Gait recognition using gait Gaussian image - IEEE Xplore

2015 2nd International Conference on Signal Processing and Integrated Networks (SPIN)

Gait Recognition Using Gait Gaussian Image Parul Arora

Smriti Srivastava

Netaji Subhas Inst. of Technology, New Delhi, India. [email protected]

Netaji Subhas Inst. of Technology, New Delhi, India. [email protected]

Abstract— In this paper, we proposed a new spatio- temporal based method for human gait recognition, named as Gait Gaussian Image (GGI). Gait Gaussian image is a period based gait technique, which is used for feature extraction of gait image over a gait cycle. The features derived from GGI are classified through Nearest neighbor method. Simulations and results are calculated on two benchmark datasets i.e. CASIA-B and Soton. Experimental results show the efficiency and effectiveness of the proposed method.

In this paper, a new gait cycle based approach has been proposed based on Gaussian membership function. This approach not only saves computation time, but also becomes less sensitive to noise in individual frames. The efficacy of the proposed representation has been demonstrated on two benchmark datasets, CASIA-B [16] and Soton small data set [17]. Nearest Neighbor classifier is used to identify humans through proposed features.

Keywords- Gait Gaussian image, Nearest neighbor, Gait cycle

I.

II.

INTRODUCTION

GAIT GAUSSIAN IMAGE (GGI) REPRESENTATION

From the last few decades, human recognition through gait has become a challenging field for the researchers in the applications like security and surveillance. Because of the ease it provides, while capturing the data, it has taken the front seat and has attracted the new researchers to explore it. However, gait has some limitations: It is affected by change in clothing and weight carrying conditions, but still its inherent characteristics make it versatile modality for identification and verification [1].

A. Motivation Some of the gait recognition techniques take the correlation between individual frames, without considering their order, while some techniques extract features from individual frames. Here keeping the order of the frames in mind, we have developed this technique.

Two different approaches have been used to study the gait identification: one is model based and other is model free. Model based methods capture the structural model of the human body, when in motion. Model based approaches [2, 3, 4, 5, 6, 7, 8] are found to be very complex, hence we have focused on model free approach here. Model free approach does not need any structure modelling, it only works on binary silhouettes. Many authors have attempted model free approaches [9, 10, 11] reported in literature.

B. Silhouette extraction In this paper, we have used CASIA-B and Soton small gait database, in which individual frames of a video sequence are provided. Now from each binary image, we need to extract the silhouette, the region of interest. So, First, we extract Region of interest (ROI) by calculating bounding box from the binarized image. This silhouette image is cropped according to position and size of the bounding box.

We have introduced fuzzy logic concept here because of its aptitude to deal with nonlinearities and uncertainties.

Next, we have normalized and centralized the ROI and scaled them into uniform size of 128 x 88, as shown in Fig. 1, where the left image is an original image and the right image is the final normalized and centralized, cropped image.

In model free approaches, some techniques are frame based and some techniques are gait cycle based. The proposed work is done on gait cycle based approach. In Frame based method, features are extracted from each frame of the video sequence, while in gait cycle based approach, features are extracted from a single image calculated over a gait cycle. Gait Energy Image (GEI) [12], Gait Entropy Image (GEnI) [13], Gait Flow Image (GFI) [14] and Gait Probability Image (GPI) [15] are examples of gait cycle based techniques.

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C. Gait Period extraction Gait cycle is one complete cycle from rest position to right foot forward to rest to left foot forward to again to rest position. Number of images in one gait cycle corresponds to gait period. This is an important step, as this work is period based. To calculate gait cycle, only lower half image is considered as it

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XN X1

X2

Fig. 1. Original image and cropped centralized image

carries the most dynamic information. To estimate the gait cycle, count of number of foreground pixels is used [1]. When the legs are far apart, count of foreground pixels is high, as compared to when the legs are overlapped. So count of each frame makes a periodical signal, which is shown in Fig. 2. The graph shows the relation between the number of foreground pixels for individual frames. By leaving every second peak out of three peaks, an estimation of the gait period is obtained.

Fig. 3. Rearrangements of frames over a gait cycle for feature extraction ଵ

ܽଵ ൌ σே ௜ୀଵ ‫ݔ‬௜ ߤ௜ (2) ே

In this way, all features (128 x 88=11264) are calculated over a gait cycle, which makes a single image. Fig. 4 shows an example of Gait Gaussian Image, which is made up from binary images (BI) of one gait cycle.

