• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.350-353
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• 2013

In this paper, a hardware implementation of phase calculator for extracting 3D depth image from TOF(Time-Of-Flight) sensor is proposed. The designed phase calculator, which adopts redundant binary number systems and a pipelined architecture to improve throughput and speed, performs arctangent operation using vectoring mode of DCORDIC algorithm. Fixed-point MATLAB simulations are carried out to determine the optimized bit-widths and number of iteration. The designed phase calculator is verified by emulating the restoration of virtual 3D data using MATLAB/Simulink and FPGA-in-the-loop verification, and the estimated performance is about 7.5 Gbps at 469 MHz clock frequency.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.6
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• pp.39-45
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• 2017

Conventional Color-Based Face Recognition Methods are Sensitive to Illumination Changes, and there are the Possibilities of Forgery and Falsification so that it is Difficult to Apply to Various Industrial Fields. In This Paper, we propose a Face Recognition Method Based on LBP(Local Binary Pattern) using the Depth Images to Solve This Problem. Face Detection Method Using Depth Information and Feature Extraction and Matching Methods for Face Recognition are implemented, the Simulation Results show the Recognition Performance of the Proposed Method.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.131-139
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• 2016

Recognition of human motions has become a main area of computer vision due to its potential human-computer interface (HCI) and surveillance. Among those existing recognition techniques for human motions, head detection and tracking is basis for all human motion recognitions. Various approaches have been tried to detect and trace the position of human head in two-dimensional (2D) images precisely. However, it is still a challenging problem because the human appearance is too changeable by pose, and images are affected by illumination change. To enhance the performance of head detection and tracking, the real-time three-dimensional (3D) data acquisition sensors such as time-of-flight and Kinect depth sensor are recently used. In this paper, we propose an effective feature extraction method, called adaptive local binary pattern (ALBP), for depth image based applications. Contrasting to well-known conventional local binary pattern (LBP), the proposed ALBP cannot only extract shape information without texture in depth images, but also is invariant distance change in range images. We apply the proposed ALBP for head detection and tracking in depth images to show its effectiveness and its usefulness.

• Journal of Biomedical Engineering Research
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• v.19 no.2
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• pp.199-210
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• 1998

In this paper, we present a 3D reconstruction method of color volume data for a computerized human atlas. Binary volume rendering which takes the advantages of object-order ray traversal and run-length encoding visualizes 3D organs at an interactive speed in a general PC without the help of specific hardwares. This rendering method improves the rendering speed by simplifying the determination of the pixel value of an intermediate depth image and applying newly developed normal vector calculation method. Moreover, we describe the 3D boundary encoding that reduces the involved data considerably without the penalty of image quality. The interactive speed of the binary rendering and the storage efficiency of 3D boundary encoding will accelerate the development of the PC-based human atlas.

• ETRI Journal
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• v.35 no.3
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• pp.491-500
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• 2013

A connected component analysis from a binary image is a popular character segmentation method but occasionally fails to segment the characters owing to image noise and uneven illumination. A multimethod binarization scheme that incorporates two or more binary images is a novel solution, but selection of binarization methods has never been analyzed before. This paper reveals the best combination of binarization methods and parameters and presents an in-depth analysis of the multimethod binarization scheme for better character segmentation. We carry out an extensive quantitative evaluation, which shows a significant improvement over conventional single-method binarization methods. Experiment results of six binarization methods and their combinations with different test images are presented.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.2
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• pp.1-9
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• 2018

In this paper, we propose a face recognition method which is not sensitive to illumination change and prevents false recognition of photographs. The proposed method uses infrared and depth images at the same time, solves sensitivity of illumination change by infrared image, and prevents false recognition of two - dimensional image such as photograph by depth image. Face detection method using infrared and depth images simultaneously and feature extraction and matching method for face recognition are realized. Simulation results show that accuracy of face recognition is increased compared to conventional methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.3
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• pp.1390-1403
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• 2016

