• Title/Summary/Keyword: Face Detection

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DFT integration for Face Detection (DFT를 이용한 Face Detection)

  • Han, Seok-Min;Choi, Jin-Young
    • Proceedings of the KIEE Conference
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    • pp.117-119
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    • 2006
  • In this work, we suggest another method to localize DFT in spatial domain. This enables DFT algorithm to be used for local pattern matching. Once calculated, it costs same load to calculate localized DFT regardless of the size or the position of local region In spatial domain. We applied this method to face detection problem and got the results which prove the utility of our method.

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Selection of Optimal Face Detection Algorithms by Fuzzy Inference (퍼지추론을 이용한 최적의 얼굴검출 알고리즘 선택기법)

  • Jang, Dae-Sik
    • Journal of the Korea Society of Computer and Information
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    • v.16 no.1
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    • pp.71-80
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    • 2011
  • This paper provides a novel approach for developers to use face detection techniques for their applications easily without special knowledge by selecting optimal face detection algorithms based on fuzzy inference. The purpose of this paper is to come up with a high-level system for face detection based on fuzzy inference 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 developers can use them to express various problems. The expressed conditions and available face detection algorithms constitute the fuzzy inference rules and the Fuzzy Interpreter is constructed based on the rules. Once the conditions are expressed by developers, the Fuzzy Interpreter proposed take the role to inference the conditions and find and organize the optimal algorithms to solve the represented problem with corresponding conditions. A proof-of-concept is implemented and tested compared to conventional algorithms to show the performance of the proposed approach.

Face Detection Using Pixel Direction Code and Look-Up Table Classifier (픽셀 방향코드와 룩업테이블 분류기를 이용한 얼굴 검출)

  • Lim, Kil-Taek;Kang, Hyunwoo;Han, Byung-Gil;Lee, Jong Taek
    • IEMEK Journal of Embedded Systems and Applications
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    • v.9 no.5
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    • pp.261-268
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    • 2014
  • Face detection is essential to the full automation of face image processing application system such as face recognition, facial expression recognition, age estimation and gender identification. It is found that local image features which includes Haar-like, LBP, and MCT and the Adaboost algorithm for classifier combination are very effective for real time face detection. In this paper, we present a face detection method using local pixel direction code(PDC) feature and lookup table classifiers. The proposed PDC feature is much more effective to dectect the faces than the existing local binary structural features such as MCT and LBP. We found that our method's classification rate as well as detection rate under equal false positive rate are higher than conventional one.

Real Time Face Detection and Recognition based on Embedded System (임베디드 시스템 기반 실시간 얼굴 검출 및 인식)

  • Lee, A-Reum;Seo, Yong-Ho;Yang, Tae-Kyu
    • Journal of The Institute of Information and Telecommunication Facilities Engineering
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    • v.11 no.1
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    • pp.23-28
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    • 2012
  • In this paper, we proposed and developed a fast and efficient real time face detection and recognition which can be run on embedded system instead of high performance desktop. In the face detection process, we detect a face by finding eye part which is one of the most salient facial features after applying various image processing methods, then in the face recognition, we finally recognize the face by comparing the current face with the prepared face database using a template matching algorithm. Also we optimized the algorithm in our system to be successfully used in the embedded system, and performed the face detection and recognition experiments on the embedded board to verify the performance. The developed method can be applied to automatic door, mobile computing environment and various robot.

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Multi-Face Detection on static image using Principle Component Analysis

  • Choi, Hyun-Chul;Oh, Se-Young
    • 제어로봇시스템학회:학술대회논문집
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    • pp.185-189
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    • 2004
  • For face recognition system, a face detector which can find exact face region from complex image is needed. Many face detection algorithms have been developed under the assumption that background of the source image is quite simple . this means that face region occupy more than a quarter of the area of the source image or the background is one-colored. Color-based face detection is fast but can't be applicable to the images of which the background color is similar to face color. And the algorithm using neural network needs so many non-face data for training and doesn't guarantee general performance. In this paper, A multi-scale, multi-face detection algorithm using PCA is suggested. This algorithm can find most multi-scaled faces contained in static images with small number of training data in reasonable time.

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Driver's Face Detection Using Space-time Restrained Adaboost Method

  • Liu, Tong;Xie, Jianbin;Yan, Wei;Li, Peiqin
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.6 no.9
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    • pp.2341-2350
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    • 2012
  • Face detection is the first step of vision-based driver fatigue detection method. Traditional face detection methods have problems of high false-detection rates and long detection times. A space-time restrained Adaboost method is presented in this paper that resolves these problems. Firstly, the possible position of a driver's face in a video frame is measured relative to the previous frame. Secondly, a space-time restriction strategy is designed to restrain the detection window and scale of the Adaboost method to reduce time consumption and false-detection of face detection. Finally, a face knowledge restriction strategy is designed to confirm that the faces detected by this Adaboost method. Experiments compare the methods and confirm that a driver's face can be detected rapidly and precisely.