D. Feature Extraction As the proposed feature extraction technique is dependent upon the gait cycle approach, features are extracted from a single image (size 128 x 88) calculated over a gait cycle. Gait Gaussian Image (GGI) is computed for each pixel of the image over a complete cycle. As in one gait cycle, there are N numbers of frames,therefore for one pixel taken from each frame (x1,x2,x3,,,xi,….,xN), we get N number of pixels in a vector as shown in Fig. 3. Then fuzzification of these N pixels is done using Gaussian membership function, by considering mean and variance of the vector. Gaussian membership function i(x) is defined in (1) as:

III.

In this section, we have mainly described how to utilize Gait Gaussian Image (GGI) for person recognition. Nearest neighbor classifier is adopted to recognize the person. This classifier is found to be very simple and effective. It uses Euclidean distance to calculate the distance between GGI gallery sequence and GGI probe sequence. It classifies the probe sample to the category, which has minimum euclidean distance with the probe sample. In other words, it classifies the different subjects according to the minimum distance with the training sample. The result has been expressed in the terms of Correct Classification Rate (CCR), which is nothing, but the ratio of the number of correctly recognized persons (Nc) to the total number of persons (N), as shown in (3).

మ

ߤ௜ ሺ‫ݔ‬ሻ ൌ ݁

ഥ൯ ൫ೣ షೣ ି ೔ మ഑

PERSON IDENTIFICATION USING GGI

(1)

Where xi is the respective pixel of ith frame , ‫ݔ‬ҧ is the mean and ı is the variance of the pixel vector. Then, each membership value of a pixel is multiplied by its corresponding pixel and thus average of all the values gives a single value, as shown in (2).

‫ܴܥܥ‬ሺΨሻ ൌ

ே೎ ே

ൈ ͳͲͲ

(3)

IV. EXPERIMENTS AND DISCUSSIONS To show the performance of the proposed method, the analysis has been carried out on two datasets.

BI 1 Fig. 2. Variations of foreground pixels over individual frames

BI 10

BI 20

GGI

Fig. 4. Example of Gait Gaussian image (GGI) of 3rd person in Casia-B dataset

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One dataset is Casia-B and other one is soton small database. A. Dataset Casia-B database [16] is a multiview database and has 124 subjects. Three kinds of variations are included in the dataset namely viewing angle, clothing variation and carrying condition. But to compare with earlier developed features’ performance, we have considered only lateral (90º) view and normal clothing. For training and testing, four sequences of normal walking data are taken as gallery data and remaining two sequences of the same kind are taken as probe data. Soton small database [17] contains 11 subjects with many variations in clothing, carrying conditions and in walking directions. Two normal sequences are taken in to gallery set and one normal sequence is taken in to probe set. B. Results and discussion For purpose of comparison, the experiments also include the results of GEI [12] and GEnI [13]. As it has been established with GEI, that it represents human motion in a single image, while preserving temporal information. It is basically a time normalized accumulative energy image of human walking in complete cycle. In simple words, GEI [12] is calculated just by taking average of corresponding pixels over total number of frames in a cycle, which represents a sort of weighted centre of a distribution of data. GEnI [13] came up as a variant of GEI, computes entropy, while encoding the randomness of pixel valuesin the silhouette images over a complete gait cycle, in a single image. It captures mostly motion information by calculating Shannon entropy for each pixel in the silhouette images. The recognition rate in the terms of correct classification rate is expressed in table no. 1. The comparison is also presented through the CMC (cumulative match characteristic) graphs, which is used to evaluate the ranking capability of an identification system. CMC graph implies the probability that the correct match is a part of top n matches.

Fig. 5. CMC curve shows comparison between different features for CASIA-B Database

better or comparable results in comparison to other features. Actually in GGI, considering all the frames in the gait cycle, we compute Gaussian distribution of each pixel. Gaussian function uses two statistical parameters mean and variance.Variance measures the dispersion of data. It measure the amount of randomness around the mean. So unlike other methods, GGI uses both mean and variance to calculate the trained image, giving better results.

TABLE 1 COMPARISON OF DIFFERENT GAIT RECOGNITION ALGORITHMS ON CASIA-B AND SOTON DATABASE

Datasets

Recognition Accuracy (%) GEI

GEnI

GGI

Casia-B

94.0

95.0

98.0

Soton

100

100

100

Fig. 6. CMC curve shows comparison between different features for SOTON Database

V.

