Browsing by Autor "Israel Raul Tinini Alvarez"

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Cross-View Gait Recognition Based on U-Net
(2020) Israel Raul Tinini Alvarez; Guillermo Sahonero-Alvarez
Gait based recognition systems allow automatic subjects' recognition by using the way of walking. However, the performance of these systems is often degraded by some covariate factors such as walking direction, appearance changes, occlusions, among others. From these, it has been shown that change in appearance is the most influent covariant by drastically affecting the recognition performance. Consequently, inspired by the great successes of GANs in image translation tasks, we propose a method of gait recognition using a conditional generative model to generate view-invariant features. The proposed method is evaluated on one of the largest datasets available under the variations of view, clothing and carrying conditions: CASIA gait database B. Experimental results show that the proposed method outperforms state-of-the-art methods specially in carrying-bag and wearing-coat sequences. The full implementation and trained networks are available at https://gitlab.com/IsRaTiAl/gait.
Exploring Edge Computing for Gait Recognition
(2021) Israel Raul Tinini Alvarez; Guillermo Sahonero-Alvarez; Carlos Menacho; Josmar Suarez
Gait Recognition, as a way to identify people, is re-markably attractive for scenarios in which it is not possible to rely on subjects' collaboration. Nevertheless, from all the modalities that Gait Recognition involve, vision-based approaches are better to meet hardware and settings-limitations. Because of that, in the past years, there has been several efforts on developing robust algorithms against visual gait covariates, i.e., view, clothing and carrying variations. However, besides robustness, real-world gait recognition systems also require to be implemented considering near real-time computational demands as well as portability. In this work we propose an Edge Computing approach based on the NVIDIA Jetson Nano development board and the OpenCV OAK-D camera to perform Gait Recognition. To adapt our approach, we created two small data sets that allowed our system to particularize the system to local data. Our pipeline implies the usage of a pre-trained object detection algorithm in the OAK-D, and the execution of both the representation extraction and inference on the Jetson Nano. To test our framework, we first explore its feasibility and consistency in an offline manner. Later, we characterize the complexity and time processing when executing the procedures in an online setup. Our results show that the approach is promising as it allows online operation with an inference time of 35.8 ms.
Gait Recognition Based on Modified Gait Energy Image
(2018) Israel Raul Tinini Alvarez; Guillermo Sahonero-Alvarez
Biometric systems allow us to identify individuals from distinctive biological traits. Gait recognition is a biometric technique used to recognize humans based on the style of their walk. However, model-free based gait recognition performance is often degraded by the presence of some covariate factors such as view, clothing and carrying variations. From these, it has been shown that the change in appearance is the covariant that most affect the recognition performance. To address such issues, we propose to use a feature representation that takes both dynamic and static regions of silhouettes. This way, more robustness against covariates and better discriminative performance are expected. The proposed method is evaluated on one of the largest datasets available under the variations of clothing and carrying conditions: CASIA gait database B. Results show that the proposed method achieves correct classification rate up to 90% and outperformed state-of-the-art methods.