Objective

2

Gait recognition aims to identify and verify individuals on the basis of walking postures. The performance of existing gait recognition methods is easily influenced by factors such as viewing variances

clothing changes

and types of objects carried by a person. Furthermore

none of these methods consider that the coordination and periodicity of human walking are also important features for gait recognition. Therefore

we propose the pose energy map without considering shoulders (PEMoS) to reduce the effect of clothing changes and 2D Fourier transform magnitude spectrum of gait parameters (2DFoMS) to enhance the effect of the coordination and periodicity of human movements. As these features have a close relationship with the human pose

we call them pose features. Moreover

the proposed pose features are fused together with other excellent features such as GaitSet to improve the overall performance of gait recognition.

Method

2

Clothing changes can affect the detected positions of body joints

especially the shoulder joints. Therefore

we propose PEMoS

which ignores the shoulder width

to reduce the effect of clothing changes. The construction process of PEMoS is as follows: First

body joints in each frame are detected by pose estimation methods. Second

six upper limb joints

namely

RShoulder(right shoulder)

RElbow(right elbow)

RWrist(right wrist)

LShoulder(loft shoulder)

LElbow(left elbow)

and LWrist(left wrist)

are horizontally shifted with the displacement between the neck joint and the right or left shoulder joint

whereas the rest remain unchanged. Third

the pose binary map is formatted by connecting the corrected joints in a predefined order and width. Then

it is resized to 128×88 pixels centering on the MidHip joint. Fourth

PEMoS is computed by averaging pose binary maps within a period that include at least one complete gait cycle. Finally

PEMoS is activated by gamma transformation to improve the performance. 2DFoMS uses the coordination between human movements and the periodicity of one single movement to enhance the gait recognition performance. As the lower limbs are less affected by clothing or bags

three new gait parameters computed from the lower limbs are proposed

which include the area of the triangle formed by MidHip(middle hip)

LKnee(left knee)

and RKnee(right knee); the area of the triangle formed by MidHip

LAnkle(left ankle)

and RAnkle(right ankle); and the area of the polygon enclosed by all the lower limb joints. Unlike the existing gait parameters that only consider the relationship between two joints

the proposed area parameters consider the local structural relationship of more than three points

which can enhance the effect of lower limb joints to the gait. The proposed three parameters are concatenated with the other 16 gait parameters extracted by regular methods to form a gait parameter column vector in each frame. The gait parameter column vectors of successive frames over time form a two-dimensional gait parameter matrix. As the gait parameters vary with time

they should be aligned to facilitate training and matching. On the basis of the observation that the width between two ankles varies with time much more regularly than other gait parameters

we propose to use the peak value position of the ankle width curve as base points to align all other gait parameters. 2DFoMS is computed by applying 2D Fourier transform on the registered gait parameter matrix. To fully utilize the advantages of the proposed features and the state-of-the-art features

such as GaitSet

two-level score fusion based on weighted average-max pooling is proposed to compute the matching score. The first level is the weighted average scores of multiple features. At this level

three groups of weights applicable to three different scenarios are proposed to compute the scores. At the second level

the max pooling of the first level scores is used as the final matching score.

Result

2

We evaluate the proposed method in terms of four aspects: overall performance

performance under different walking conditions

performance under cross-view conditions

and ablation study. In CASIA Gait Database B

the first 62 subjects are used for training

and the remaining 62 subjects are used for testing. The experimental results show that the proposed method achieves average accuracies of 99.56%

99.23%

and 94.25% under walking normally

walking with a bag

and walking with a different coat

respectively

when the views of the probe sequence and its counterpart in the gallery set are the same. For cross-view recognition

the proposed method achieves average accuracies of 91.32%

85.34%

and 69.51% under walking normally

walking with a bag

and walking with a different coat

respectively. Compared with the state-of-the-art methods

the average accuracy of the proposed method increases by about 6.98% under clothing changing conditions. Ablation study shows that PEMoS and 2DFoMS are effective in improving the gait recognition accuracy.

Conclusion

2

The proposed PEMoS

which ignores the shoulder width

can increase the robustness and accuracy of gait recognition under clothing changing conditions. The proposed local structure gait parameters can enhance the effect of lower limb points to the gait

which are more robust to clothing or bag changes than the upper limb parameters. 2DFoMS can emphasize the effect of coordination between human movements and the periodicity of one single moment on the performance of gait recognition. The experimental results show that the proposed algorithm has achieved state-of-the-art performance

especially under clothing or bag changing conditions.