• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.9
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• pp.39-48
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• 2009

Human face tracking has gradually become an important issue in applications for portable multimedia devices such as digital camcorder, digital still camera and cell phone. Current embedded face tracking software implementations lack the processing abilities to track faces in real time mobile video processing. In this paper, we propose a power efficient hardware-based face tracking architecture operating in real time. The proposed system was verified by FPGA prototyping and ASIC implementation using Samsung 65nm CMOS process. The implementation result shows that tracking speed is less than 8.4 msec with 150K gates and 20 mW average power consumption. Consequently it is validated that the proposed system is adequate for portable multimedia device.

• Journal of information and communication convergence engineering
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• v.11 no.3
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• pp.207-215
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• 2013

In this paper, we address the challenging computer vision problem of obtaining a reliable facial expression analysis from a naturally interacting person. We propose a system that combines a 3D generic face model, 3D head tracking, and 2D tracker to track facial landmarks and recognize expressions. First, we extract facial landmarks from a neutral frontal face, and then we deform a 3D generic face to fit the input face. Next, we use our real-time 3D head tracking module to track a person's head in 3D and predict facial landmark positions in 2D using the projection from the updated 3D face model. Finally, we use tracked 2D landmarks to update the 3D landmarks. This integrated tracking loop enables efficient tracking of the non-rigid parts of a face in the presence of large 3D head motion. We conducted experiments for facial expression recognition using both framebased and sequence-based approaches. Our method provides a 75.9% recognition rate in 8 subjects with 7 key expressions. Our approach provides a considerable step forward toward new applications including human-computer interactions, behavioral science, robotics, and game applications.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.3
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• pp.162-170
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• 2014

Appearance-based subspace models such as eigenfaces have been widely recognized as one of the most successful approaches to face recognition and tracking. The success of eigenfaces mainly has its origins in the benefits offered by principal component analysis (PCA), the representational power of the underlying generative process for high-dimensional noisy facial image data. The sparse extension of PCA (SPCA) has recently received significant attention in the research community. SPCA functions by imposing sparseness constraints on the eigenvectors, a technique that has been shown to yield more robust solutions in many applications. However, when SPCA is applied to facial images, the time and space complexity of PCA learning becomes a critical issue (e.g., real-time tracking). In this paper, we propose a very fast and scalable greedy forward selection algorithm for SPCA. Unlike a recent semidefinite program-relaxation method that suffers from complex optimization, our approach can process several thousands of data dimensions in reasonable time with little accuracy loss. The effectiveness of our proposed method was demonstrated on real-world face recognition and tracking datasets.

• The KIPS Transactions:PartB
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• v.16B no.5
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• pp.385-394
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• 2009

Face tracking and recognition are difficult problems because the face is a non-rigid object. The main reasons for the failure to track and recognize the faces are the changes of a face pose and environmental illumination. To solve these problems, we propose a nonlinear manifold framework for the face pose and the face illumination normalization processing. Specifically, to track and recognize a face on the video that has various pose variations, we approximate a face pose density to single Gaussian density by PCA(Principle Component Analysis) using images sampled from training video sequences and then construct the GMM(Gaussian Mixture Model) for each person. To solve the illumination problem for the face tracking and recognition, we decompose the face images into the reflectance and the illuminance using the SSR(Single Scale Retinex) model. To obtain the normalized reflectance, the reflectance is rescaled by histogram equalization on the defined range. We newly approximate the illuminance by the trained manifold since the illuminance has almost variations by illumination. By combining these two features into our manifold framework, we derived the efficient face tracking and recognition results on indoor and outdoor video. To improve the video based tracking results, we update the weights of each face pose density at each frame by the tracking result at the previous frame using EM algorithm. Our experimental results show that our method is more efficient than other methods.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.714-718
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• 2009

