A novel approach of gait recognition through fusion with footstep information

Vera-Rodríguez, Rubén; Fiérrez, Julián; Mason, John S.; Ortega‐Garcia, Javier

doi:10.1109/icb.2013.6613014

Cited by 16 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is also worth noting that Vera-Rodriguez et al [107] have suggested that their fusion of FS and VS modalities, implemented on handcrafted features as an input to shallow learning methods, is amenable to deep learning methods for automatic extraction of fused features to improve the accuracy.…”

Section: Multi-modality Gait Sensor Fusionmentioning

confidence: 99%

Deep Learning for Monitoring of Human Gait: A Review

2019

View full text Add to dashboard Cite

The essential human gait parameters are briefly reviewed, followed by a detailed review of the state of the art in deep learning for the human gait analysis. The modalities for capturing the gait data are grouped according to the sensing technology: video sequences, wearable sensors, and floor sensors, as well as the publicly available datasets. The established artificial neural network architectures for deep learning are reviewed for each group, and their performance are compared with particular emphasis on the spatiotemporal character of gait data and the motivation for multi-sensor, multi-modality fusion. It is shown that by most of the essential metrics, deep learning convolutional neural networks typically outperform shallow learning models. In the light of the discussed character of gait data, this is attributed to the possibility to extract the gait features automatically in deep learning as opposed to the shallow learning from the handcrafted gait features.

show abstract

Section: Multi-modality Gait Sensor Fusionmentioning

confidence: 99%

Deep Learning for Monitoring of Human Gait: A Review

2019

View full text Add to dashboard Cite

show abstract

“…Such is the case of the work in [16], where they fused information of the gait biometric trait with some shape cues such as body weight, width and some body part proportions. Then in [17,18], a multimodal system based on footsteps and gait was built. Likewise, in [19], a spatial temporal analysis of the lower part of the human silhouette was used to build a gait recognition system.…”

Section: Related Work On Shape-based Recognitionmentioning

confidence: 99%

Feature exploration for biometric recognition using millimetre wave body images

Gonzalez-Sosa¹,

Vera-Rodríguez²,

Fiérrez³

et al. 2015

J Image Video Proc.

Self Cite

View full text Add to dashboard Cite

The use of millimetre wave images has been proposed recently in the biometric field to overcome certain limitations when using images acquired at visible frequencies. Furthermore, the security community has started using millimetre wave screening scanners in order to detect concealed objects. We believe we can exploit the use of these devices by incorporating biometric functionalities. This paper proposes a biometric recognition system based on the information of the silhouette of the human body, which may be seen as a type of soft biometric trait. To this aim, we report experimental results on the BIOGIGA database with four feature extraction approaches (contour coordinates, shape contexts, Fourier descriptors and landmarks) and three classification methods (Euclidean distance, dynamic time warping and support vector machines). The best configuration of 1.33 % EER is achieved when using contour coordinates with dynamic time warping.

show abstract