을 적용한다. 특히, 주어진 영상의 RGB 값을 정규화(normalization) 해주는 전처리 과정을 통해 얼굴의 특징점 후보 영역을 얻어내는 초기 값을 설정해주어야 하는 과정을 생략해주고 보다 정확한 값을 얻을 수 있도록 구현한다. RGB 값을 이용한 정규화 과정을 적용한 방법과 적용하지 않은 방법을 구현한 결과를 비교해줌으로써, 정규화 과정을 거친 방법의 성능이 더 우수함을 보여준다.

• Journal of the Semiconductor & Display Technology
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• v.15 no.2
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• pp.61-65
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• 2016

Facial feature detection is a fundamental function in the field of computer vision such as security, bio-metrics, 3D modeling, and face recognition. There are many algorithms for the function, active shape model is one of the most popular local texture models. This paper addresses issues related to face detection, and implements an efficient extraction algorithm for extracting the facial feature points to use on iOS platform. In this paper, we extend the original ASM algorithm to improve its performance by four modifications. First, to detect a face and to initialize the shape model, we apply a face detection API provided from iOS CoreImage framework. Second, we construct a weighted local structure model for landmarks to utilize the edge points of the face contour. Third, we build a modified model definition and fitting more landmarks than the classical ASM. And last, we extend and build two-dimensional profile model for detecting faces within input images. The proposed algorithm is evaluated on experimental test set containing over 500 face images, and found to successfully extract facial feature points, clearly outperforming the original ASM.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.127-133
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• 2021

In this study, an augmented reality-based make-up tool was built to analyze the user's face shape based on face-type reference model data and to provide virtual makeup by providing face-type makeup. To analyze the face shape, first recognize the face from the image captured by the camera, then extract the features of the face contour area and use them as analysis properties. Next, the feature points of the extracted face contour area are normalized to compare with the contour area characteristics of each face reference model data. Face shape is predicted and analyzed using the distance difference between the feature points of the normalized contour area and the feature points of the each face-type reference model data. In augmented reality-based virtual makeup, in the image input from the camera, the face is recognized in real time to extract the features of each area of the face. Through the face-type analysis process, you can check the results of virtual makeup by providing makeup that matches the analyzed face shape. Through the proposed system, We expect cosmetics consumers to check the makeup design that suits them and have a convenient and impact on their decision to purchase cosmetics. It will also help you create an attractive self-image by applying facial makeup to your virtual self.