Gait Recognition using GEl and Pattern Trace Transform

Transcription

1 2012 NTERNATONAL SYMPOSUM ON NFORMATON TECHNOLOGY N MEDCNE AND EDUCATON Gait Recognition using GEl and Pattern Trace Transform Pomtep Theekhanont Electrical Engineering Graduate Program, Mahanakorn University of Technology, Bangkok, Thailand. Serge Miguet LRS LAB, Universite Lumiere Lyon 2, Lyon, France. Werasak Kurutach nformation Technology Department, Mahanakorn University of Technology, Bangkok, Thailand. ABSTRACT n this paper, gait recognition using Gait Energy mage (GEl) and pattern trace transform, is presented for human identification. The first step, gait recognition using GEl and pattern trace transform, used silhouette input. The gait period could be estimated by computing the median of the walkingstep width of the three consecutive maxima. Subsequently, we calculated the Gait Energy mages from the silhouettes. GEl was transformed to a trace transform image. The pattern trace transform images were calculated using a threshold from the trace transform image to develop the pattern trace transform, which was a significant difference. Finally, we used template matching for identification. The results of gait recognition using GEl and pattern trace transform show that the proposed technique is quite effective and can be developed for higher performance. ndex Terms- trace transform; gait recognition; Gait Energy mage, GEL 1. NTRODUCTON Gait recognition is used in many areas of research, such as medical, security, computer vision, pattern recognition and image-sequence processing because of its advantages. For example, it can capture low-resolution images; it can be performed at a distance, does not require contact and can allow the observer to hide. Gait recognition is a biometric method, which can be used to identify individuals at a certain level. However, in order to identify a certain individual from their walle We need to develop the proper method. There are several methods used for gait recognition, which can be divided into two groups: modelbased and model-free. n model-based gait recognition, data on body position, such as hip and knee position is used to constrnct the gait recognition. n the model-free method, a sequence of binary silhouette images is transformed into another gait representation. Previous gait recognition systems have used many methods. One commonly used method is to use a gait energy image (GEl) [6]; a spatiotemporal gait representation, which is designed to keep motion data. Our previous work, the gait recognition using shape trace transform [14] in TME2011, proposed the technique of gait recognition based on shape trace transform. The result of gait recognition is 77.5%. We found the problem, the speed of calculation was slow because all of the silhouettes were transformed to the trace transform images. This paper proposes a new gait recognition using GEl and pattern trace transform. The experimental results show that this method can be used in gait recognition and in its continued development. The second part of the paper concerns the related work of gait recognition. The third part presents the methodology which was used in our experiment. n the fourth part, we demonstrate our experimental results. The conclusion and future work are presented in the fifth part. 2. RELATED WORKS Wang et al. [1] proposed "Silhouette Analysis-Based Gait Recognition for Human dentification." They suggested using the transformation technique for reducing the dimensionality of the input space by unwrapping a 2D silhouette image and transforming it into aid time-varying distance signal. The result of the experiment in the CASA gait dataset A was 82.5%. Han et al. [6] proposed a model-free gait representation, called gait energy image (GEl). The result of the experiment in the CASA gait dataset A was 91.25%. Hong et al. [7] proposed "Fusion of Multiple Gait Cycles for Human dentification." They extracted two gait features, gait energy image (GEl) and motion silhouette images (MS) for gait recognition, using the outputs of the nearest neighbour classifiers. They were fused at the abstract level, based on majority voting. The result of experiment in the CASA gait dataset A was 95.0%. Zhang et al. [8] proposed "Robust Post-processing Strategy for Gait Silhouette." They used a robust post-processing strategy to refine the raw silhouettes. The Principal Component Analysis (PCA) and Normalized Euclidean Distance (NED) were used for the gait classification. The /$ EEE 936

