• Title/Summary/Keyword: Face Detection

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Speeding Up Neural Network-Based Face Detection Using Swarm Search

  • Sugisaka, Masanori;Fan, Xinjian
    • 제어로봇시스템학회:학술대회논문집
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    • pp.1334-1337
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    • 2004
  • This paper presents a novel method to speed up neural network (NN) based face detection systems. NN-based face detection can be viewed as a classification and search problem. The proposed method formulates the search problem as an integer nonlinear optimization problem (INLP) and expands the basic particle swarm optimization (PSO) to solve it. PSO works with a population of particles, each representing a subwindow in an input image. The subwindows are evaluated by how well they match a NN-based face filter. A face is indicated when the filter response of the best particle is above a given threshold. To achieve better performance, the influence of PSO parameter settings on the search performance was investigated. Experiments show that with fine-adjusted parameters, the proposed method leads to a speedup of 94 on 320${\times}$240 images compared to the traditional exhaustive search method.

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Speaker Detection and Recognition for a Welfare Robot

  • Sugisaka, Masanori;Fan, Xinjian
    • 제어로봇시스템학회:학술대회논문집
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    • pp.835-838
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    • 2003
  • Computer vision and natural-language dialogue play an important role in friendly human-machine interfaces for service robots. In this paper we describe an integrated face detection and face recognition system for a welfare robot, which has also been combined with the robot's speech interface. Our approach to face detection is to combine neural network (NN) and genetic algorithm (GA): ANN serves as a face filter while GA is used to search the image efficiently. When the face is detected, embedded Hidden Markov Model (EMM) is used to determine its identity. A real-time system has been created by combining the face detection and recognition techniques. When motivated by the speaker's voice commands, it takes an image from the camera, finds the face inside the image and recognizes it. Experiments on an indoor environment with complex backgrounds showed that a recognition rate of more than 88% can be achieved.

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Face detection using haar-like feature and Tracking with Lucas-Kanade feature tracker (Haar-like feature를 이용한 얼굴 검출과 추적을 위한 Lucas-Kanade특징 추적)

  • Kim, Ki-Sang;Kim, Se-Hoon;Park, Gene-Yong;Choi, Hyung-Il
    • 한국HCI학회:학술대회논문집
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    • pp.835-838
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    • 2008
  • In this paper, we present automatic face detection and tracking which is robustness in rotation and translation. Detecting a face image, we used Haar-like feature, which is fast detect facial image. Also tracking, we applied Lucas-Kanade feature tracker and KLT algorithm, which has robustness for rotated facial image. In experiment result, we confirmed that face detection and tracking which is robustness in rotation and translation.

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Face Detection and Tracking using Skin Color Information and Haar-Like Features in Real-Time Video (실시간 영상에서 피부색상 정보와 Haar-Like Feature를 이용한 얼굴 검출 및 추적)

  • Kim, Dong-Hyeon;Im, Jae-Hyun;Kim, Dae-Hee;Kim, Tae-Kyung;Paik, Joon-Ki
    • 한국HCI학회:학술대회논문집
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    • pp.146-149
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    • 2009
  • Face detection and recognition in real-time video constitutes one of the recent topics in the field of computer vision. In this paper, we propose face detection and tracking algorithm using the skin color and haar-like feature in real-time video sequence. The proposed algorithm further includes color space to enhance the result using haar-like feature and skin color. Experiment results reveal the real-time video processing speed and improvement in the rate of tracking.

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A Face-Detection Postprocessing Scheme Using a Geometric Analysis for Multimedia Applications

  • Jang, Kyounghoon;Cho, Hosang;Kim, Chang-Wan;Kang, Bongsoon
    • JSTS:Journal of Semiconductor Technology and Science
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    • v.13 no.1
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    • pp.34-42
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    • 2013
  • Human faces have been broadly studied in digital image and video processing fields. An appearance-based method, the adaptive boosting learning algorithm using integral image representations has been successfully employed for face detection, taking advantage of the feature extraction's low computational complexity. In this paper, we propose a face-detection postprocessing method that equalizes instantaneous facial regions in an efficient hardware architecture for use in real-time multimedia applications. The proposed system requires low hardware resources and exhibits robust performance in terms of the movements, zooming, and classification of faces. A series of experimental results obtained using video sequences collected under dynamic conditions are discussed.

