Construction of a Unique Gait Template in .NET

Development QR Code ISO/IEC18004 in .NET Construction of a Unique Gait Template
Construction of a Unique Gait Template
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The temporal normalization process, described in the previous section, can be errorprone and computationally heavy. This is why one of the challenges in gait representation is to compact an individual s gait information on a single two-dimensional template that is suitable for use in recognition applications. Because a gait sequence is a spatiotemporal volume, its reduction to a two-dimensional template or a onedimensional vector, without any loss of discriminatory information, is not a trivial task. In general, a template-based approach for gait recognition should: r capture as much structural information as possible; r capture the gait dynamics; r condense all gait information into a relatively small number of coef cients; r be applied directly without any need for prior compensation for speed differences between the compared walking subjects.
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The simplest and one of the most widely used templates is the average silhouette. After an object detection and extraction step is deployed, silhouettes in a gait sequence are appropriately scaled and aligned. Then the template is calculated by simply averaging the foreground maps (silhouettes) of the sequence
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21.4 Gait Recognition
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Figure 21.7. (a) Template based on the average silhouette. (b) Template for Radon transform.
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where NS is the number of silhouettes and Sa , a = 1, . . . , NS , are the silhouettes in a gait sequence. Figure 21.7a shows the average silhouettes of one subject in the Gait Challenge database [35]. The ef ciency and recognition power of this feature was shown in references 41 and 42. Several gait recognition systems are based on such template construction, e.g., in reference 43, the average silhouette is de ned as Gait Energy Image (GEI) and is used for further component and discriminant analysis; in reference 44, average silhouettes from multiple views are used for combined-view recognition. Another template is based on the Radon transform of silhouettes. It contains both the structural and frequency information that exists in a gait cycle for each gait sequence [34]. First, each Radon-transformed silhouette SRi is associated with a phase as follows: SRi = SRi (1 + cos i + j (1 + sin i )). In the above equation, the angle i is de ned as i = 2 i, 4N1 (21.17) (21.16)
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where N1 is the number of frames in a gait cycle, and i is an index in the range from 0 to N1 1. Since S is a two-dimensional matrix consisting of Radon coef cients, then each of its coef cients is affected in the same manner. A Radon template T (see Figure 21.7b), is de ned as follows: T= 1 N1
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where N1 is the total number of silhouettes in a sequence.
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Unobtrusive Biometric Identi cation Based on Gait
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Dimensionality Reduction
The construction of a template for gait recognition requires the availability of a video sequence depicting one walking cycle of the observed individual. In practice, however, more than one walking cycle might be available in a gait sequence. This means that recognition can be improved by focusing on the template coef cients that do not change much within templates corresponding to the same subject while they vary considerably between different subjects. Putting emphasis on the more discriminative coef cients and reducing the dimensionality of the problem are possible by using linear discriminant analysis (LDA) [45]. This is achieved by calculating a subspace protection of the original template using an appropriate matrix W. This matrix is calculated by maximization of Fisher s criterion J(W) = |W SB WT | , |W SW WT |
where SB is the between-class scatter matrix, and SW is the within-class scatter matrix. While LDA has been extensively applied to other biometric traits, such as ngerprints and face, here we present its application on gait recognition using the templates derived in the previous section. Since the discrimination power of the template representation is expected to be unevenly distributed among template coef cients, LDA can be used in order to reduce the dimensionality of the problem and, therefore, simplify the recognition task. This is particularly important considering that the original feature vector is derived from templates and, therefore, is highdimensional; that is, a template of dimensions of M N is converted into a I 1 vector, where I = M N. If only one template is available for each subject in the database, principal component analysis (PCA) [29, 31, 45] can be used. However, it must be noted that, unlike LDA, PCA does not emphasize the most discriminative coef cients; but instead, it uses the coef cients that best describe the templates. For this reason, PCA will generally perform worse than LDA.