Robust Recognition against Illumination Variations Based on SIFT

Nowruzi, Farzan Erlik; Balafar, Mohammad Ali; Pashazadeh, Saeid

doi:10.1007/978-3-642-33503-7_49

Cited by 3 publications

(2 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This restriction is particularly important when the application has low access or no access to ideal images for training purposes. In this instance, IS‐SIFT [96] obtained the highest recognition from reported results.…”

Section: Resultsmentioning

confidence: 88%

“…They are obtained by first decomposing the image using BEMD and then applying Riesz transform to obtain monogenic phase. Finally, Nowruzi et al [96] proposed a combination of local scale-invariant feature transform (SIFT) key-points based on the variations of illumination in the training data. SIFT features are obtained after smoothing and down-sampling the input image while creating a pyramid of DoG.…”

Section: Feature-basedmentioning

confidence: 99%

See 1 more Smart Citation

Addressing the illumination challenge in two‐dimensional face recognition: a survey

Ochoa-Villegas

Nolazco-Flores

Barron-Cano

et al. 2015

IET Computer Vision

View full text Add to dashboard Cite

Uncontrolled illumination is one of the most widely researched and most encountered face recognition challenges in recent years. In this study, the authors propose the division of algorithms into two categories: (i) relighting and (ii) unlighting. Relighting methods try to match the probe's illumination conditions using a subset of representative gallery images, while unlighting methods seek to suppress the variations. A total of 64 state-of-the-art methods are summarised and categorised in each of the groups. To make this work concise and easy to follow, they restricted themselves to selected conferences/journals and they limited the number of approaches reviewed. Also, eight past state-of-the-art approaches are used in both identification and verification experiments. However, only significant reported results from all methods were compared and organised in tables. The author's main objective is not to provide an exhaustive analysis of each category, but to present a collection of papers that can be useful in identifying research directions. Results indicate that unlighting methods are a better and a practical solution to address illumination challenges.

show abstract

Section: Resultsmentioning

confidence: 88%

Section: Feature-basedmentioning

confidence: 99%

Addressing the illumination challenge in two‐dimensional face recognition: a survey

Ochoa-Villegas

Nolazco-Flores

Barron-Cano

et al. 2015

IET Computer Vision

View full text Add to dashboard Cite

show abstract

An Analysis of RGB, Hue and Grayscale under Various Illuminations

Fitriyah

Wihandika

2018

2018 International Conference on Sustainable Information Engineering and Technology (SIET)

View full text Add to dashboard Cite

Local vs. Global

Riccio

Casanova

Fenu

Face Recognition in Adverse Conditions

View full text Add to dashboard Cite

Face recognition in real world applications is a very difficult task because of image misalignments, pose and illumination variations, or occlusions. Many researchers in this field have investigated both face representation and classification techniques able to deal with these drawbacks. However, none of them is free from limitations. Early proposed algorithms were generally holistic, in the sense they consider the face object as a whole. Recently, challenging benchmarks demonstrated that they are not adequate to be applied in unconstrained environments, despite of their good performances in more controlled conditions. Therefore, the researchers' attention is now turning on local features that have been demonstrated to be more robust to a large set of non-monotonic distortions. Nevertheless, though local operators partially overcome some drawbacks, they are still opening new questions (e.g., Which criteria should be used to select the most representative features?). This is the reason why, among all the others, hybrid approaches are showing a high potential in terms of recognition accuracy when applied in uncontrolled settings, as they integrate complementary information from both local and global features. This chapter explores local, global, and hybrid approaches.

show abstract

Robust Recognition against Illumination Variations Based on SIFT

Cited by 3 publications

References 13 publications

Addressing the illumination challenge in two‐dimensional face recognition: a survey

Addressing the illumination challenge in two‐dimensional face recognition: a survey

An Analysis of RGB, Hue and Grayscale under Various Illuminations

Local vs. Global

Contact Info

Product

Resources

About