• Title/Summary/Keyword: Face Detection

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Face detection in compressed domain using color balancing for various illumination conditions (다양한 조명 환경에서의 실시간 사용자 검출을 위한 압축 영역에서의 색상 조절을 사용한 얼굴 검출 방법)

  • Min, Hyun-Seok;Lee, Young-Bok;Shin, Ho-Chul;Lim, Eul-Gyoon;Ro, Yong-Man
    • 한국HCI학회:학술대회논문집
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    • pp.140-145
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    • 2009
  • Significant attention has recently been drawn to human robot interaction system that uses face detection technology. The most conventional face detection methods have applied under pixel domain. These pixel based face detection methods require high computational power. Hence, the conventional methods do not satisfy the robot environment that requires robot to operate in a limited computing process and saving space. Also, compensating the variation of illumination is important and necessary for reliable face detection. In this paper, we propose the illumination invariant face detection that is performed under the compressed domain. The proposed method uses color balancing module to compensate illumination variation. Experiments show that the proposed face detection method can effectively increase the face detection rate under existing illumination.

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Design and Implementation of User-oriented Face Detection System for Application Developers (응용개발자를 위한 사용자 중심 얼굴검출 시스템 설계 및 구현)

  • Jang, Dae Sik
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.6 no.4
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    • pp.161-170
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    • 2010
  • This paper provides a novel approach for a user oriented system for face detection system for application developers. Even though there are many open source or commercial libraries to solve the problem of face detection, they are still hard to use because they require specific knowledge on detail algorithmic techniques. The purpose of this paper is to come up with a high-level system for face detection with which users can develop systems easily and even without specific knowledge on face detection theories and algorithms. Important conditions are firstly considered to categorize the large problem space of face detection. The conditions identified here are then represented as expressions so that application developers can use them to express various problems. Once the conditions are expressed by developers, the interpreter proposed take the role to interpret the conditions, find and organize the optimal algorithms to solve the represented problem with corresponding conditions. A proof-of-concept is implemented and some example problems are tested and analyzed to show the ease of use and usability.

ENHANCEMENT OF FACE DETECTION USING SPATIAL CONTEXT INFORMATION

  • Min, Hyun-Seok;Lee, Young-Bok;Lee, Si-Hyoung;Ro, Yong-Man
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • pp.108-113
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    • 2009
  • Significant attention has recently been drawn to digital home photo albums that use face detection technology. The tendency can be found in home photo albums that people prefer to allocate concerned objects in the center of the image rather than the boundary when they take a picture. To improve detection performance and speed that are important factors of face detection task, this paper proposes a face detection method that takes spatial context information into consideration. Experiments were performed to verify the usefulness of the proposed method and results indicate that the proposed face detection method can efficiently reduce the false positive rate as well as the runtime of face detection.

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High Speed Face Detection Using Skin Color (살색을 이용한 고속 얼굴검출 알고리즘의 개발)

  • 한영신;박동식;이칠기
    • Proceedings of the IEEK Conference
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    • pp.173-176
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    • 2002
  • This paper describes an implementation of fast face detection algorithm. This algorithm can robustly detect human faces with unknown sizes and positions in complex backgrounds. This paper provides a powerful face detection algorithm using skin color segmenting. Skin Color is modeled by a Gaussian distribution in the HSI color space among different persons within the same race, Oriental. The main feature of the Algorithm is achieved face detection robust to illumination changes and a simple adaptive thresholding technique for skin color segmentation is employed to achieve robust face detection.

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A Study on Real-time Face Detection in Video (동영상에서 실시간 얼굴검출에 관한 연구)

  • Kim, Hyeong-Gyun;Bae, Yong-Guen
    • Journal of the Korea Society of Computer and Information
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    • v.15 no.2
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    • pp.47-53
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    • 2010
  • This paper proposed Residual Image detection and Color Info using the face detection technique. The proposed technique was fast processing speed and high rate of face detection on the video. In addition, this technique is to detection error rate reduced through the calibration tasks for tilted face image. The first process is to extract target image from the transmitted video images. Next, extracted image processed by window rotated algorithm for detection of tilted face image. Feature extraction for face detection was used for AdaBoost algorithm.

