Jang-Hee Yoo

A Review on Video-Based Human Activity Recognition

This review article surveys extensively the current progresses made toward video-based human activity recognition. Three aspects for human activity recognition are addressed including core technology, human activity recognition systems, and applications from low-level to high-level representation. In the core technology, three critical processing stages are thoroughly discussed mainly: human object segmentation, feature extraction and representation, activity detection and classification algorithms. In the human activity recognition systems, three main types are mentioned, including single person activity recognition, multiple people interaction and crowd behavior, and abnormal activity recognition. Finally the domains of applications are discussed in detail, specifically, on surveillance environments, entertainment environments and healthcare systems. Our survey, which aims to provide a comprehensive state-of-the-art review of the field, also addresses several challenges associated with these systems and applications. Moreover, in this survey, various applications are discussed in great detail, specifically, a survey on the applications in healthcare monitoring systems.

Gender classification in human gait using support vector machine

We describe an automated system that classifies gender by utilising a set of human gait data. The gender classification system consists of three stages: i) detection and extraction of the moving human body and its contour from image sequences; ii) extraction of human gait signature by the joint angles and body points; and iii) motion analysis and feature extraction for classifying gender in the gait patterns. A sequential set of 2D stick figures is used to represent the gait signature that is primitive data for the feature generation based on motion parameters. Then, an SVM classifier is used to classify gender in the gait patterns. In experiments, higher gender classification performances, which are 96% for 100 subjects, have been achieved on a considerably larger database.

Extracting human gait signatures by body segment properties

We describe a new method for extracting human gait signatures by topological analysis, using properties of body segments. The gait signature is extracted in three stages: extraction of the body contour by a thresholding and morphological filter; extraction of the leg angles based on regression analysis of contour data; finding the body points guided by known anatomical knowledge. A 2D stick figure is used to represent the human body model and trajectory-based kinematic features are extracted from the image sequences for describing and analyzing the gait motion. Also, the inherent periodicity in gait motion is analyzed by delay coordinates and a phase-space portrait. The utility of the proposed method is demonstrated in experiments, with comparison to medical data.

Automated Human Recognition by Gait using Neural Network

We describe a new method for recognizing humans by their gait using back-propagation neural network. Here, the gait motion is described as rhythmic and periodic motion, and a 2D stick figure is extracted from gait silhouette by motion information with topological analysis guided by anatomical knowledge. A sequential set of 2D stick figures is used to represent the gait signature that is primitive data for the feature extraction based on motion parameters. Then, a back-propagation neural network algorithm is used to recognize humans by their gait patterns. In experiments, higher gait recognition performances have been achieved.

A motion and similarity-based fake detection method for biometric face recognition systems

In this paper, a motion and similarity-based fake detection algorithm is presented for biometric face recognition systems. First, an input video is segmented as foreground and background regions. Second, the similarity is measured between a background region, i.e., a region without a face and upper body, and an original background region recorded at an initializing stage. Third, a background motion index is calculated to indicate the amount of motion in the background region compared with the motion in the foreground region. By combining the result of similarity and the result of background motion index, a fake video can be detected robustly with a regular USB camera.

Model-driven statistical analysis of human gait motion

We describe a new method for analyzing and extracting human gait motion by combining statistical methods with image processing. The periodic motion of human gait is modeled by trigonometric-polynomial interpolant functions. The gait description is derived by topological analysis guided by medical studies that selects areas from which joint angles are derived by regression analysis. Then, the interpolant functions are fitted to the gait data and whilst showing fidelity to earlier medical studies, also show recognition capability. As such, a new combination of medical knowledge, image processing and regression analysis can be used to label human motion in image sequences.

Face Recognition using Energy Probability in DCT Domain

In this paper, we propose a novel feature extraction method for face recognition. This method is based on discrete cosine transform (DCT), energy probability (EP), and linear discriminant analysis (LDA). We define an energy probability as magnitude of effective information. It is used to create a frequency mask in DCT domain. Our method consists of three steps. First, the spatial domain of face images is transformed into the frequency domain called DCT domain. Second, for energy probability is applied on DCT domain which acquires from face image, dimension reduction of data and optimization of valid information. At last, in order to obtain the most significant feature of face images, LDA is applied to the extracted data using frequency mask. Our experimental results show that the proposed method improves on the dimension reduction of feature space and the face recognition over the previously proposed methods

Biometrics Information Protection Using Fuzzy Vault Scheme

Biometric based authentication can provide strong security for identifying the users. In addition, the security of biometric data is important, because most biometric data is not changeable in a lifetime. However, cancellable biometrics can stores a non-invertible transformed version of the biometric data. Namely, the biometric data is safely remained even if the storage is compromised. The cryptographic construction, called fuzzy vault, has recently been proposed. The fuzzy vault aims to secure critical data with the biometric template in a way that only the authorized user can access the secret by providing the valid biometric. In this paper, we propose three solutions for fuzzy fingerprint vault. First is more efficient fuzzy fingerprint vault with automatic alignment. Second solution can be resistant to correlation attack. Third is fuzzy fingerprint vault for OTP (One Time Template).

Real-Time Face Verification for Mobile Platforms

We propose a novel method for real-time face verification on mobile platforms such as PDAs and cell-phones. To implement the real-time system, a fixed-point arithmetic is used, and the face components are extracted by using a fast boosting algorithm. In addition, an image reduction method is adapted by using a pre-calculated look-up table, and integral image calculation method is modified to reduce the processing time. Also, the efficient valid coefficients of a DCT transformed image in the face region are calculated, and the face features are extracted by using the EP-LDA methods. In experiments, the usefulness of the proposed method has been demonstrated on a smart phone with high face verification performances.

Design of Embedded Multimodal Biometric Systems

The embedded devices in biometrics have gained increasing attention due to the demand for reliable and cost-effective personal identification systems. However, the current available embedded devices are not suitable for the real-time implementation of a biometric application system because of the limited computational resource and memory space. In this paper, we describe the design of embedded biometric systems that identify person by using face-fingerprint or iris-fingerprint multimodal biometrics technology. To implement real-time system, the biometric algorithms are efficiently enhanced for fixed-point representation and optimized for memory and computational capacity. In addition, the most time consuming components of each biometric algorithm are implemented in a field programmable gate arrays (FPGA).