CONCLUSION

In this paper, we have proposed a novel technique based on Gaussian membership function, known as GGI ( Gait Gaussian Image). GGI is a gait period dependent image, which is calculated over a gait cycle. Given the gallery images and probe images, nearest neighbor classifier is used to measure Euclidean distance between Gaussian gait signnatures. The proposed features shows significant improvement in recognition results on Casia-B and Soton dataset, for normal walking conditions, over other methods existing in literature.

Figure 5 shows the CMC curve for Casia-B database. If we compare the rank 1 performance, we observe that GGI outperforms with 98% score, whereas GEI and GEnI achieves only 94% and 95% scores respectively. Figure 6 shows the CMC graph for SOTON database. Here, GGI achieves 100% score , which is comparable to the previous results. From the experiments, we notice that, GGI (Gait Gaussian image) gives

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REFERENCES [1] S. Sarkar, P. J. Phillips, Z. Liu, I. R. Vega, P. Grother and K. W. Bowyer, "The humanID gait challenge problem: datasets, performance, and analysis," IEEE Trans. on Pattern Anal. Mach. Intell., vol. 27, no. 2, pp. 162-177, 2005. [2] S. X. Yang, P. K. Larsen, T. Alkjær, E. B. Simonsen and N. Lynnerup, "Variability and similarity of gait as evaluated by joint angles: implications for forensic gait analysis," J. Forensic Sci., vol. 59, no. 2, pp. 494-504, 2014. [3] J. Preis, M. Kessel, M. Werner and C. Linnhoff-Popien, "Gait recognition with kinect," in 1st Int. Workshop Kinect in Pervasive Computing, Newcastle, UK, 18th June, 2012. [4] F. Tafazzoli and R. Safabakhsh, "Model-based human gait recognition using leg and," Eng. Appl. Artif. Intell., vol. 23, pp. 1237-1246, 2010. [5] G. Ariyanto and M. S. Nixon, "Marionette mass-spring model for 3d gait biometrics," in 5th IAPR Int.l Conf. on Biometrics, New Delhi, 29th March1st April, 2012. [6] R. Tanawongsuwan and A. Bobick, "Gait recognition from time normalized joint-angle trajectories in the walking plane," in IEEE Conf. Computer Vision and Pattern Recognition, vol.2, pp. 726-731, 8th-14th Dec. 2001. [7] C. Yam, M. Nixon and J. Carter, "Gait recognition by walking and running: A model-based approach," in Asia Conf. on computer vision, Melbourne, Australia, 2002. [8] B. Bhanu and J. Han, "Individual recognition by kinematic-based gait analysis," in Int. Conf. Pattern Recognition, 2002. [9] A. Kale, A. Sundaresan, A. N. Rajagopalan, N. P. Cuntoor, A. K. RoyChowdhury, V. Kruger and R. Chellappa, "Identification of humans using gait," IEEE Trans. on Image Processing, vol. 13, no. 9, pp. 1163-1173, 2004. [10] W. Kusakunniran, Q. Wu, J. Zhang and H. Li, "“Gait recognition across various walking speeds using higher order shape configuration based on a diffrential composition model," IEEE Trans. on System, Man & Cybern. B, , vol. 42, no. 6, pp. 1654-1668, 2012. [11] J. Little and J. Boyd, "Recognizing people by their gait: The shape of motion," Videre: Journal of Computer Vision Research , vol. 1, no. 2, pp. 1-32, 1998. [12] J. Han and B. Bhanu, "Individual recognition using gait energy image," IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 2, pp. 316-322, 2006. [13] K. Bashir, T. Xiang and S. Gong, "Gait recognition using gait entropy image," in 3rd Int. Conf. Imaging for Crime Detection and Prevention,IET, 2009. [14] T. H. Lam, K. H. Cheung and J. N. Liu, "Gait flow image: A silhouettebased gait representation for human identification," Pattern Recognition , vol. 44, no. 4, pp. 973-987, 2011. [15] C. P. Lee, A. W. Tan and S. C. Tan, "Gait probability image: an information-theoretic model of gait representation," Vis. Commun. image representation, 2014. [16] S. Yu, D. Tan and T. Tan, "A Framework for Evaluating the Effect of View Angle, Clothing and Carrying Condition on Gait Recognition," in In Proceedings of IEEE 18th International Conference on Pattern Recognition (ICPR), Hong Kong, pp. 441-444, 20th-24th Aug.,2006. [17] J. Shutler, M. Grant, M. S. Nixon and J. N. Carter, "On a large sequence based human gait database," in in Proceedings of the 4th International Conference on Recent Advances in Soft Computing, Nottingham,UK, 2002.

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