Kanghun Jeong

Object Detection Using FAST Corner Detector Based on Smartphone Platforms

in this paper, we proposed a real-time object recognition system under smart phone environments. The proposed object recognition system consists of two key modules: feature extraction and object recognition. Feature detectors such as Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Feature (SURF) are good methods which yield high quality features, however they are too computationally intensive for use in real-time applications of any complexity. Compared to PC platforms, smart phone platforms have limited resources, so computation-intensive SIFT and SURF descriptors are less usable in such resource-limited environments. In this paper utilizes the FAST corner detector that provides faster feature computation by extracting only corner information. The number of corners detected by the FAST corner detector varies so normalization is applied to adjust the extracted corners (interest points) to the same number. Based on the normalized corner information, support vector machine (SVM) and back-propagation neural network (BPNN) training are performed for the efficient recognition of objects. Compared to conventional SIFT and SURF algorithms, the proposed object recognition system based on the FAST corner detector yields increased speed and low performance degradation on smart phones.

Efficient 3D Model based Face Representation and Recognition Algorithm using Pixel-to-Vertex Map (PVM)

A 3D model based approach for a face representation and recognition algorithm has been investigated as a robust solution for pose and illumination variation. Since a generative 3D face model consists of a large number of vertices, a 3D model based face recognition system is generally inefficient in computation time and complexity. In this paper, we propose a novel 3D face representation algorithm based on a pixel to vertex map (PVM) to optimize the number of vertices. We explore shape and texture coefficient vectors of the 3D model by fitting it to an input face using inverse compositional image alignment (ICIA) to evaluate face recognition performance. Experimental results show that the proposed face representation and recognition algorithm is efficient in computation time while maintaining reasonable accuracy.

Optimization of Face Recognition Algorithms for Ubiquitous Computing Environment

In this paper, we explore face recognition technology for embedded system. We develop an algorithm suitable for ubiquitous computing environment. The basic requirements include appropriate data format and ratio of feature data to achieve efficiency of algorithm. Our experiment presents a face recognition algorithm for handheld devices based on embedded system. The essential part of proposed system includes; integer representation from floating point calculation and optimization for memory management.

Authentication Performance Optimization for Smart-phone based Multimodal Biometrics

In this paper, we have proposed personal multimodal biometric authentication system based on face detection, recognition and speaker verification for smart-phone environment. Proposed system detect the face with Modified Census Transform algorithm then find the eye position in the face by using gabor filter and k-means algorithm. Perform preprocessing on the detected face and eye position, then we recognize with Linear Discriminant Analysis algorithm. Afterward in speaker verification process, we extract the feature from the end point of the speech data and Mel Frequency Cepstral Coefficient. We verified the speaker through Dynamic Time Warping algorithm because the speech feature changes in real-time. The proposed multimodal biometric system is to fuse the face and speech feature (to optimize the internal operation by integer representation) for smart-phone based real-time face detection, recognition and speaker verification. As mentioned the multimodal biometric system could form the reliable system by estimating the reasonable performance.

Breast Image Registration of 3D Suface-Point Using Iterative Closet Point (ICP) Method

Breast cancer is a cancer which occurs commonly for women of the entire world. [9] In this thesis, in order to find cancer by using image conformation technology, computer tomography (CT), positron emission tomography (PET) and magnetic resonance imaging (MRI) images were used. For conforming the images of each the different modalities, surface points, which become common feature of MRI and CT images were extracted and they were registered in 3D by using iterative closet point. (ICP)

3D Face Representation Using Inverse Compositional Image Alignment for Multimodal Face Recognition

A 3D model based approach for a face representation and recognition algorithm has been investigated as a robust solution for pose and illumination variation compared to 2D face recognition system. However, a 3D model based face recognition system is generally inefficient in computation time and complexity. In this paper, we propose a 3D face representation algorithm to optimize to have the same vertex number. Then, create an average model using processed 3D data. Finally, we evaluate fitting and face recognition performance based on 3D average model.

3D Model Based Face Recognition Using Inverse Compositional Image Alignment

3D model based approach for face recognition has been investigated as a robust solution for pose and illumination variation. Since a generative 3D face model consists of a large number of vertices, a 3D model based face recognition system is generally inefficient in computation time and complexity. In this paper we propose a novel 3D face representation algorithm based on pixel to vertex map (PVM) to reduce number of vertices. We explore shape and texture coefficient vectors of the model by fitting it to an input face using inverse compositional image alignment (ICIA) to evaluate face recognition performance. Experimental results show that proposed face recognition system is efficient in computation time while maintaining reasonable accuracy.

Face Recognition Technology for Ubiquitous Computing Environment

In this paper, we explore face detection and face recognition algorithms for ubiquitous computing environment. We develop algorithms for application programming interface (API) suitable for embedded system. The basic requirements include appropriate data format and collection of feature data to achieve efficiency of algorithm. Our experiment presents a face detection and face recognition algorithm for handheld devices. The essential part for proposed system includes; integer representation from floating point calculation, optimization of memory management scheme and efficient face detection performance on complex background scene.