Face Detection using AdaBoost and ASM (AdaBoost와 ASM을 활용한 얼굴 검출)

  • Lee, Yong-Hwan;Kim, Heung-Jun
    • Journal of the Semiconductor & Display Technology
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    • v.17 no.4
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    • pp.105-108
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    • 2018
  • Face Detection is an essential first step of the face recognition, and this is significant effects on face feature extraction and the effects of face recognition. Face detection has extensive research value and significance. In this paper, we present and analysis the principle, merits and demerits of the classic AdaBoost face detection and ASM algorithm based on point distribution model, which ASM solves the problems of face detection based on AdaBoost. First, the implemented scheme uses AdaBoost algorithm to detect original face from input images or video stream. Then, it uses ASM algorithm converges, which fit face region detected by AdaBoost to detect faces more accurately. Finally, it cuts out the specified size of the facial region on the basis of the positioning coordinates of eyes. The experimental result shows that the method can detect face rapidly and precisely, with a strong robustness.

Face Detection Based on Thick Feature Edges and Neural Networks

  • Lee, Young-Sook;Kim, Young-Bong
    • Journal of Korea Multimedia Society
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    • v.7 no.12
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    • pp.1692-1699
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    • 2004
  • Many researchers have developed various techniques for detection of human faces in ordinary still images. Face detection is the first imperative step of human face recognition systems. The two main problems of human face detection are how to cutoff the running time and how to reduce the number of false positives. In this paper, we present frontal and near-frontal face detection algorithm in still gray images using a thick edge image and neural network. We have devised a new filter that gets the thick edge image. Our overall scheme for face detection consists of two main phases. In the first phase we describe how to create the thick edge image using the filter and search for face candidates using a whole face detector. It is very helpful in removing plenty of windows with non-faces. The second phase verifies for detecting human faces using component-based eye detectors and the whole face detector. The experimental results show that our algorithm can reduce the running time and the number of false positives.

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Face Detection by Eye Detection with Progressive Thresholding

  • Jung, Ji-Moon;Kim, Tae-Chul;Wie, Eun-Young;Nam, Ki-Gon
    • 제어로봇시스템학회:학술대회논문집
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    • pp.1689-1694
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    • 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.

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Implementation of Face Detection System on Android Platform for Real-Time Applications (실시간 응용을 위한 안드로이드 플랫폼에서의 안면 검출 시스템 구현)

  • Han, Byung-Gil;Lim, Kil-Taek
    • IEMEK Journal of Embedded Systems and Applications
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    • v.8 no.3
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    • pp.137-143
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    • 2013
  • This paper describes an implementation of face detection technology for a real-time application on the Android platform. Java class of Face-Detection for detection of human face is provided by the Android API. However, this function is not suitable to apply for the real-time applications due to inadequate detection speed and accuracy. In this paper, the AdaBoost based classification method which utilizes Local Binary Pattern (LBP) histogram is employed for face detection. The face detection module has been developed by C/C++ language for high-speed image processing, and this module is included to the Android platform using the Java Native Interface (JNI). The experiments were carried out in the Java-based environment and JNI-based environment. The experimental results have shown that the performance of JNI-based is faster than Java-based method and our system is well enough to apply for real-time applications.

Face Detection Algorithm Using Color Distribution Matching (영상의 색상 분포 정합을 이용한 얼굴 검출 알고리즘)

  • Kwon, Seong-Geun
    • Journal of Korea Multimedia Society
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    • v.16 no.8
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    • pp.927-933
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    • 2013
  • Face detection algorithm of OpenCV recognizes the faces by Haar matching between input image and Haar features which are learned through a set of training images consisting of many front faces. Therefore the face detection method by Haar matching yields a high face detection rate for the front faces but not in the case of the pan and deformed faces. On the assumption that distributional characteristics of color histogram is similar even if deformed or side faces, a face detection method using the histogram pattern matching is proposed in this paper. In the case of the missed detection and false detection caused by Haar matching, the proposed face detection algorithm applies the histogram pattern matching with the correct detected face area of the previous frame so that the face region with the most similar histogram distribution is determined. The experiment for evaluating the face detection performance reveals that the face detection rate was enhanced about 8% than the conventional method.