A study on Face Image Classification for Efficient Face Detection Using FLD

  • Nam, Mi-Young;Kim, Kwang-Baek
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.106-109
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    • 2004
  • Many reported methods assume that the faces in an image or an image sequence have been identified and localization. Face detection from image is a challenging task because of variability in scale, location, orientation and pose. In this paper, we present an efficient linear discriminant for multi-view face detection. Our approaches are based on linear discriminant. We define training data with fisher linear discriminant to efficient learning method. Face detection is considerably difficult because it will be influenced by poses of human face and changes in illumination. This idea can solve the multi-view and scale face detection problem poses. Quickly and efficiently, which fits for detecting face automatically. In this paper, we extract face using fisher linear discriminant that is hierarchical models invariant pose and background. We estimation the pose in detected face and eye detect. The purpose of this paper is to classify face and non-face and efficient fisher linear discriminant..

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Fast and Robust Face Detection based on CNN in Wild Environment (CNN 기반의 와일드 환경에 강인한 고속 얼굴 검출 방법)

  • Song, Junam;Kim, Hyung-Il;Ro, Yong Man
    • Journal of Korea Multimedia Society
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    • v.19 no.8
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    • pp.1310-1319
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    • 2016
  • Face detection is the first step in a wide range of face applications. However, detecting faces in the wild is still a challenging task due to the wide range of variations in pose, scale, and occlusions. Recently, many deep learning methods have been proposed for face detection. However, further improvements are required in the wild. Another important issue to be considered in the face detection is the computational complexity. Current state-of-the-art deep learning methods require a large number of patches to deal with varying scales and the arbitrary image sizes, which result in an increased computational complexity. To reduce the complexity while achieving better detection accuracy, we propose a fully convolutional network-based face detection that can take arbitrarily-sized input and produce feature maps (heat maps) corresponding to the input image size. To deal with the various face scales, a multi-scale network architecture that utilizes the facial components when learning the feature maps is proposed. On top of it, we design multi-task learning technique to improve detection performance. Extensive experiments have been conducted on the FDDB dataset. The experimental results show that the proposed method outperforms state-of-the-art methods with the accuracy of 82.33% at 517 false alarms, while improving computational efficiency significantly.

Face Detection Algorithm and Hardware Implementation for Auto Focusing Using Face Features in Skin Regions (AF를 위한 피부색 영역의 얼굴 특징을 이용한 Face Detection 알고리즘 및 하드웨어 구현)

  • Jeong, Hyo-Won;Kwak, Boo-Dong;Ha, Joo-Young;Han, Hag-Yong;Kang, Bong-Soon
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.13 no.12
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    • pp.2547-2554
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    • 2009
  • In this paper, we proposed a face detection algorithm and a hardware implementation method for ROI(Region Of Interest) of AF(Auto Focusing). We used face features in skin regions of YCbCr color space for face detection. The face features are the number of skin pixels in face regions, edge pixels in eye regions, and shadow pixels in lip regions. The each feature was statistically selected by 2,000 sample pictures of face. The proposed algorithm detects two faces that are closer center of the image for considering the effectiveness of hardware resource. The detected faces are displayed by rectangle for ROI of AF, and the rectangles are represented by positions in the image about starting point and ending point of the rectangles. The proposed face detection method was verified by using FPGA boards and mobile phone camera sensor.

Role of linking parameters in Pulse-Coupled Neural Network for face detection

  • Lim, Young-Wan;Na, Jin-Hee;Choi, Jin-Young
    • 제어로봇시스템학회:학술대회논문집
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    • pp.1048-1052
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    • 2004
  • In this work, we have investigated a role of linking parameter in Pulse-Coupled Neural Network(PCNN) which is suggested to explain the synchronous activities among neurons in the cat cortex. Then we have found a method to determine the linking parameter for a satisfactory face detection performance in a given color image. Face detection algorithm which uses the color information is independent on pose, size and obstruction of a face. But the use of color information encounters some problems arising from skin-tone color in the background, intensity variation within faces, and presence of random noise and so on. Depending on these conditions, PCNN's linking parameters should be selected an appropriate values. First we obtained the mean and variance of the skin-tone colors by experiments. Then, we introduced a preprocess that the pixel with a mean value of skin-tone colors has the highest level value (255) and the other pixels have values between 0 and 255 according to normal distribution with a variance. This preprocessing leads to an easy decision of the linking parameter of the Pulse-Coupled Neural Network. Through experiments, it is verified that the proposed method can improve the face detection performance compared to the existing methods.

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