Young-Ouk Kim

Automatic face region tracking for highly accurate face recognition in unconstrained environments

We present a combined real-time face region tracking and highly accurate face recognition technique for an intelligent surveillance system. High-resolution face images are very important to achieving accurate identification of a human face. Conventional surveillance or security systems, however, usually provide poor image quality because they use only fixed cameras to record scenes passively. We have implemented a real-time surveillance system that tracks a moving face using four pan-tilt-zoom (PTZ) cameras. While tracking, the region-of-interest (ROI) can be obtained by using a low-pass filter and background subtraction with the PTZ. Color information in the ROI is updated to extract features for optimal tracking and zooming. FaceIt/sup /spl reg//, which is one of the most popular face recognition software packages, is evaluated and then used to recognize the faces from the video signal. Experimentation with real human faces showed highly acceptable results in the sense of both accuracy and computational efficiency.

Multi-Modal Human Verification Using Face and Speech

In this paper, we propose a personal verification method using both face and speech to improve the rate of single biometric verification. False acceptance rate (FAR) and false rejection rate (FRR) have been a fundamental bottleneck of real-time personal verification. The proposed multimodal biometric method is to improve both verification rate and reliability in real-time by overcoming technical limitations of single biometric verification methods. The proposed method uses principal component analysis (PCA) for face recognition and hidden markov model (HMM) for speech recognition. It also uses fuzzy logic for the final decision of personal verification. Based on experimental results, the proposed system can reduce FAR down to 0.0001%, which provides that the proposed method overcomes the limitation of single biometric system and provides stable personal verification in real-time.

Robust Feature Detection Using 2D Wavelet Transform under Low Light Environment

A novel local feature detection method is presented for mobile robot’s visual simultaneous localization and map building (v-SLAM). Camera-based visual localization can handle complicated problems, such as kidnapping and shadowing, which come with other type of sensors. Fundamental requirement of robust self-localization is robust key-point extraction under affine transform and illumination change. Especially, localization under low light environment is crucial for the purpose of guidance and navigation. This paper presents an efficient local feature extraction method under low light environment. A more efficient local feature detector and a compensation scheme of noise due to the low contrast images are proposed. The propose scene recognition method is robust against scale, rotation, and noise in the local feature space. We adopt the framework of scale-invariant feature transform (SIFT), where the difference of Gaussian (DoG)-based scale-invariant feature detection module is replaced by the difference of wavelet (DoW).

Pose-invariant face detection using edge-like blob map and fuzzy logic

We present an effective method of face and facial feature detection under pose variation in cluttered background. Our approach is flexible to both color and gray facial images and is also feasible for detecting facial features in quasi real-time. Based on the characteristics of neighborhood area of facial features, a new directional template for the facial feature is defined. By applying this template to the input facial image, novel edge-like blob map (EBM) with multiple strength intensity is constructed. And we propose an effective pose estimator using fuzzy logic and a simple PCA method. Combining these methods, robust face localization is achieved for face recognition in mobile robots. Experimental results using various color and gray images prove accuracy and usefulness of the proposed algorithm. This research was supported by Korea Ministry of Science and Technology under the National Research Laboratory project, by Korea Ministry of Education under the BK21 project, and by Korean Ministry of Information and Communication under HNRC-ITRC program at Chung-Ang university supervised by IITA.

Back-up Control of Truck-Trailer Vehicles with Practical Constraints: Computing Time Delay and Quantization

In this paper, we present implementation of backward movement control of truck-trailer vehicles using a fuzzy mode-based control scheme considering practical constraints and computational overhead. We propose a fuzzy feedback controller where output is predicted with the delay of a unit sampling period. Analysis and design of the proposed controller is very easy, because it is synchronized with sampling time. Stability analysis is also possible when quantization exists in the implementation of fuzzy control architectures, and we show that if the trivial solution of the fuzzy control system without quantization is asymptotically stable, then the solutions of the fuzzy control system with quantization are uniformly ultimately bounded. Experimental results using a toy truck show that the proposed control system outperforms a conventional system.

Robust and Adaptive Fuzzy Feedback Linearization Regulator Design

In this paper, we have proposed a new adaptive fuzzy control algorithm and the regulation problem based on Takagi-Sugeno fuzzy model. The regulation problem for the uncertain SISO nonlinear system is solved by the proposed algorithm. Using the advanced stability theory, the stability of the state, the control gain and the parameter choosing considering the approximation error due to the fuzzy modeling is proved. Some numerical examples are presented to verify the effectiveness of the proposed methods.

Robust stable feedback linearization of fuzzy modeled nonlinear systems via LMI's

This paper presents the robust stability analysis and design methodology of the fuzzy feedback linearization control systems. Uncertainty and disturbances with known bounds are assumed to be included in the Takagi-Sugeno (TS) fuzzy models representing the nonlinear plants. L/sub 2/ robust stability of the closed system is analyzed by casting the systems into the diagonal norm bounded linear differential inclusions (DNLDI) formulation. Based on the linear matrix inequality (LMI) optimization programming, a numerical method for finding the maximum stable ranges of the fuzzy feedback linearization control gains is also proposed. To verify the effectiveness of the proposed scheme, the robust stability analysis and control design examples are given.

Dynamic region-of-interest acquisition and face tracking for intelligent surveillance system

Recently, surveillance systems gain more attraction than simple CCTV systems, especially for complicated security environment. The major purpose of the proposed system is to monitor and track intruders. More specifically, accurate identification of each intruder is more important than simply recording what they are doing. Most existing surveillance systems simply keep recording the fixed viewing area, and some others adopt the tracking technique for wider coverage. Although panning and tilting the camera can extend the viewing area, only a few automatic zoom control techniques for acquiring the optimum ROI has been proposed. This paper describes a system for tracking multiple faces from input video sequences using facial convex hull-based facial segmentation and robust hausdorff distance. The proposed algorithm adapts skin color reference map in the YCbCr color space and hair color reference map in the RGB color space for classifying face region. Then, we obtain an initial face model with preprocessing and convex hull. For tracking, this algorithm computes displacement of the point set between frames using a robust hausdorff distance and the best possible displacement is selected. Finally, the initial face model is updated using the displacement. We provide experimental result to demonstrate the performance of the proposed tracking algorithm, which efficiently tracks rotating, and zooming faces as well as multiple faces in video sequences obtained from at CCD camera.

Multiple Face Segmentation and Tracking Based on Robust Hausdorff distance Matching

This paper describes a system for tracking multiple faces in an input video sequence using facial convex hull based facial segmentation and robust hausdorff distance. The algorithm adapts skin color reference map in YCbCr color space and hair color reference map in RGB color space for classifying face region. Then, we obtain an initial face model with preprocessing and convex hull. For tracking, this algorithm computes displacement of the point set between frames using a robust hausdorff distance and the best possible displacement is selected. Finally, the initial face model is updated using the displacement. We provide an example to illustrate the proposed tracking algorithm, which efficiently tracks rotating and zooming faces as well as existing multiple faces in video sequences obtained from CCD camera.