Face Detection Algorithm Using Pulse-Coupled Neural Network (Pulse-Coupled Neural Network를 이용한 얼굴추출 알고리즘)

  • Lim, Young-Wan;Na, Jin-Hee;Choi, Jin-Young
    • Proceedings of the KIEE Conference
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    • pp.105-107
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    • 2004
  • In this work, we suggested the method which improves the efficiency of the face detection algorithm using Pulse-Coupled Neural Network. Face detection algorithm which uses the color information is independent on size, angle, 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, we obtained the mean and variance of skin-tone colors by experiments. Then we introduce a preprocess that the pixel with a mean value of skin-tone colors has highest level value(255) and the other pixels in the skin-tone region have values between 0 and 255 according to a normal distribution with a variance. This preprocess leads to an easy decision of the linking parameters.

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A Study on the Performance Enhancement of Face Detection using SVM (SVM을 이용한 얼굴 검출 성능 향상에 대한 연구)

  • Lee Chi-Ceun;Jung Sung-Tae
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.9 no.2
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    • pp.330-337
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    • 2005
  • This paper proposes a method which improves the performance of face detection by using SVM(Support Vector Machine). first, it finds face region candidates by using AdaBoost based object detection method which selects a small number of critical features from a larger set. Next it classifies if the candidate is a face or non-face by using SVM(Support Vector Machine). Experimental results shows that the proposed method improve accuracy of face detection in comparison with existing method.

Performance Analysis of Viola & Jones Face Detection Algorithm (Viola & Jones 얼굴 검출 알고리즘의 성능 분석)

  • Oh, Jeong-su;Heo, Hoon
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.477-480
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    • 2018
  • Viola and Jones object detection algorithm is a representative face detection algorithm. The algorithm uses Haar-like features for face expression and uses a cascade-Adaboost algorithm consisting of strong classifiers, a linear combination of weak classifiers for classification. This algorithm requires several parameter settings for its implementation and the set values affect its performance. This paper analyzes face detection performance according to the parameters set in the algorithm.

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An Improved Genetic Algorithm for Fast Face Detection Using Neural Network as Classifier

  • Sugisaka, Masanori;Fan, Xinjian
    • 제어로봇시스템학회:학술대회논문집
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    • pp.1034-1038
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    • 2005
  • This paper presents a novel method to speed up neural network (NN) based face detection systems. NN-based face detection can be viewed as a classification and search problem. The proposed method formulates the search problem as an integer nonlinear optimization problem (INLP) and develops an improved genetic algorithm (IGA) to solve it. Each individual in the IGA represents a subwindow in an input image. The subwindows are evaluated by how well they match a NN-based face filter. A face is indicated when the filter response of the best particle is above a given threshold. Experimental results show that the proposed method leads to a speedup of 83 on $320{\times}240$ images compared to the traditional exhaustive search method.

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Face Detection for Interactive TV Control System in Near Infra-Red Images (인터랙티브 TV 컨트롤 시스템을 위한 근적외선 영상에서의 얼굴 검출)

  • Won, Chul-Ho
    • Journal of Sensor Science and Technology
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    • v.20 no.6
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    • pp.388-392
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    • 2011
  • In this paper, a face detection method for interactive TV control system using a new feature, edge histogram feature, with a support vector machine(SVM) in the near-infrared(NIR) images is proposed. The edge histogram feature is extracted using 16-directional edge intensity and a histogram. Compared to the previous method using local binary pattern(LBP) feature, the proposed method using edge histogram feature has better performance in both smaller feature size and lower equal error rate(EER) for face detection experiments in NIR databases.