Gabor-based Face Recognition with Illumination Variation using Subspace-Linear Discriminant Analysis

Kusuma, Hendra; Wirawan, Nfn; Soeprijanto, Adi

doi:10.12928/telkomnika.v10i1.767

Cited by 5 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These have been introduced in the field of analysis of facial images for the first time by Lades and al [17] for detecting face characteristics. Then they became a powerful tool in many algorithms of face recognition [18] [19] and face detection [20] [21]. The work that exploited Gabor filters to extract relevant information from facial images argues that this representation is robust to changes caused by variations in illumination or changes facial expressions.…”

Section: The Developed Descriptor Of Posementioning

confidence: 99%

Generic and Robust Method for Head Pose Estimation

Hajraoui

Sabri

2016

IJEECS

View full text Add to dashboard Cite

Head pose estimation has fascinated the research community due to its application in facial motion capture, human-computer interaction and video conferencing. It is a pre-requisite to gaze tracking, face recognition, and facial expression analysis. In this paper, we present a generic and robust method for model-based global 2D head pose estimation from single RGB Image. In our approach we use of the one part the Gabor filters to conceive a robust pose descriptor to illumination and facial expression variations, and that target the pose information. Moreover, we ensure the classification of these descriptors using a SVM classifier. The approach has proved effective view the rate for the correct pose estimations that we got.

show abstract

Section: The Developed Descriptor Of Posementioning

confidence: 99%

Generic and Robust Method for Head Pose Estimation

Hajraoui

Sabri

2016

IJEECS

View full text Add to dashboard Cite

show abstract

Personalised face neutralisation based on subspace bilinear regression

Chen

Bai

Chen

2014

IET Computer Vision

View full text Add to dashboard Cite

Expression face neutralisation helps to improve the performance of expressive face recognition with one single neutral sample in gallery per subject. For learning-based expression neutralisation, the virtual neutral face totally relies on training samples, which removes person-specific characters from the neutralised face. Bilinear kernel rank reduced regression (BKRRR) algorithm is designed in a virtual subspace to simultaneously and efficiently generate both virtual expressive and neutral images from training samples. An expression mask is then established using grey and gradient differences of the two images. The test expression image is transformed to neutral template by piece-wise affine warp (PAW). Using the virtual BKRRR neutral image as source, the PAW image as destination and the area covered by expression mask as clone area, an image fusion strategy based on Poisson equation is then designed, which achieves virtual neutralised face image with personspecific characters preserved. From experiments on the CMU Multi-PIE databases, it could be observed that the neutral faces synthesised by the proposed method could effectively approximate the real ground truth expressive faces, and greatly improve the performance of classic face recognition algorithms on expression variant problems.

show abstract