A View Transformation Model Based on Sparse and Redundant Representation for Human Gait Recognition
Publish place: Journal of medical signals and sensors، Vol: 10، Issue: 3
Publish Year: 1399
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_JMSI-10-3_001
تاریخ نمایه سازی: 28 تیر 1402
Abstract:
Background: Human gait as an effective behavioral biometric identifier has received much
attention in recent years. However, there are challenges which reduce its performance. In this
work we aim at improving performance of gait systems under variations in view angles, which
present one of the major challenges to gait algorithms. Methods: We propose employment of a
view transformation model based on sparse and redundant (SR) representation. More specifically,
our proposed method trains a set of corresponding dictionaries for each viewing angle, which are
then used in identification of a probe. In particular, the view transformation is performed by first
obtaining the SR representation of the input image using the appropriate dictionary, then multiplying
this representation by the dictionary of destination angle to obtain a corresponding image in the
intended angle. Results: Experiments performed using CASIA Gait Database, Dataset B, support
the satisfactory performance of our method. It is observed that in most tests, the proposed method
outperforms the other methods in comparison. This is especially the case for large changes in the
view angle, as well as the average recognition rate. Conclusion: A comparison with state‑of‑the‑art
methods in the literature showcases the superior performance of the proposed method, especially in
the case of large variations in view angle.
Keywords:
Biometrics , gait analysis , human identification , sparse and redundant representation , view transformation model , view‑invariant
Authors
Abbas Ghebleh
Department of Computer Engineering and Science, Shahid Beheshti University, Tehran, Iran
Mohsen Ebrahimi Moghaddam
Department of Computer Engineering and Science, Shahid Beheshti University, Tehran, Iran