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Most of gait recognition algorithms involve walking cycle estimation to accomplish signature matching. However, we may be plagued by two cycle-related issues when developing real-time gait-based walker recognition systems. One is accurate cycle evaluation, which is computation intensive, and the other is the inconvenient acquisition of long continuous sequences of gait patterns, which are essential to the estimation of gait cycles. These drive us to address the problem of distant walker recognition from another view toward gait, in the hope of detouring the step of gait cycle estimation. This paper proposes a new gait representation, called normalized dual-diagonal projections (NDDP), to characterize walker signatures and employs a normal distribution to approximately describe the variation of each subject's gait signatures in the statistical sense. We achieve the recognition of unknown gait features in a simplified Bayes framework after reducing the dimension of raw gait signatures based on linear subspace projections. Extensive experiments demonstrate that our method is effective and promising. [ABSTRACT FROM AUTHOR]

Human identification at distance by analysis of gait patterns extracted from video has recently become very popular research in biometrics. This paper presents multi-projections based approach to extract gait patterns for human recognition. Binarized silhouette of a motion object is represented by 1-D signals which are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four projections to silhouette. Eigenspace transformation is applied to time-varying distance vectors and the statistical distance based supervised pattern classification is then performed in the lower-dimensional eigenspace for human identification. A fusion strategy developed is finally executed to produce final decision. Based on normalized correlation on the distance vectors, gait cycle estimation is also performed to extract the gait cycle. Experimental results on four databases demonstrate that the right person in top two matches 100% of the times for the cases where training and testing sets corresponds to the same walking styles, and in top three-four matches 100% of the times for training and testing sets corresponds to the different walking styles. [ABSTRACT FROM AUTHOR]

In this paper, a new method for gender recognition via gait silhouettes is proposed. In the feature extraction process, Radon transform on all the 180 angle degrees is applied to every silhouette to construct gait templates and the initial phase of each silhouette in an entire gait cycle is also associated to the templates representing dynamic information of walking. Then the Relevant Component Analysis (RCA) algorithm is employed on the radon-transformed templates to get a maximum likelihood estimation of the within class covariance matrix. At last, the Mahalanobis distances are calculated to measure gender dissimilarity in recognition. The Nearest Neighbor (NN) classifier is adopted to determine whether a sample in the Probe Set is male or female. Experimental results in comparison to state-of-the-art methods show considerable improvement in recognition performance of our proposed algorithm. [ABSTRACT FROM AUTHOR]

In these days, various foot problems have been rapidly increasing. Hallux valgus(HV), one of the most popular foot diseases, is found especially in mid-aged women. The most common cause of HV is to wear with small and pointed shoes for a long time. If HV is left untreated, the deformity in the other regions of the foot also follows. Even though many motion analyses and radiologic studies have been applied to various foot diseases, they do not provide quantitative information on the detailed motion of the foot. The purpose of this study is to analyze detailed foot motions of the normal and HV patients using a multi-segment foot model. Ten healthy adults, who have no disorders in the lower extremity, participated in three dimensional gait analysis. Twelve HV patients of Grade 2 were also chosen for the study. Results showed that HV patients exhibited relatively smaller flexion/extension, especially in terminal stance. An early abduction at the ankle during terminal stance was also noted in HV patients, which resulted in an excessive abduction in subtalar joint. In HV patients, medial MP joints exhibited excessive amount of adduction during terminal stance. In HV patients, hallux MP joint and talocrural joint exhibited excessive external rotation over the whole period of gait cycle. Understanding the detailed foot motions by the present multi-segment foot model would be very useful to diagnose and to treat patients with various foot diseases, and helpful for the design of foot orthoses. [ABSTRACT FROM AUTHOR]