A Scalable Patch-Based Approach for RGB-D Face Recognition

Grati, Nesrine; Ben-Hamadou, Achraf; Hammami, Mohamed

doi:10.1007/978-3-319-46681-1_35

Cited by 1 publication

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An overall of 94.6%, 88.67% and 94.23% are achieved with our system respectively for RGB, depth and multimodal data. It's clear that our RGB performance need to be improved but our depth performance seems better than this reported in (Li et al, 2013) which demon- (Ciaccio et al, 2013;Hsu et al, 2014) and some recent works (Grati et al, 2016;Kaashki and Safabakhsh, 2018).…”

Section: Validation and Resultsmentioning

confidence: 68%

“…For the first set of experiments, and on CurtinFaces dataset, we report our results as well as those of (Hsu et al, 2014) (Li et al, 2013), (Ciaccio et al, 2013), (Kaashki and Safabakhsh, 2018), and (Grati et al, 2016). To make a fair comparison, we use the same protocol as (Li et al, 2013).…”

Section: Validation and Resultsmentioning

confidence: 99%

“…We followed (Grati et al, 2016) for the adaptive and dynamic dictionary selection in the SRC process. The objective the SRC is to reconstruct an input signal by a linear combination of atoms in a selected dictionary.…”

Section: Patch Classificationmentioning

confidence: 99%

See 2 more Smart Citations

Discriminant Patch Representation for RGB-D Face Recognition using Convolutional Neural Networks

Grati

Ben-Hamadou

Hammami

2019

Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

Self Cite

View full text Add to dashboard Cite

This paper focuses on designing data-driven models to learn a discriminant representation space for face recognition using RGB-D data. Unlike hand-crafted representations, learned models can extract and organize the discriminant information from the data, and can automatically adapt to build new compute vision applications faster. We proposed an effective way to train Convolutional Neural Networks to learn face patch discriminant features. The proposed solution was tested and validated on state-of-the-art RGB-D datasets and showed competitive and promising results relatively to standard hand-crafted feature extractors.

show abstract