• Title/Summary/Keyword: Face Detection

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Improving the Processing Speed and Robustness of Face Detection for a Psychological Robot Application (심리로봇적용을 위한 얼굴 영역 처리 속도 향상 및 강인한 얼굴 검출 방법)

  • Ryu, Jeong Tak;Yang, Jeen Mo;Choi, Young Sook;Park, Se Hyun
    • Journal of the Korea Industrial Information Systems Research
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    • v.20 no.2
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    • pp.57-63
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    • 2015
  • Compared to other emotion recognition technology, facial expression recognition technology has the merit of non-contact, non-enforceable and convenience. In order to apply to a psychological robot, vision technology must be able to quickly and accurately extract the face region in the previous step of facial expression recognition. In this paper, we remove the background from any image using the YCbCr skin color technology, and use Haar-like Feature technology for robust face detection. We got the result of improved processing speed and robust face detection by removing the background from the input image.

Real-time Slant Face detection using improvement AdaBoost algorithm (개선한 아다부스트 알고리즘을 이용한 기울어진 얼굴 실시간 검출)

  • Na, Jong-Won
    • Journal of Advanced Navigation Technology
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    • v.12 no.3
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    • pp.280-285
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    • 2008
  • The traditional face detection method is to use difference picture method are used to detect movement. However, most do not consider this mathematical approach using real-time or real-time implementation of the algorithm is complicated, not easy. This paper, the first to detect real-time facial image is converted YCbCr and RGB video input. Next, you convert the difference between video images of two adjacent to obtain and then to conduct Glassfire Labeling. Labeling value compared to the threshold behavior Area recognizes and converts video extracts. Actions to convert video to conduct face detection, and detection of facial characteristics required for the extraction and use of AdaBoost algorithm.

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Face Detection and Matching for Video Indexing (비디오 인덱싱을 위한 얼굴 검출 및 매칭)

  • Islam Mohammad Khairul;Lee Sun-Tak;Yun Jae-Yoong;Baek Joong-Hwan
    • Proceedings of the Korea Institute of Convergence Signal Processing
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    • pp.45-48
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    • 2006
  • This paper presents an approach to visual information based temporal indexing of video sequences. The objective of this work is the integration of an automatic face detection and a matching system for video indexing. The face detection is done using color information. The matching stage is based on the Principal Component Analysis (PCA) followed by the Minimax Probability Machine (MPM). Using PCA one feature vector is calculated for each face which is detected at the previous stage from the video sequence and MPM is applied to these feature vectors for matching with the training faces which are manually indexed after extracting from video sequences. The integration of the two stages gives good results. The rate of 86.3% correctly classified frames shows the efficiency of our system.

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Face Detection for Cast Searching in Video (비디오 등장인물 검색을 위한 얼굴검출)

  • Paik Seung-ho;Kim Jun-hwan;Yoo Ji-sang
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.30 no.10C
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    • pp.983-991
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    • 2005
  • Human faces are commonly found in a video such as a drama and provide useful information for video content analysis. Therefore, face detection plays an important role in applications such as face recognition, and face image database management. In this paper, we propose a face detection algorithm based on pre-processing of scene change detection for indexing and cast searching in video. The proposed algorithm consists of three stages: scene change detection stage, face region detection stage, and eyes and mouth detection stage. Experimental results show that the proposed algorithm can detect faces successfully over a wide range of facial variations in scale, rotation, pose, and position, and the performance is improved by $24\%$with profile images comparing with conventional methods using color components.

Face Detection Using Geometrical Information of Face and Hair Region (얼굴과 헤어영역의 기하학적 정보를 이용한 얼굴 검출)

  • Lee, Woo-Ram;Hwang, Dong-Guk;Jun, Byoung-Min
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.34 no.2C
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    • pp.194-199
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    • 2009
  • This paper proposes a face detection algorithm that uses geometrical information on face and hair region. This information that face adjoins hair regions can be the important one for face detection. It is also kept in images with frontal, rotated and lateral face. The face candidates are founded by the analysis of skin regions after detecting the skin and hair color regions in an image. Next, the intersected lesions between face candidates and hair's are created. Finally, the face candidates that include the subsets of these regions turn out to be face. Experimental results showed the high detection rates for frontal and lateral faces as well as faces geometrically distorted.

