Learning Multi-scale Block Local Binary Patterns for Face Recognition

Liao, Shengcai; Zhu, Xiangxin; Lei, Zhen; Zhang, Lun; Li, Stan Z.

doi:10.1007/978-3-540-74549-5_87

Cited by 462 publications

(273 citation statements)

References 19 publications

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“…Pengembangan ini dikhususkan untuk meningkatkan kemampuan membedakan [29][30], meningkatkan ketahanan terhadap perubahan (robustness) [31][32], pengembangan dalam penelitian pemilihan neighbourhood [33][34], pengembangkan dalam ranah dimensi tiga [35][36], pengembangan dalam kombinasi penggunaan dengan metode lain [37][38].…”

Section: Issn 2085-4811unclassified

Survei Penelitian Pengenalan Pola dalam Identifikasi Biometrik

Lionnie¹,

Alaydrus²

2017

InComTech

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Pengenalan pola memainkan peranan yang penting dalam identifikasibiometrik. Hal ini dikarenakan pengenalan pola dalam identifikasibiometrik membantu pihak berwenang dalam mengungkap identitasseorang kriminal. Pengenalan pola identifikasi biometrik dalam imageprocessing mencakup pengenalan pola wajah, geometri dari sebuahtangan, iris dan retina dari organ mata, sklera mata, pembuluh darah,tanda kulit dan rambut tubuh. Pengenalan pola identifikasi biometrikmembutuhkan metode pengenalan pola yang akurat, pemilihan tahap praproses dan metode klasifikasi yang sesuai. Pada survei paper ini dibahasmengenai beberapa metode tahap pra proses seperti Averaging Filter,Histogram, Desaturation, Binerisation dan Image Alignment. Metodepengenalan pola yang dibahas pada paper ini adalah Gabor Features,Local Binary Pattern, Local Gabor Binary Pattern dan Haar WaveletTransform. Sedangkan metode klasifikasi yang dibahas adalah Euclideandistance, Chi-square distance dan Histogram Matching. Agar dapatmemberikan hasil terbaik, setiap sistem pengenalan pola tidak dapatmenggunakan metode yang sama untuk mengenali pola identifikasibiometrik yang berbeda. Dibutuhkan penelitian dalam penggunaanmetode pra proses, ekstraksi fitur dan klasifikasi untuk setiap identifikasibiometrik yang ingin dikenali polanya.

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Section: Issn 2085-4811unclassified

Survei Penelitian Pengenalan Pola dalam Identifikasi Biometrik

Lionnie¹,

Alaydrus²

2017

InComTech

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“…4. Choose the correct match from the trained pattern spectrum database for which the given testing pattern HMM and GMM Face recognition through Modelbased methods [5] Multiscale Block LBP Face Recognition using Texture model [9] Combined approach of LBP and geometrical features Face recognition through the combined approach of texture with geometry features [11] Fusion of shape and texture features…”

Section: Testing Phasementioning

confidence: 99%

Face Representation and Face Recognition using Optimized Local Ternary Patterns (OLTP)

Raja¹,

Sadasivam²

2017

Journal of Electrical Engineering and Technology

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-For many years, researchers in face description area have been representing and recognizing faces based on different methods that include subspace discriminant analysis, statistical learning and non-statistics based approach etc. But still automatic face recognition remains an interesting but challenging problem. This paper presents a novel and efficient face image representation method based on Optimized Local Ternary Pattern (OLTP) texture features. The face image is divided into several regions from which the OLTP texture feature distributions are extracted and concatenated into a feature vector that can act as face descriptor. The recognition is performed using nearest neighbor classification method with Chi-square distance as a similarity measure. Extensive experimental results on Yale B, ORL and AR face databases show that OLTP consistently performs much better than other well recognized texture models for face recognition.

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“…The proposed approach is partially inspired by Multiscale Block Local Binary Pattern (MBLBP) [25], in which authors propose to compare average intensities of neighbouring blocks of pixels in order to create a binary string. In MBLBP, three patches with 3×3, 9×9, and 15×15 pixels are divided into nine blocks (cells).…”

Section: Optimisation Problemmentioning

confidence: 99%

An optimisation approach to the design of a fast, compact and distinctive binary descriptor

Oszust

2016

SIViP

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The desired local feature descriptor should be distinctive, compact and fast to compute and match. Therefore, many computer vision applications use binary keypoint descriptors instead of floating-point, rich techniques. In this paper, an optimisation approach to the design of a binary descriptor is proposed, in which the detected keypoint is described using several, scale-dependent patches. Each such patch is divided into disjoint blocks of pixels, and then, binary tests between blocks' intensities, as well as their gradients, are used to obtain the binary string. Since the number of image patches and their relative sizes influence the descriptor creation pipeline, a simulated annealing algorithm is used to determine them, optimising recall and precision of keypoint matching. The simulated annealing is also used for dimensionality reduction in long binary strings. The proposed approach is extensively evaluated and compared with SIFT, SURF and BRIEF on public benchmarks. Obtained results show that the binary descriptor created using the resulted pipeline is faster to compute and yields comparable or better performance than the state-of-the-art descriptors under different image transformations.

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Learning Multi-scale Block Local Binary Patterns for Face Recognition

Cited by 462 publications

References 19 publications

Survei Penelitian Pengenalan Pola dalam Identifikasi Biometrik

Survei Penelitian Pengenalan Pola dalam Identifikasi Biometrik

Face Representation and Face Recognition using Optimized Local Ternary Patterns (OLTP)

An optimisation approach to the design of a fast, compact and distinctive binary descriptor

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