Gabor-based Face Recognition With Illumination Variation Using Subspace-Linear Discriminant Analysis

Kusuma, Hendra; Wirawan, Nfn; Soeprijanto, Adi

doi:10.11591/telkomnika.v10i1.661

Cited by 9 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It may be because the We experiment with expression variation only in this paper and illumination is considered to be constant. Owing to the independent block structure of the overall framework, the work can be extended to handle lighting variations by illumination adaption [27] or using some illumination invariant representation such as the self-quotient image [31] and Gabor feature [32].…”

Section: Face Recognitionmentioning

confidence: 99%

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

Section: Face Recognitionmentioning

confidence: 99%

Personalised face neutralisation based on subspace bilinear regression

Chen

Bai

Chen

2014

IET Computer Vision

View full text Add to dashboard Cite

show abstract

“…On the other hand, Gabor wavelets can be tuned for specific frequencies and orientations which is useful for both thick and thin vessels. Gabor wavelet has its application in almost every field and they are mostly used to enhance the pattern at some specific orientation [36]. 2-D Gabor wavelet is used due to its directional selectiveness capability of detecting oriented features and fine tuning to specific frequencies [29], [30].…”

Section: Blood Vessel Enhancement Using Gabor Waveletsmentioning

confidence: 99%

Blood Vessel Enhancement and Segmentation for Screening of Diabetic Retinopathy

2012

View full text Add to dashboard Cite

Abstrak Diabetic retinopathy adalah penyakit mata yang disebabkan kenaikan kadar insulin dalam arah dan penyakit ini merupakan salah satu penyebab utama kebutaan di negara industri. Penyakit ini merupakan penyakit progresif dan membutuhkan deteksi dan penanganan awal. Pola vaskular dari retina manusia membantu opthamologis dalam skrining otomatis dan diagnosa diabetic retinopathy. Pada makalah ini, disajikan sebuah metode untuk segmentasi dan peningkatan pola vaskular. Sistem terotomisasi yang menggunakan wavelets disajikan untuk menaikkan pola vaskular dan kemudian diterapkan pada probing ambang sesepenggal dan pengambangan adaptif untuk kegunaan lokalisasi pembuluh darah dan segmentasi berkaitan. Metode dievaluasi dan diuji menggunakan database retina yang tersedia umum dan lebih jauh metode yang diusulkan dibandingkan dengan teknik yang sudah ada. Kata kunci, diabetic retinopathy, blood vessels, : probing ambang, wavelets Abstract Diabetic retinopathy is an eye disease caused by the increase of insulin in blood and it is one of the main cuases of blindness in idusterlized countries. It is a progressive disease and needs an early detection and treatment. Vascular pattern of human retina helps the ophthalmologists in automated screening and diagnosis of diabetic retinopathy. In this article, we present a method for vascular pattern ehnacement and segmentation. We present an automated system which uses wavelets to enhance the vascular pattern and then it applies a piecewise threshold probing and adaptive thresholding for vessel localization and segmentation respectively. The method is evaluated and tested using publicly available retinal databases and we further compare our method with already proposed techniques.

show abstract