We present a system for identifying humans by their walking sounds. This problem is also known as acoustic gait recognition. The goal of the system is to analyse sounds emitted by walking persons (mostly the step sounds) and identify those persons. These sounds are characterised by the gait pattern and are influenced by the movements of the arms and legs, but also depend on the type of shoe. We extract cepstral features from the recorded audio signals and use hidden Markov models for dynamic classification. A cyclic model topology is employed to represent individual gait cycles. This topology allows to model and detect individual steps, leading to very promising identification rates. For experimental validation, we use the publicly available TUM GAID database, which is a large gait recognition database containing 3050 recordings of 305 subjects in three variations. In the best setup, an identification rate of 65.5 is achieved out of 155 subjects. This is a relative improvement of almost 30 compared to our previous work, which used various audio features and support vector machines.
The most-widespread approach for video-based gait recognition is the Gait Energy Image (GEI) @cite_14 , which is a simple silhouette-based approach. It can be combined with face recognition @cite_0 or with depth information @cite_17 . Furthermore, model-based approaches have been proposed for visual gait recognition @cite_16 . Besides using video or audio information, other methods to identify walking persons include using acoustic Doppler sonar @cite_1 or pressure sensors in the floor @cite_4 .
@cite_14 @cite_4 @cite_1 @cite_0 @cite_16 @cite_17
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Using gait recognition methods, people can be identified by the way they walk. The most successful and efficient of these methods are based on the Gait Energy Image (GEI). In this paper, we extend the traditional Gait Energy Image by including depth information. First, GEI is extended by calculating the required silhouettes using depth data. We then formulate a completely new feature, which we call the Depth Gradient Histogram Energy Image (DGHEI). We compare the improved depth-GEI and the new DGHEI to the traditional GEI. We do this using a new gait database which was recorded with the Kinect sensor. On this database we show significant performance gain of DGHEI. In this paper, we propose a new spatio-temporal gait representation, called Gait Energy Image (GEI), to characterize human walking properties for individual recognition by gait. To address the problem of the lack of training templates, we also propose a novel approach for human recognition by combining statistical gait features from real and synthetic templates. We directly compute the real templates from training silhouette sequences, while we generate the synthetic templates from training sequences by simulating silhouette distortion. We use a statistical approach for learning effective features from real and synthetic templates. We compare the proposed GEI-based gait recognition approach with other gait recognition approaches on USF HumanID Database. Experimental results show that the proposed GEI is an effective and efficient gait representation for individual recognition, and the proposed approach achieves highly competitive performance with respect to the published gait recognition approaches Many of the existing gait recognition approaches represent a gait cycle using a single 2D image called Gait Energy Image (GEI) or its variants. Since these methods suffer from lack of dynamic information, we model a gait cycle using a chain of key poses and extract a novel feature called Pose Energy Image (PEI). PEI is the average image of all the silhouettes in a key pose state of a gait cycle. By increasing the resolution of gait representation, more detailed dynamic information can be captured. However, processing speed and space requirement are higher for PEI than the conventional GEI methods. To overcome this shortcoming, another novel feature named as Pose Kinematics is introduced, which represents the percentage of time spent in each key pose state over a gait cycle. Although the Pose Kinematics based method is fast, its accuracy is not very high. A hierarchical method for combining these two features is, therefore, proposed. At first, Pose Kinematics is applied to select a set of most probable classes. Then, PEI is used on these selected classes to get the final classification. Experimental results on CMU's Mobo and USF's HumanID data set show that the proposed approach outperforms existing approaches. Gait Energy Image (GEI) has been proved to be an effective identity signature in gait recognition. But previous approaches only treat this 2D image representation as a holistic feature and neglect the intrinsic dynamic characteristics of gait patterns. In this paper, we use variation analysis to obtain the dynamic region in GEI which reflects the walking manner of an individual. Based on this analysis, a dynamics weight mask is constructed to enhance the dynamic region and suppress the noises on the unimportant regions. The obtained gait representation called enhanced GEI (EGEI) is then represented in low dimensional subspace by Gabor-based discriminative common vectors analysis. We test the proposed approach on the USF HumanID Gait Database. Experimental results prove its effectiveness in terms of recognition rate.