2 result of the experiment m the CASA gait dataset A was 93.75%. Yi-Bo Li et al. [9] proposed "Gait Extraction and Recognition Based on Lower Leg and Ankle." They used their knowledge of human anatomy to extract the lower leg and foot area. The position of the ankle is the intersection curve of the lower leg and foot, and they used the least-square method to fit the angle sequence of the lower leg, normalized this information and used Discrete Cosine Transform to transform the Amplitude Angle sequences. Two kinds of optimal characteristics were matched using feature fusion strategies. The result of the experiment in the CASA gait dataset A was 87.5%, which showed that the lower leg and ankle were significant and effective in the gait feature. 3. GAT RECOGNTON SYSTEM n this section, we demonstrate the details of gait recogmtlon using GEl and pattern trace transform. Figure 1 shows an overview of the proposed gait recognition system. The input of the system is a series of binary silhouettes from each frame. Our system consists of the following steps (figure 1 ): a. Preprocessing - this process deletes some noise, crops the human body pictures and finds the gait period, using the maximum width of a human walking step; b. The binary silhouettes from one period are transformed into the gait energy image (GEl); c. GEl is used to calculate the trace transform images for each period; d. We find the pattern trace transforming images by threshholding the trace transform images; e. Template matching and Euclidean distance were used to match the pattern trace transform images. The details of each step are shown below: usually a fixed size to estimate the gait period. After that, noise from the silhouette image was removed, it was cropped and aligned. The gait period value was defined from the width of a human body. Subsequently, an average filter was used to filter noise and find the width values. Figure 2. The silhouette before removing noise. The silhouette after removing noise. Figure 3. The width measurement of the silhouette. After the system acquired the width value from each silhouette, we used the average filter to remove noise from the width values. The output equation of the average filter, in terms of its input, is: (1) Testing/Training set Normalize the silhouettes Gait Ent!rzy male (GEl) TrateTransform Where: x[n] is the input value, y[ n] is the output value, and bo, bl,b2, b3,b 4 are the filter coefficients, which are equal to 0.2 (1/5). Panern Trace Transform The results of using the average filter are shown in Fignre 4. To perform a matching between testing set and training set Figure. The model of the experiment for gait recognition, using GET and pattern trace transfonn. 3.1 Preprocessing The scope of the picture box that shows a silhouette image is calculated from the binary silhouettes, which are cut to the location and size of the box boundaries. The silhouette is Figure 4. Example of the width values, before and after applying the average filter. 937

3 For the gait period calculation, first-order derivative testing was used to find the relative maximum point of the width values. The period values were found in the differentiation of the frame with the maximum width value. However, this value is the half-gait period. Because the gait period consists of the leg steps of left-right-left or right-left-right, we used the maximum width value, which had the three consecutive maxima for the gait-period calculation. The gait period (GP) of each person was estimated by finding the median of period values for each video. GP = Median{p;} Where: Pi is the gait-period value of each video. After the system acquired the gait period, the calculated time was reduced by cropping the particular human body. The maximum size of all human bodies was found and we added 10 pixels to each side to enable all silhouettes to be the same size (211 x 141 pixels). Figure 5. The crop processing to reduce the calculation: the original picture with measured 352 x 240 pixels. picture noise is removed, aligned with the horizontal centroid and the final picture size is 211 x 141 pixels. 