Face Description with Local Binary Patterns: Application to Face Recognition

Ahonen, Timo; Hadid, Abdenour; Pietikäinen, Matti

doi:10.1109/tpami.2006.244

Cited by 5,006 publications

(2,836 citation statements)

References 22 publications

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“…These features are termed as Local Feature Analysis kernels [26]. This method is more robust to local facial features to carry out a match compared global facial features.…”

Section: Face Recognition Methodsmentioning

confidence: 99%

Face recognition system based on principal components analysis and distance measures

Goyal¹,

Meenpal²

2018

IJET

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Face recognition plays a vital role and has a huge scope in the field of biometrics, image processing, artificial intelligence, pattern recognition and computer vision. This paper presents an approach to perform face recognition using Principal Components Analysis (PCA) as feature extraction technique and different distance measures as matching techniques. The proposed method is developed after the deep study of a number of face recognition methods and their outcomes. In the proposed method, Principal Components Analysis is used for facial features extraction and data representation. It generates eigenvalues of the facial images, hence, reduces the dimensionality. The recognition is produced using three different matching techniques (Euclidean, Manhattan and Mahalanobis) and the results are` presented. Yale and Aberdeen Face Databases are used to test and analyze the results of the proposed method.

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“…These features are termed as Local Feature Analysis kernels [26]. This method is more robust to local facial features to carry out a match compared global facial features.…”

Section: Face Recognition Methodsmentioning

confidence: 99%

Face recognition system based on principal components analysis and distance measures

Goyal¹,

Meenpal²

2018

IJET

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“…Recently, Local Binary Patterns (LBPs) have been proposed and applied to face recognition 17) . LBP is obtained by thresholding neighborhoods of each pixel with the center pixel value, and then the histogram of LBPs is used as a texture descriptor.…”

Section: Feature Extraction and Matchingmentioning

confidence: 99%

[Invited Paper] Recent Advances in Biometric Recognition

Ito

Aoki

2018

MTA

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This paper presents recent advances in biometric recognition, where we focus on face, fingerprint and iris recognition, which are major research topics on biometric recognition. We summarize the research trend of face, fingerprint and iris recognition over the past decade. This paper also presents our activities of biometric recognition. Our approach employs the phase information obtained by Discrete Fourier Transform (DFT) of images. The phase information preserves the inherent features of the image, and its correlation function, called phase correlation or Phase-Only Correlation (POC), gives us both the good similarity measure for biometric recognition and the translational displacement for image registration.Our approach of using phase information has been successfully applied to fingerprint, face, iris, palmprint, finger knuckle and dental recognition. Among them, we present some interesting results of palmprint recognition, finger knuckle recognition and dental recognition.

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“…Applications of ICA in data analysis and feature extraction ICA minimizes both second -order and higher -order dependencies in the input data and attempt to find the basis along which the data are statistically independent. There are two construction of ICA for face recognition [11] Architecture I -Statistically independent basis image. Architecture IIfactorial code representation.To obtain completely independent components, which constitute complete faces.…”

Section: Independent Component Analysismentioning

confidence: 99%

Advance Technique for Feature Extraction and Image Compression

Kourav¹,

Singh²

2013

IJCA

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For image processing, it is very necessary that the selection of transform. In this paper, a comparative analysis of curve let transform with other transform for image processing .In this we proposed the applications of curve let transform in the field of image Compression ,phase recognition and feature extraction. For higher compression with quality reconstruction .The Wavelets gave a different aspect to the compression. Curvelet Transform gives better results in terms of PSNR. Face recognition is very important for many applications such as: video surveillance, criminal investigations and forensic applications, secure electronic banking, mobile phones, credit cards, secure access to buildings . The curve let transform is a multi scale directional transform, which allows an almost optimal non adaptive sparse representation of objects with edges. Curve let have also proven useful in diverse fields beyond the traditional image processing application, Curvelet transform improve recognition accuracy with featature extraction extraction algorithms PCA, LDA,ICA and NMF.

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Face Description with Local Binary Patterns: Application to Face Recognition

Cited by 5,006 publications

References 22 publications

Face recognition system based on principal components analysis and distance measures

Face recognition system based on principal components analysis and distance measures

[Invited Paper] Recent Advances in Biometric Recognition

Advance Technique for Feature Extraction and Image Compression

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