3D reconstruction of a human face from an image sequence remains an important problem in computer vision. We propose a method, based on a factorization algorithm, that reconstructs a 3D face model from short image sequences exhibiting rotational motion. Factorization algorithms can recover structure and motion simultaneously from one image sequence, but they usually require that all feature points be well tracked. Under rotational motion, however, feature tracking often fails due to occlusion and frame out of features. Additionally, the paucity of images may make feature tracking more difficult or decrease reconstruction accuracy. The proposed 3D reconstruction approach can handle short image sequences exhibiting rotational motion wherein feature points are likely to be missing. We implement the proposal as a reconstruction method; it employs image sequence division and a feature tracking method that uses Active Appearance Models to avoid the failure of feature tracking. Experiments conducted on an image sequence of a human face demonstrate the effectiveness of the proposed method.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.615-618
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• 2002

This paper proposes a real-time face detection and tracking a method in complex backgrounds. The proposed method is based on the principal component analysis (PCA) technique. For the detection of a face, first, we use a skin color model and motion information. And then using the PCA technique the detected regions are verified to determine which region is indeed the face. The tracking of a face is based on the Euclidian distance in eigenspace between the previously tracked face and the newly detected faces. Camera control for the face tracking is done in such a way that the detected face region is kept on the center of the screen by controlling the pan/tilt platform. The proposed method is extensible to other systems such as teleconferencing system, intruder inspection system, and so on.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.9
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• pp.1825-1838
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• 2012

This paper proposes a fast face detection and tracking method which uses depth images as well as RGB images. It consists of the face detection procedure and the face tracking procedure. The face detection method basically uses an existing method, Adaboost, but it reduces the size of the search area by using the depth information and skin color. The proposed face tracking method uses a template matching technique and incorporates an early-termination scheme to reduce the execution time further. The results from implementing and experimenting the proposed methods showed that the proposed face detection method takes only about 39% of the execution time of the existing method. The proposed tracking method takes only 2.48ms per frame. For the exactness, the proposed detection method and previous method showed a same detection ratio but in the error ratio, which is about 0.66%, the proposed method showed considerably improved performance. In all the cases except a special one, the tracking error ratio is as low as about 1%. Therefore, we expect the proposed face detection and tracking methods can be used individually or in combined for many applications that need fast execution and exact detection or tracking.

• Journal of KIISE:Information Networking
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• v.29 no.1
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• pp.97-107
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• 2002

In general, under interactive broadcasting environment, user finds additional service using top-down menu. However, user can't know that additional service provides information until retrieval has finished and top-down menu requires multi-level retrieval. This paper proposes the new method for additional service providing not using top-down menu but using object selection. For the purpose of this method, the movie of a MPEG should be synchronized with the object information(position, size, shape) and object tracking technique is required. Synchronization technique uses the Directshow provided by the Microsoft. Object tracking techniques use a motion-based tracking and a model-based tracking together. We divide object into two parts. One is face and the other is substance. Face tracking uses model-based tracking and Substance uses motion-based tracking base on the block matching algorithm. To improve precise tracking, motion-based tracking apply the temporal prediction search algorithm and model-based tracking apply the face model which merge ellipse model and color model.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.49.1-49
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• 2001

The skin color model is a very important concept in face detection, face recognition and face tracking. Usually, this model is obtained by estimating a probability density function of skin color distribution. In many cases, it is assumed that the underlying density function follows a Gaussian distribution. In this paper, a new method for non-parametric estimation of the probability density function, by using feed-forward neural network, is used to estimate the underlying skin color model. By using this method, the resulting skin color model is better than the Gaussian estimation and substantially approaches the real distribution. Applications to face detection and face ...

• Journal of Korea Multimedia Society
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• v.18 no.5
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• pp.610-619
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• 2015

*In this paper, we propose a real-time face tracking system using an adaptive face detector and a tracking algorithm. An image is divided into the regions of background and face candidate by a real-time updated skin color identifying system in order to accurately detect facial features. The facial characteristics are extracted using the five types of simple Haar-like features. The extracted features are reinterpreted by Principal Component Analysis (PCA), and the interpreted principal components are processed by Support Vector Machine (SVM) that classifies into facial and non-facial areas. The movement of the face is traced by Kalman filter and Mean shift, which use the static information of the detected faces and the differences between previous and current frames. The proposed system identifies the initial skin color and updates it through a real-time color detecting system. A similar background color can be removed by updating the skin color. Also, the performance increases up to 20% when the background color is reduced in comparison to extracting features from the entire region. The increased detection rate and speed are acquired by the usage of Kalman filter and Mean shift.