3.2 Gait Energy mage (GEl) The binary silhouettes B;(x,y,t) are extracted at time t in a gait cycle i, gait energy image (GEl) is defined as follows: (2) 3.3 Trace Transform Kadyrov and Petrou [10-11] proposed the concept of Trace Transform and its applications. Trace Ttransform is a generalization of Radon Ttransform and it has already shown good performance for image extraction. Trace Transform includes tracking images with straight lines along which certain functions of the image are calculated. The different functions may be used to produce a different result from the same image.. 0 t Figure 7. Definition of the parameters of an image tracing line. Srisuk et al. [12] used trace transform in a face authentication system. They defined 22 trace functions for their work, whereas we chose only one proper trace function for ours because this function provide the best pattern trace transform. The trace function used in this experiment is as follows: Where: r = x - r(j(x))= feiklogrrp f(r)dr [O,a] c and c = medianx,(j(x ))h } (3) (4) Where: N is the number of frames in one period, B is a silhouette image whose pixel coordinates are given by x and y, and t is the frame index. Figure 6 shows some samples of GEL Figure 8. GEl image. The Trace mage of GEL Figure 6. Gait Energy mage (GEl) of a person in one period. 3.4 Pattern Trace Transform (PTT) After the system had produced the trace transform image, the threshold value (T=41) was defined to find the pattern trace transform images because the result of this threshold 938

4 value was the best. Some examples of the pattern trace transform images are shown in Figure 9. 6fr (c) -.. Figure 9. Pattern Trace Transfonn images of all videos from the same person when T=41: ( a) pattern image for Video 1; pattern image for Video 2; (c) pattern image for Video 3 ; (d) pattern image for Video Template Matching We used the template matching and Euclidean Distance for classification. First, the template-matching technique was used in classifying objects and comparing portions of images against one another to determine which sample image may be used in order to recognize similar objects in the source image. Correlation is a measure of the degree to which two variables agree, not necessarily in actual values, but in general behaviour. The two variables are the corresponding pixel values in two images, the template and the source. cor = : (x; -x) (y; - (d) y) ----r= ======= (5) ;=0 ;=0 Where: cor is the correlation value between two images, x is - the template image, x is the average in the template image, y is the source image section, y is the average in the source image and N is the number of pixels in the section image (N= template image size = colunms * rows). The value cor is between -1 and + 1, with larger values representing a stronger relationship between the two images. Secondly, Euclidean Distance (d(,g,r,e)) was adopted as a similarity measurement to explain the distance between different patterns. The Euclidean Distance between the two patterns can be defined by: 4. EXPERMENTAL RESULTS Extensive experiments have been carried out on the nstitute of Automation of the Chinese Academy of Sciences (CASA) gait dataset A to demonstrate the validity of the proposed system. The CASA gait dataset A [1] consists of 20 subjects and involves four sequences obtained from three different camera angles per subject, leading to a total of 80 sequences per view. n this paper, we consider only the canonical view. The performance statlstlcs are shown as the cumulative match scores [13], which measure the percentage of probes correctly identified at each ranking, and are plotted on a graph. The vertical axis of the graph is the cumulative match values or the ratio of the correct match and the horizontal axis is the rank. n Figures lo and 11, the blue line of the graph shows the cumulative matching scores that video 1 defined as the testing set and the others are the training set. The green line of the graph shows the cumulative matching score that video 2 defined as the test set and the others are the training set. The red line of the graph shows the cumulative matching score that video 3 defined as the test set and the others are the training set. The pink line of the graph shows the cumulative matching score that video 4 defined as the test set and the others are the training set. n Figure 12, the comparison of the average cumulative matching score of the Euclidean Method and Template Matching Method are shown in the green line and the blue line, respectively. From all the results (Table and Figure 12), the average cumulative matching scores was 91.25% in rank 1 and 100% in rank '"'" t-._... 90,'" / 80 ; ! Cumulative Value of Each Video Vid' Video 2 _.-.-. Video 3... Video ' '5 20 Figure 10. The cumulative matching score of each video when we used template matching and T=41. d(,g,r,e) = 2 n n ((r+ i,e + j) -g(i,j)) ;=1 j=1 (6) Where: is an image and g is a template of size n by m. The coordinates (r,e) denote the top left comer of template g. 939

