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Abstract:
Gait energy image (GEI) is composed of static body silhouette and dynamic frequency information of human gait. To achieve fast and efficient gait recognition, combined with the accurate description of the information of details and directions in image by Curvelet transform, a gait recognition method using GEI and Curvelet (GEIC) is presented. Firstly, to gain the gait energy images, the gait cycle is selected according to the aspect ratio. Secondly, Curvelet energy coefficients of the GEI, which are used as gait feature vector, are extracted by Curvelet transform in different scales and different directions. Finally, the gait recognition is accomplished by the K nearest neighbor (KNN) classifier. The experimental results demonstrate that GEIC performs well on CASIA(B) database, with the average accuracy of 86.83%. Compared with GEI+KPCA, GEI+W(2D)2PCA and GEI+(2D)2PCA, the algorithm GEIC achieves better robustness in the condition of the person wearing or packaging.
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Key words:
- gait recognition /
- GEI /
- curvelet decomposition /
- curvelet feature extraction /
- KNN
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Abstract:International and domestic academics are attracted by biometric features recognition, because the intelligent monitoring, which is the basis of social security, is more and more required by people. Compared with other biometric features (face, iris fingerprint and so on), gait feature has its advantages, such as acceptability, noninvasiveness, hard to hide and easy to be collected. Gait recognition, recognizing different persons by the way of his/her walking, which has some advantages such as non-contact, low demand in image quality and difficult disguise, is the most potentially biological recognition technology by the way of one’s walking. Recently, the research of gait recognition is a hot topic in the field of computer vision, entrance guard system and medical diagnose, which has extensive realistic significance and wide applying foreground.
Gait feature, which is extracted to describe the motion of a cycle gait, is the key step of gait recognition to our way of thinking. Gait energy image (GEI), which contains both the static silhouette feature and the dynamic frequency of each part of human body in the process of walking, is demonstrated to be an effective feature for identity recognition. GEI reflecting gait characteristic is gained from gait images of one cycle by using weighted average method. Compared with using all the gait images of one cycle as the gait feature, computation complexity is decreased in a large part by using GEI. Curvelet transformation is a multi-scale pyramid decomposition, which reflects one image at different directions and scales. However, this pyramid is nonstandard because the length and width which is the square of length in each Curvelet are variable. On account of the accurate description of the detail and direction information of gait frequency by Curvelet transform, GEI is analyzed in the view of texture analysis by the extracted Curvelet features.
To achieve fast and efficient gait recognition, combined with the accurate description of the information of details and directions in image by Curvelet transform, a gait recognition method using GEI and Curvelet (GEIC) is presented. Firstly, the gait cycle is selected through analyzing the aspect ratio of a gait sequence and one cycle gait images are normalized and centralized to reduce redundant information computation complexity. Secondly, gait energy image is extracted by weighted average method from those gait images after preprocessing. Thirdly, Curvelet energy coefficients of the GEI, which were used as gait feature vector, were extracted by Curvelet transform in different scales and different directions. Finally, the gait recognition was accomplished by the K nearest neighbor (KNN) classifier. The experimental results demonstrate that GEIC performs well on CASIA (B) database, with the average accuracy as 86.83%. Compared with GEI+KPCA, GEI+W(2D)2PCA and GEI+(2D)2PCA , the proposed algorithm based on GEI and Curvelet for gait recognition gets higher performance. Thus, the information of human body silhouette and motion frequency in GEI can be expressed accurately by Curvelet with less energy coefficients in the multi-scale, considering the view of the edge and texture. Using the energy coefficients of Curvelet transform as gait feature vector can not only improve the recognition efficiency, but also reduce the dimension of gait feature which basically satisfies the real-time requirement.
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Figure 2. (a) Four cases of reaching extreme point in one gait cycle. (b) The H-W ratio curve of a gait sequence.
Table 1. The Curvelet feature of GEI.
Feature 1 2 3 4 5 6 7 8 9 10 Value 12247.9 140.705 326.293 349.155 216.235 251.192 446.521 617.081 345.962 140.705 Feature 11 12 13 14 15 16 17 18 19 20 Value 326.293 349.155 216.235 251.192 446.521 617.081 345.962 56.89389 100.066 111.513 Feature 21 22 23 24 25 26 27 28 29 30 Value 151.409 138.979 131.168 60.783 49.7588 61.3174 80.2487 91.9216 135.123 223.940 Feature 31 32 33 34 35 36 37 38 39 40 Value 178.354 70.7249 77.8300 56.8939 100.066 111.513 151.409 138.979 131.168 60.7834 Feature 41 42 43 44 45 46 47 48 49 50 Value 49.7588 61.3174 91.9216 91.9216 135.123 223.940 178.354 70.7249 77.8300 210.850 
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Table 2. Training and testing selection of Sets1, Sets2 and Sets3.
Number of people Sequence number of one person Total Set 1 Set 2 Set 3 Set 1 Set 2 Set 3 Training 124 1 1 1 124 124 124 Testing 124 5 1 1 620 124 124
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Table 3. The recognition rate of different algorithms.
CEI+KPCA GEI+(2D)2PCA GEI+W(2D)2PCA GEIC Normal gait/% 75.5 79.4 80.2 85.5 Packaging gait/% 79.0 81.5 83.0 87.1 Wearing gait/% 78.2 84.6 86.2 87.9 Average/% 77.57 81.83 83.13 86.83
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