• Title, Summary, Keyword: Pose Recognition

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Pose-normalized 3D Face Modeling for Face Recognition

  • Yu, Sun-Jin;Lee, Sang-Youn
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.35 no.12C
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    • pp.984-994
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    • 2010
  • Pose variation is a critical problem in face recognition. Three-dimensional(3D) face recognition techniques have been proposed, as 3D data contains depth information that may allow problems of pose variation to be handled more effectively than with 2D face recognition methods. This paper proposes a pose-normalized 3D face modeling method that translates and rotates any pose angle to a frontal pose using a plane fitting method by Singular Value Decomposition(SVD). First, we reconstruct 3D face data with stereo vision method. Second, nose peak point is estimated by depth information and then the angle of pose is estimated by a facial plane fitting algorithm using four facial features. Next, using the estimated pose angle, the 3D face is translated and rotated to a frontal pose. To demonstrate the effectiveness of the proposed method, we designed 2D and 3D face recognition experiments. The experimental results show that the performance of the normalized 3D face recognition method is superior to that of an un-normalized 3D face recognition method for overcoming the problems of pose variation.

Effective Pose-based Approach with Pose Estimation for Emotional Action Recognition (자세 예측을 이용한 효과적인 자세 기반 감정 동작 인식)

  • Kim, Jin Ok
    • KIPS Transactions on Software and Data Engineering
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    • v.2 no.3
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    • pp.209-218
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    • 2013
  • Early researches in human action recognition have focused on tracking and classifying articulated body motions. Such methods required accurate segmentation of body parts, which is a sticky task, particularly under realistic imaging conditions. Recent trends of work have become popular towards the use of more and low-level appearance features such as spatio-temporal interest points. Given the great progress in pose estimation over the past few years, redefined views about pose-based approach are needed. This paper addresses the issues of whether it is sufficient to train a classifier only on low-level appearance features in appearance approach and proposes effective pose-based approach with pose estimation for emotional action recognition. In order for these questions to be solved, we compare the performance of pose-based, appearance-based and its combination-based features respectively with respect to scenario of various emotional action recognition. The experiment results show that pose-based features outperform low-level appearance-based approach of features, even when heavily spoiled by noise, suggesting that pose-based approach with pose estimation is beneficial for the emotional action recognition.

Face Recognition Robust to Pose Variations (포즈 변화에 강인한 얼굴 인식)

  • 노진우;문인혁;고한석
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.41 no.5
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    • pp.63-69
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    • 2004
  • This paper proposes a novel method for achieving pose-invariant face recognition using cylindrical model. On the assumption that a face is shaped like that of a cylinder, we estimate the object's pose and then extract the frontal face image via a pose transform with previously estimated pose angle. By employing the proposed pose transform technique we can increase the face recognition performance using the frontal face images. Through representative experiments, we achieved an increased recognition rate from 61.43% to 94.76% by the pose transform. Additionally, the recognition rate with the proposed method achieves as good as that of the more complicated 3D face model.

Design of Face Recognition and Tracking System by Using RBFNNs Pattern Classifier with Object Tracking Algorithm (RBFNNs 패턴분류기와 객체 추적 알고리즘을 이용한 얼굴인식 및 추적 시스템 설계)

  • Oh, Seung-Hun;Oh, Sung-Kwun;Kim, Jin-Yul
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.64 no.5
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    • pp.766-778
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    • 2015
  • In this paper, we design a hybrid system for recognition and tracking realized with the aid of polynomial based RBFNNs pattern classifier and particle filter. The RBFNN classifier is built by learning the training data for diverse pose images. The optimized parameters of RBFNN classifier are obtained by Particle Swarm Optimization(PSO). Testing data for pose image is used as a face image obtained under real situation, where the face image is detected by AdaBoost algorithm. In order to improve the recognition performance for a detected image, pose estimation as preprocessing step is carried out before the face recognition step. PCA is used for pose estimation, the pose of detected image is assigned for the built pose by considering the featured difference between the previously built pose image and the newly detected image. The recognition of detected image is performed through polynomial based RBFNN pattern classifier, and if the detected image is equal to target for tracking, the target will be traced by particle filter in real time. Moreover, when tracking is failed by PF, Adaboost algorithm detects facial area again, and the procedures of both the pose estimation and the image recognition are repeated as mentioned above. Finally, experimental results are compared and analyzed by using Honda/UCSD data known as benchmark DB.

Design of Robust Face Recognition System to Pose Variations Based on Pose Estimation : The Comparative Study on the Recognition Performance Using PCA and RBFNNs (포즈 추정 기반 포즈변화에 강인한 얼굴인식 시스템 설계 : PCA와 RBFNNs 패턴분류기를 이용한 인식성능 비교연구)

  • Ko, Jun-Hyun;Kim, Jin-Yul;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.64 no.9
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    • pp.1347-1355
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    • 2015
  • In this study, we compare the recognition performance using PCA and RBFNNs for introducing robust face recognition system to pose variations based on pose estimation. proposed face recognition system uses Honda/UCSD database for comparing recognition performance. Honda/UCSD database consists of 20 people, with 5 poses per person for a total of 500 face images. Extracted image consists of 5 poses using Multiple-Space PCA and each pose is performed by using (2D)2PCA for performing pose classification. Linear polynomial function is used as connection weight of RBFNNs Pattern Classifier and parameter coefficient is set by using Particle Swarm Optimization for model optimization. Proposed (2D)2PCA-based face pose classification performs recognition performance with PCA, (2D)2PCA and RBFNNs.