Facial expression recognition (FER) plays a very significant role in computer vision, pattern recognition, and image processing applications such as human computer interaction as it provides sufficient information about emotions of people. For video-based facial expression recognition, depth cameras can be better candidates over RGB cameras as a person's face cannot be easily recognized from distance-based depth videos hence depth cameras also resolve some privacy issues that can arise using RGB faces. A good FER system is very much reliant on the extraction of robust features as well as recognition engine. In this work, an efficient novel approach is proposed to recognize some facial expressions from time-sequential depth videos. First of all, efficient Local Binary Pattern (LBP) features are obtained from the time-sequential depth faces that are further classified by Generalized Discriminant Analysis (GDA) to make the features more robust and finally, the LBP-GDA features are fed into Hidden Markov Models (HMMs) to train and recognize different facial expressions successfully. The depth information-based proposed facial expression recognition approach is compared to the conventional approaches such as Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA) where the proposed one outperforms others by obtaining better recognition rates.

• Journal of Information Processing Systems
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• v.12 no.3
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• pp.468-488
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• 2016

The paper proposes a novel framework for 3D face verification using dimensionality reduction based on highly distinctive local features in the presence of illumination and expression variations. The histograms of efficient local descriptors are used to represent distinctively the facial images. For this purpose, different local descriptors are evaluated, Local Binary Patterns (LBP), Three-Patch Local Binary Patterns (TPLBP), Four-Patch Local Binary Patterns (FPLBP), Binarized Statistical Image Features (BSIF) and Local Phase Quantization (LPQ). Furthermore, experiments on the combinations of the four local descriptors at feature level using simply histograms concatenation are provided. The performance of the proposed approach is evaluated with different dimensionality reduction algorithms: Principal Component Analysis (PCA), Orthogonal Locality Preserving Projection (OLPP) and the combined PCA+EFM (Enhanced Fisher linear discriminate Model). Finally, multi-class Support Vector Machine (SVM) is used as a classifier to carry out the verification between imposters and customers. The proposed method has been tested on CASIA-3D face database and the experimental results show that our method achieves a high verification performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.3
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• pp.643-650
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• 2014

A hardware implementation of phase calculator for extracting 3D depth image from TOF(Time-Of-Flight) sensor is described. The designed phase calculator adopts redundant binary number systems and a pipelined architecture to improve throughput and speed. It performs arctangent operation using vectoring mode of DCORDIC(Differential COordinate Rotation DIgital Computer) algorithm. Fixed-point MATLAB simulations are carried out to determine the optimal bit-widths and number of iteration. The phase calculator has ben verified by FPGA-in-the-loop verification using MATLAB/Simulink. A test chip has been fabricated using a TSMC $0.18-{\mu}m$ CMOS process, and test results show that the chip functions correctly. It has 82,000 gates and the estimated throughput is 400 MS/s at 400Mhz@1.8V.

• Journal of the Korea Society of Computer and Information
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• v.19 no.3
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• pp.25-35
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• 2014

This paper proposes a method of boosting-based classification for the purpose of real-time face detection. The proposed method uses depth images to ensure strong performance of face detection in response to changes in lighting and face size, and uses the depth difference feature to conduct learning and recognition through the I-MCTBoost classifier. I-MCTBoost performs recognition by connecting the strong classifiers that are constituted from weak classifiers. The learning process for the weak classifiers is as follows: first, depth difference features are generated, and eight of these features are combined to form the weak classifier, and each feature is expressed as a binary bit. Strong classifiers undergo learning through the process of repeatedly selecting a specified number of weak classifiers, and become capable of strong classification through a learning process in which the weight of the learning samples are renewed and learning data is added. This paper explains depth difference features and proposes a learning method for the weak classifiers and strong classifiers of I-MCTBoost. Lastly, the paper presents comparisons of the proposed classifiers and the classifiers using conventional MCT through qualitative and quantitative analyses to establish the feasibility and efficiency of the proposed classifiers.