5 so Cumulative Value of Each Video 20 V'd'" Video Video Video , , 5 ==== 20 Figure. The cumulative matching score of each video when we used Euclidean Distance and T 41. TABLE 1. Cumulative Valu e of Each M ethod 100 r--r ----:: r::: 90-80,- /,- -=--=-" a-o--o- o--o-a-a - Euclidean Template Matching 50 0L , 0 --!===0,5=======c2J O Figure 12. The cumulative matching score when T 41. COMPARSON WTH PREVOUS WORKS ON THE CASA GAT DATASET A N THE CANONCAL VEW Methods Accuracy 1 5 L. Wang 2003[1] L. Lee 2003[2] E. H. Zhang 2005[3] Han 2006[4] M. H. Cheng 2008[5] H. Lee 2008[6] S. Hong 2009[7] Y. Zhang 2009[8] Yi-Bo Li 2010[9] Our Method(T=41 Euclidean distance) Our Method(T=41 Template Matching) 5. CONCLUSON n this paper, we proposed a novel technique of gait recognition based on GEl and pattern trace transform. This method is simple. The best result of gait recognition from our methods is 91.25% in rank 1 and 100% in rank 5, which is better than some previous work done in rank 1 or rank 5. So, our method can distinguish the person from their gait and it can be developed to perform better. n the future, we will improve this algorithm to improve the results. 6. ACKNOWLEDGMENTS The authors would like to thank the associate editor and the anonymous reviewers for their excellent suggestions. [] 7. REFERENCES 1.. Wang, T. Tan, H. Z. Ning, and W. M. Hu, "Silhouette Analysis-based Gait Recognition for Human identification," EEE Tranactions on Pattern Analysis and Machine ntelligence, vol. 25, no. 12, pp , Dec [2] 1.. Lee, G. Daney, K. Tieu, "Learning Pedestrian Models for Silhouette Refinement," Proc. of Ninth EEE nternational Conference on Computer Vision (CCV2003), EEE Computer Society, Oct. 2003, pp , doi: /CCV [3] E. H. Zhang, J. W. Lu, and G. L. Duan, "Gait Recognition via ndependent Component Analysis Based on Support Vector Machine and Neural Network," in Proc. 1st nt. Conf. Advances in Natural Computation, Changsha, China, Aug. 2005, pp [4] J. Han, B. Bhanu, "ndividual Recognition Using Gait Energy mage," EEE Trans. PAM., vol. 28, no. 2, Feb. 2006, pp , doi: /FGR [5] M. H. Cheng, M. F. Ho, and C. L. Huang, "Gait Analysis for Human dentification Through Manifold Learning and HMM," Pattern Recognition, vol. 41, no. 8, pp , Aug [6] H. Lee, S. Hong, and E. Kim, "An Efficient Gait Recognition Based on a Selective Neural Network Ensemble," nternational Journal of maging Systems and Technology, vol. 18, no. 4, pp , Oct [7] S. Hong, H. Lee, and E. Kim, "Fusion of Multiple Gait Cycles for Human dentification," CCAS-SCE, nternational Joint Conference 2009, vol., no., pp , Aug [8] Y. Zhang, X. Wu, T. Guo, and X. Li, "Robust Post-processing Strategy for Gait Silhouette," 2nd EEE nternational Conference on Computer Science and nformation Technology, CCST 2009., vop, no., pp , Aug [9] Yi-Bo Li, Q. Yang, "Gait Extraction and Recognition Based on Lower Leg and Ankle," 2010 nternational Conference on ntelligent Computation Technology and Automation (CCTA20 0), pp , May 20 O. [10] A. Kadyrov and M. Petrou, 'The Trace Transform as a Tool to nvariant Feature Construction," in Fourteenth nternational Conference on Pattern Recognition, Proceedings., vol. 2,1998, pp [] A. Kadyrov and M. Petrou, 'The Trace Transform and its Applications," EEE Transactions on Pattern Analysis and Machine ntenigence, PAM, vol. 23, no. 8, pp ,2001. [12] S. Srisuk, M. Petrou, W. Kurutach, and A. Kadyrov, "A Face Authentication System Using the Trace Transform," Pattern Anal. Applicat., vol.8, pp.50-61, [13] S. Chen and Y. Gao, "Stride History mage: a New Feature Representation for Pedestrian dentification," Workshop on Signal Processing Systems, in Proc. of EEE, Oct 2007, pp [14] P. Theekhanont, S. Miguet, W. Kurutach, "Gait recognition using shape trace transform," T in Medicine and Education (TME), 20 nternational Symposium on Volume 2, Dec 2011, pp