Developing Interactive Game Contents using 3D Human Pose Recognition (3차원 인체 포즈 인식을 이용한 상호작용 게임 콘텐츠 개발)

  • Choi, Yoon-Ji;Park, Jae-Wan;Song, Dae-Hyeon;Lee, Chil-Woo
    • The Journal of the Korea Contents Association
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    • v.11 no.12
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    • pp.619-628
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    • 2011
  • Normally vision-based 3D human pose recognition technology is used to method for convey human gesture in HCI(Human-Computer Interaction). 2D pose model based recognition method recognizes simple 2D human pose in particular environment. On the other hand, 3D pose model which describes 3D human body skeletal structure can recognize more complex 3D pose than 2D pose model in because it can use joint angle and shape information of body part. In this paper, we describe a development of interactive game contents using pose recognition interface that using 3D human body joint information. Our system was proposed for the purpose that users can control the game contents with body motion without any additional equipment. Poses are recognized comparing current input pose and predefined pose template which is consist of 14 human body joint 3D information. We implement the game contents with the our pose recognition system and make sure about the efficiency of our proposed system. In the future, we will improve the system that can be recognized poses in various environments robustly.

Design of Face Recognition System Based on Pose Estimation : Comparative Studies of Pose Estimation Algorithms (포즈 추정 기반 얼굴 인식 시스템 설계 : 포즈 추정 알고리즘 비교 연구)

  • Kim, Jin-Yul;Kim, Jong-Bum;Oh, Sung-Kwun
    • The Transactions of The Korean Institute of Electrical Engineers
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    • v.66 no.4
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    • pp.672-681
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    • 2017
  • This paper is concerned with the design methodology of face recognition system based on pose estimation. In 2-dimensional face recognition, the variations of facial pose cause the deterioration of recognition performance because object recognition is carried out by using brightness of each pixel on image. To alleviate such problem, the proposed face recognition system deals with Learning Vector Quantizatioin(LVQ) or K-Nearest Neighbor(K-NN) to estimate facial pose on image and then the images obtained from LVQ or K-NN are used as the inputs of networks such as Convolution Neural Networks(CNNs) and Radial Basis Function Neural Networks(RBFNNs). The effectiveness and efficiency of the post estimation using LVQ and K-NN as well as face recognition rate using CNNs and RBFNNs are discussed through experiments carried out by using ICPR and CMU PIE databases.

Invariant Range Image Multi-Pose Face Recognition Using Fuzzy c-Means

  • Phokharatkul, Pisit;Pansang, Seri
    • 제어로봇시스템학회:학술대회논문집
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    • pp.1244-1248
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    • 2005
  • In this paper, we propose fuzzy c-means (FCM) to solve recognition errors in invariant range image, multi-pose face recognition. Scale, center and pose error problems were solved using geometric transformation. Range image face data was digitized into range image data by using the laser range finder that does not depend on the ambient light source. Then, the digitized range image face data is used as a model to generate multi-pose data. Each pose data size was reduced by linear reduction into the database. The reduced range image face data was transformed to the gradient face model for facial feature image extraction and also for matching using the fuzzy membership adjusted by fuzzy c-means. The proposed method was tested using facial range images from 40 people with normal facial expressions. The output of the detection and recognition system has to be accurate to about 93 percent. Simultaneously, the system must be robust enough to overcome typical image-acquisition problems such as noise, vertical rotated face and range resolution.

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A New 3D Active Camera System for Robust Face Recognition by Correcting Pose Variation

  • Kim, Young-Ouk;Jang, Sung-Ho;Park, Chang-Woo;Sung, Ha-Gyeong;Kwon, Oh-Yun;Paik, Joon-Ki
    • 제어로봇시스템학회:학술대회논문집
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    • pp.1485-1490
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    • 2004
  • Recently, we have remarkable developments in intelligent robot systems. The remarkable features of intelligent robot are that it can track user, does face recognition and vital for many surveillance based systems. Advantage of face recognition when compared with other biometrics recognition is that coerciveness and contact that usually exist when we acquire characteristics do not exist in face recognition. However, the accuracy of face recognition is lower than other biometric recognition due to decrease in dimension from of image acquisition step and various changes associated with face pose and background. Factors that deteriorate performance of face recognition are many such as distance from camera to face, lighting change, pose change, and change of facial expression. In this paper, we implement a new 3D active camera system to prevent various pose variation that influence face recognition performance and propose face recognition algorithm for intelligent surveillance system and mobile robot system.

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Pose Recognition of Soccer Players for Three Dimensional Animation (방송 축구 영상으로부터 3차원 애니메이션 변환을 위한 축구 선수 동작 인식)

  • 장원철;남시욱;김재희
    • Proceedings of the IEEK Conference
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    • pp.33-36
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    • 2000
  • To create a more realistic soccer game derived from TV images, we are developing an image synthesis system that generates 3D image sequence from TV images. We propose the method for the team and the pose recognition of players in TV images. The representation includes camera calibration method, team recognition method and pose recognition method. To find the location of a player on the field, a field model is constructed and a player's field position is transformed by 4-feature points. To recognize the team information of players, we compute RGB mean values and standard deviations of a player in TV images. Finally, to recognize pose of a player, this system computes the velocity and the ratio of player(height/width). Experimental results are included to evaluate the performance of the team and the pose recognition.

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