Chin Poo Lee

Gait recognition via optimally interpolated deformable contours

Gait as a biometric was inspired by the ability to recognize an acquaintance by his manner of walking even when seen at a distance. In this paper, we describe a novel Fourier descriptor based gait recognition method that models the periodic deformation of human contours. A new measure of similarity using the product of Fourier coefficients is proposed as a distance measure between closed curves. In order to maximize the similarity between subsequent closed curves, the assembly of contours in gait cycle is circularly shifted by a circular permutation matrix. Subsequently, an element-wise frame interpolation is correspondingly applied to produce length invariant gait signatures. The experiments on OU-ISIR gait database and CASIA gait database reveal promising recognition accuracy. The element-wise frame interpolation method is able to preserve temporal information even when the gait cycles change, and therefore offers a better robustness to slight variation in walking speed.

Short Communication: Gait probability image: An information-theoretic model of gait representation

In this paper, we propose a new probabilistic gait representation to characterize human walking for recognition by gait. The approach obtains the binomial distribution of every pixel in a gait cycle. Organizing the binomial distribution of all pixels in the gait image, we obtain the gait signature, which we denote as the Gait Probability Image (GPI). In the recognition stage, symmetric Kullback-Leibler divergence is used to measure the information theoretical distance between gait signatures. The experimental results reveal that GPI achieves promising recognition rates. Besides that, experiments on different walking speeds demonstrate that GPI is robust to slight variation in walking speed.

Comparative Study of Hu Moments and Zernike Moments in Object Recognition

There are lots of ways to perform object recognition. This paper is part of a project studying object recognition. The project is intended as a starting point to further learning about object recognition. Therefore, moment invariants are studied as a good starting point. Hu moment invariant methods and Zernike moment invariant methods are implemented and compared. Zernike moment invariants are shown to outperform Hu moment invariants.

Time-sliced averaged motion history image for gait recognition

In this paper, we propose a time-sliced averaged motion history image (TAMHI) alongside the histograms of oriented gradients (HOG) to generate gait signatures in a gait recognition problem. Building on the motion history image (MHI), TAMHI divides the gait cycle into several regular time windows to generate the same number of TAMHI composite images. HOG descriptors are then calculated on these composite images to obtain the gait signature. The time-slicing procedure to produce multi-composite images preserve more detailed transient information of gait cycles. Additionally, time-normalization also introduces gait length invariancy into the representation, hence, offering a better recognition rate to slight changes in walking speed.

Gait recognition with Transient Binary Patterns

Transient Binary Patterns of gait sequence.Inherently combines both appearance information and temporal information.Less sensitive to silhouette noise in individual frames. In this work, we present a combination of spatiotemporal approach and texture descriptors to extract the temporal patterns in gait cycles. Unlike most conventional methods that focus on spatial information while limiting temporal information captured, spatiotemporal methods preserve both spatial and temporal information. Inspired by the success of texture descriptors in face recognition, the proposed method likewise constructs texture descriptors of gait motion over time. For each gait cycle, the pixel-wise binary patterns along the temporal axis, referred to as the Transient Binary Patterns (TBP), is analyzed. These pixel-wise TBPs are then grouped into regional blocks from which we construct regional TBP histograms. These regional TBP histograms collectively form the global TBP histogram that represents both the distribution of temporal patterns and spatial location. Experimental results clearly show the superiority of the proposed approach over other considered methods.

Finger spelling recognition using neural network

Finger spelling is a way of communication by expressing words using hand signs in order to ensure deaf and dumb community can communicate with others effectively. Therefore, a system that can understand finger spelling is needed. As a result of that, this work is conducted to primarily develop a tutoring system for finger spelling. To develop a robust real-time finger spelling tutoring system, it is necessary to ensure the accuracy of the finger spelling recognition. Even though there are existing solutions available for a decade, but most of them are just focusing on improving accuracy rate without implementing their solutions as a complete tutoring system for finger spelling. Consequently, it inspires this research project to develop a tutoring system for finger spelling. Microsoft Kinect sensor is used to acquire color images and depth images of the finger spells. Depth images are used to perform segmentation on the color images. After that, the segmented images are used as input and pass into a two hidden layers backpropagation neural network for classification.

Minimal Redundancy Maximal Relevance Criterion-based Multi-biometric Feature Selection

Multimodal biometrics are always adopted to improve the recognition performance of single modality biometric systems. Besides introducing more discriminating power to the biometric system, integrating multiple modalities also leads to the curse of dimensionality problem. In this paper, we engage the minimal redundancy maximal relevance criterion to reduce the dimensionality of the feature vector. The minimal redundancy maximal relevance criterion is a feature selection criterion that aims to retain the most relevant elements while discarding the other redundant elements. Our experiments show that, with only 15% of the original feature length, minimal redundancy maximal relevance criterion-based features are able to perform similarly well or even better than the baseline results.