We introduce a novel use of 2D barcodes for storing facial biometrics. To accomplish this, we design 2D color barcodes with larger storage capabilities than traditional 2D barcodes. Biometric data are secured through digital signature so as to be protected from malicious tampering. In order to improve the quality of facial recognition, we combine eigenfaces, shape landmarks identified through s...

In this paper, we propose a novel method for image feature extraction, namely the twodimensional local graph embedding, which is based on maximum margin criterion and thus not necessary to convert the image matrix into high-dimensional image vector and directly avoid computing the inverse matrix in the discriminant criterion. This method directly learns the optimal projective vectors from 2D im...

In this paper, we extend Fisherface for face recognition from one example image per person. Fisherface is one of the most successful face recognition methods. However, Fisherface requires several training images for each face, so it cannot be applied to the face recognition applications where only one example image per person is available for training. To tackle this problem, we extended the Fi...

In this paper, we propose a robust face recognition technique based on the principle of eigenfaces. The traditional eigenface recognition (EFR) method works quite well when the input test patterns are cropped faces. However, when confronted with recognizing faces embedded in arbitrary backgrounds, the EFR method fails to discriminate effectively between faces and background patterns, giving ris...

In this paper, the problem of face authentication using salient facial features together with statistical generative models is adressed. Actually, classical generative models, and Gaussian Mixture Models in particular make strong assumptions on the way observations derived from face images are generated. Indeed, systems proposed so far consider that local observations are independent, which is ...

Facial feature extraction with enhanced discriminatory power plays an important role in face recognition (FR) applications. Linear discriminant analysis (LDA) is a powerful tool used for dimensionality reduction and feature extraction in FR tasks. However, the classification performance of traditional LDA is often degraded, due to two factors: 1) their classification accuracies suffer from the ...

Abstract Principal Component Analysis (PCA) has been successfully applied to many applications, including ear recognition. This paper presents a Two Dimensional Multi-Band PCA (2D-MBPCA) method, inspired by based techniques for multispectral and hyperspectral images, which have demonstrated significantly higher performance that of standard PCA. The proposed method divides the input image into n...

Face recognition is a biometric analysis tool that has enabled surveillance systems to detect humans and recognize humans without their co-operation. In this paper we evaluate the basics of the Principal Component Analysis (PCA) and verify the results of this algorithm on a training database of images. The same principle is in effect used to recognise the gender of the test image by evaluating ...

Natural images are the composite consequence of multiple factors related to scene structure, illumination, and imaging. Multilinear algebra, the algebra of higher-order tensors, offers a potent mathematical framework for analyzing the multifactor structure of image ensembles and for addressing the difficult problem of disentangling the constituent factors or modes. Our multilinear modeling tech...

We present generative models dedicated to face recognition. Our models consider data extracted from color face images and use Bayesian Networks to model relationships between different observations derived from a single face. Specifically, the use of color as a complementary observation to local, grayscale-based features is investigated. This is done by means of new generative models, combining...