Module 2, phase 2: SDFS

Similar to variation in clothing, the presence of a carried item significantly distort the shape of different parts of a silhouette, and the discriminability between the subjects decreases with respect to the affected parts. While it is unrealistic to predict which part(s) of a sil- houette will be affected due to clothing variation by assuming known clothing types in the training phase as in [52] due to the availability of numerous different types and combina- tions of clothing in real scenarios, the part(s) of a silhouette which is likely to be affected due to all small types of carrying conditions either at the subject’s back, with folded arms

or in an upright position can be convincingly identified based on anthropometry. The un- derlying principles are: (a) If a subject carries a backpack or a small item such as a ball or a package with folded arms, it is unlikely that the shape of silhouettes above the shoulder and below the anatomical position of hand of an upright subject, i.e., respectively 0.182Hfrom the top of the bounding rectangle and 0.377H from bottom of the bounding rectangle [97] will be affected [7]. Experimental analyses for all subjects holding a ball in CMU MoBo dataset [19] and carrying a backpack in CASIA gait dataset B verify the appropriateness of the assumption. (b) The shape of an upright silhouette above the wrist remains unchanged when the subject’s hand carries a briefcase or a small bag. The position of the wrist is about 0.515H[97] measured from the top of the bounding rectangle. Thus, the segment of silhou- ettes enclosed in the region 0.515Hof the bounding rectangle measured from the top is not affected by such carrying condition. The leg region of a silhouette enclosed betweenaH

(wherea=0.750 measured from the top of bounding rectangle and the bottom of bounding rectangle assuming a standard size of a briefcase [27] removes any shape distortion due to carrying a briefcase. (c) It is not unusual that a subject carries a bagpack at the back, a small item in one hand and a briefcase in the other hand. In this case, the shape of silhouettes above the shoulder and below 0.750Hremain undistorted.

Based on the above considerations, we divide a silhouette into four components, i.e., C1, C2, C3, and C4 as shown in Fig. 5.4 for component-based shape analysis using weighted Krawtchouk moments so as to reduce the adverse impact of carrying conditions on identification rate. The four components are enclosed by: (a) the top of bounding rectangle and up to the anatomical position of the shoulder, i.e., 0.182H (component C1); (b) the top of the bounding rectangle and up to the anatomical position of the wrist, i.e., 0.515H

(component C3); (c) the anatomical position of hand of an upright subject and the bottom of the bounding rectangle (component C2); and (d) the leg region to exclude briefcase (component C4). It is to be noted that the first and fourth components remain unaffected by the carrying conditions considered in this method, whereas the second and third components are most likely to get distorted by the carrying conditions with folded arms and in upright position. It might be argued that the second and third components will contribute more to the discrimination between the gallery and probe subjects in case of absence of a carried item, as they are larger body parts than the first and fourth components. However, these larger body components are affected by the arm-swing (an integral gait characteristic) in absence of carried items which changes due to variation in walking speed induced by change in mood, haste, etc. Since the two smaller components are not affected by the carrying conditions and they are also included in the larger body components which are most likely to be affected either by carrying conditions or by arm-swing, the smaller components, i.e., C1 and C4, are considered to contribute more to the discrimination of the subjects in ReG-IF

than the larger components, i.e., C2 and C3.

Figure 5.4: The silhouette components.

Following the unequal contribution of each body component to the subject identifi- cation due to carrying conditions, ReG-IF determines the pixel range of the silhouette image (i.e., the values ofMandN) of Eq.(4.1), such that they represent the four components of the silhouette, and computes the weighted Krawtchouk moments corresponding to each of these segments, e.g., for USF dataset, the range of values ofMandN used to extract local information of the silhouette from top of the bounding rectangle to the wrist are respectively [0,W], whereWis the width of the bounding rectangle (i.e., [0,88]), and [0,0.515H] (i.e., [0,65]). The range of values ofN andM for region betweenaH andbH are respectively [0,W], and [96,120]. The weighted Krawtchouk moments of the ten phases of a gait period of a gallery subject form part of the gallery database. ReG-IF computes Euclidean distances between the weighted Krawtchouk moments corresponding to each of these body compo- nents of the gallery and probe subjects, and subsequently combine the component-wise distances using weighted sum rule in order to determine the final similarity score between the subjects, where the weights are determined based on the discriminatory power of each component.

The Krawtchouk moments [65; 108] are adopted because their image reconstruc- tion capability is better than Zernike and Hu moments. Since they can be used to extract features from any region of an image, they can also address partially distorted frames of the ten phases of a gait period. The orthogonal property of weighted Krawtchouk moments en- sures minimal information redundancy. The scale and rotation dependency of Krawtchouk transform do not affect the extracted features as ReG-IF considers only lateral views of sil- houettes to achieve rotation invariance, and the silhouettes are pre-scaled and centre-aligned to achieve scale invariance. The weighted Krawtchouk moments of order (n+m) of aN×M

and Eq. (4.4).

The performance of any shape-based gait recognition method degrades due to varia- tion in the subject’s clothing and carrying conditions, as these variation distort the silhouette shape. The use of synthetic gait templates enables GEI and CGI to be invariant to distor- tions in the lower body-part but not in the upper body-part, e.g., due to carrying conditions with folded arms and at the back. The method in [45] analyses symmetry changes in double helical signatures at the limb region to take into account of shape distortion due to carry- ing condition, e.g., a briefcase carried by an upright subject. Although STM-SPP analyses different parts of a silhouette using EFDs to take into account of shape distortions due to carrying a briefcase and small items with folded arms, it does not consider subjects carrying a backpack. ReG-IF uses a new approach for a general analysis of invariance to carrying condition by taking into account common small items carried by a subject on his/her back, with folded ams and in upright position via component-based reconstruction using weighted Krawtchouk moments.