Face region detection algorithm of natural-image (자연 영상에서 얼굴영역 검출 알고리즘)

  • Lee, Joo-shin
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.7 no.1
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    • pp.55-60
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    • 2014
  • In this paper, we proposed a method for face region extraction by skin-color hue, saturation and facial feature extraction in natural images. The proposed algorithm is composed of lighting correction and face detection process. In the lighting correction step, performing correction function for a lighting change. The face detection process extracts the area of skin color by calculating Euclidian distances to the input images using as characteristic vectors color and chroma in 20 skin color sample images. Eye detection using C element in the CMY color model and mouth detection using Q element in the YIQ color model for extracted candidate areas. Face area detected based on human face knowledge for extracted candidate areas. When an experiment was conducted with 10 natural images of face as input images, the method showed a face detection rate of 100%.

Face Detection using Brightness Distribution in the Surrounding Area of Eye (눈 주변영역의 명암분포를 이용한 얼굴탐지)

  • Hwang, Dae-Dong;Park, Joo-Chul;Kim, Gye-Young
    • The KIPS Transactions:PartB
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    • v.16B no.6
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    • pp.443-450
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    • 2009
  • This paper develops a novel technique of face detection using brightness distribution in the surrounding area of eye. The proposed face detection consists of facial component candidate extraction, facial component candidate filtering through eye-lip combination, left/right eye classification using brightness distribution, face verification confirming edges in nose region. Because the proposed technique don't use any skin color, it can detect multiple faces in color images with complicated backgrounds and different illumination levels. The experimental results reveal that the proposed technique is better than the traditional techniques in terms of detection ratio.

Robust Face Detection Based on Knowledge-Directed Specification of Bottom-Up Saliency

  • Lee, Yu-Bu;Lee, Suk-Han
    • ETRI Journal
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    • v.33 no.4
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    • pp.600-610
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    • 2011
  • This paper presents a novel approach to face detection by localizing faces as the goal-specific saliencies in a scene, using the framework of selective visual attention of a human with a particular goal in mind. The proposed approach aims at achieving human-like robustness as well as efficiency in face detection under large scene variations. The key is to establish how the specific knowledge relevant to the goal interacts with the bottom-up process of external visual stimuli for saliency detection. We propose a direct incorporation of the goal-related knowledge into the specification and/or modification of the internal process of a general bottom-up saliency detection framework. More specifically, prior knowledge of the human face, such as its size, skin color, and shape, is directly set to the window size and color signature for computing the center of difference, as well as to modify the importance weight, as a means of transforming into a goal-specific saliency detection. The experimental evaluation shows that the proposed method reaches a detection rate of 93.4% with a false positive rate of 7.1%, indicating the robustness against a wide variation of scale and rotation.

Robust Object Tracking System Based on Face Detection (얼굴검출에 기반한 강인한 객체 추적 시스템)

  • Kwak, Min Seok
    • KIPS Transactions on Software and Data Engineering
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    • v.6 no.1
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    • pp.9-14
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    • 2017
  • Embedded devices with the development of modern computer technology also began equipped with a variety of functions. In this study, to provide a method of tracking efficient face with a small instrument of resources, such as built-in equipment that uses an image sensor in recent years has been actively carried out. It uses a face detection method using the features of the MB-LBP in order to obtain an accurate face, specify the region (Region of Interest) around the face when the face detection for the face object tracking in the next video did. And in the video can not be detected faces, to track objects using the CAM-Shift key is a conventional object tracking method, which make it possible to retain the information without loss of object information. In this study, through the comparison with the previous studies, it was confirmed the precision and high-speed performance of the object tracking system.

A Fast and Accurate Face Tracking Scheme by using Depth Information in Addition to Texture Information

  • Kim, Dong-Wook;Kim, Woo-Youl;Yoo, Jisang;Seo, Young-Ho
    • Journal of Electrical Engineering and Technology
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    • v.9 no.2
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    • pp.707-720
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    • 2014
  • This paper proposes a face tracking scheme that is a combination of a face detection algorithm and a face tracking algorithm. The proposed face detection algorithm basically uses the Adaboost algorithm, but the amount of search area is dramatically reduced, by using skin color and motion information in the depth map. Also, we propose a face tracking algorithm that uses a template matching method with depth information only. It also includes an early termination scheme, by a spiral search for template matching, which reduces the operation time with small loss in accuracy. It also incorporates an additional simple refinement process to make the loss in accuracy smaller. When the face tracking scheme fails to track the face, it automatically goes back to the face detection scheme, to find a new face to track. The two schemes are experimented with some home-made test sequences, and some in public. The experimental results are compared to show that they outperform the existing methods in accuracy and speed. Also we show some trade-offs between the tracking accuracy and the execution time for broader application.