Local Binary Pattern (LBP)

1. Infrared face recognition using texture descriptors Texture space by Moulay Akhloufi, Abdelhakim Bendada • Majority of IR face recognition techniques are inspired from their visible counterpart. • Linear subspace techniques are the most popular : • PCA: Eigenfaces • LDA: Fisherfaces • This work: • Linear and non-linear subspace techniques • Texture space (LBP and LTP) • Comparative study Face recognition Introduction • Increase of interest for efficient biometric systems (ex. Face Recognition) • Tests made in operational scenarios in 2002 and 2003 : little success in the visible spectrum. • Visible Spectrum sensitive to : • Lighting; • Facial Expressions. • Recent alternatives : • 3D (less sensitive to lighting); • Thermal IR (less sensitive to lighting and facial expressions); • Active IR (less sensitive to lighting) • Local Binary Pattern (LBP) • LBP texture analysis operator is a gray-scale invariant texture measure computed from the analysis of a 3x3 local neighborhood over a central pixel. • Largely used in visible spectrum face recognition. • In order to make images less sensitive to illumination and expression changes. • In IR spectrum : • Less sensitive to local thermal changes (LWIR and MWIR); • Less sensitive to expression changes (NIR and SWIR); • Enhance interesting face characteristics. Multispectral face databases IR Face extraction • Equinox Multimodal Face Database : • Visible (0.4-0.7 mm) • Short-wave (SWIR, 0.9-1.7 mm) • Mid-wave infrared (MWIR, 3-5 mm) • Long-wave infrared (LWIR, 8-12 mm) NIR Visible Experimental results (From left top right) Orig, LBP, LTPL, LTPU • Tests scenarios : • Facial expressions • Eyeglasses • Facial hair • Over time (2 and 3 weeks, 3 months) • Metabolic change (jogging) • 128x128 normalized image • 2 Sets : 1 for learning and 1 for recognition • Learning: 1 or 2 (4 for LDA) different face images from each individual • Recognition: • 10 tests per algo. with 100 images randomly chosen in each case (1000 tests/algo./case) • 96 000 tests / database ( 192 000 tests for the 2 databases) LWIR MWIR • Local Ternary Pattern (LTP) • Share the same characteristics as LBP. • LBP is sensitive to noise in near uniform regions. • LTP solves this later problem. • LTP extends LBP to 3-valued codes, in which local pixels are compared to a user defined thresholds –t and +t. Faces DB Visible Spectrum (facial expression – LUMF DB) IR data SVM Classifier NIR (eyeglasses – LUMF DB) • Laval University Multispectral Face (LUMF) Database • Visible (0.4-0.7 mm); • Near infrared (NIR, 0.8-0.9 mm). • Mid-wave infrared (MWIR, 3-5 mm) • Long-wave infrared (LWIR, 8-12 mm) Different facial expressions and eyeglasses Multispectral face databases SWIR (neutral – EQNX DB) • Linear techniques: • PCA • LDA • Global non linear techniques: • K-PCA • K-LDA • Local non linear techniques: • LLE: Local Linear Embedding • LPP: LocalityPreserving Projection MWIR (facial expression – LUMF DB) Aligned 90% success • Equinox Database • LLE, LPP • LTP, LBP • SWIR > VIS > MWIR > LWIR LWIR (eyeglasses – EQNX DB) LWIR (neutral – LUMF DB) Conclusion • Laval Univ. (LUMF) Database • LDA, LLE • LTP, LBP • Vis > MWIR > LWIR > NIR • Texture : increase in recognition performance. • Less sensitive to noise, illumination change and facial expressions. • Non linear techniques give interesting results. • LDA give interesting results when more images are used for intrapersonal learning (LUMF DB). • Future work : • Multispectral fusion in texture space. • “Swiss-Roll 3D data, LPP, PCA