A Face Recognition System Based on Local Feature Analysis

Arca, Stefano; Campadelli, Paola; Lanzarotti, Raffaella

doi:10.1007/3-540-44887-x_22

Cited by 19 publications

(14 citation statements)

References 9 publications

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“…-Combination of HMM and SVM [87] 100 -1DHMM + Wavelet [128] 100 -Pseudo 2D HMM + Wavelet [129] 100 -SVM + PCA [114] 97 -ICA [130] 85 -Gabor + ICA [131] 100 -LFA [132] 100 -PCA [11] 80.5 -Pose estimator [133] 99 -Fisherfaces with BCD [134] 95.45…”

Section: Resultsmentioning

confidence: 99%

A Survey of 2D Face Recognition Techniques

et al. 2016

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Despite the existence of various biometric techniques, like fingerprints, iris scan, as well as hand geometry, the most efficient and more widely-used one is face recognition. This is because it is inexpensive, non-intrusive and natural. Therefore, researchers have developed dozens of face recognition techniques over the last few years. These techniques can generally be divided into three categories, based on the face data processing methodology. There are methods that use the entire face as input data for the proposed recognition system, methods that do not consider the whole face, but only some features or areas of the face and methods that use global and local face characteristics simultaneously. In this paper, we present an overview of some well-known methods in each of these categories. First, we expose the benefits of, as well as the challenges to the use of face recognition as a biometric tool. Then, we present a detailed survey of the well-known methods by expressing each method's principle. After that, a comparison between the three categories of face recognition techniques is provided. Furthermore, the databases used in face recognition are mentioned, and some results of the applications of these methods on face recognition databases are presented. Finally, we highlight some new promising research directions that have recently appeared.

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Section: Resultsmentioning

confidence: 99%

A Survey of 2D Face Recognition Techniques

et al. 2016

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“…This resulted in creating many more algorithms with eigenfaces as their base. As many algorithms were developed with the help of eigenfaces, many applications which are present in the market or many applications which are yet to release, they all still use eigenfaces [9,14].…”

Section: Eigenfaces and Fisherfacesmentioning

confidence: 99%

Face Recognition Techniques for Authentication in Smart Devices - Comparative Study

George¹,

Tulasi²

2017

IJAIS

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With rapid development of technology there has been a surge in hand-held devices. These devices are looked upon as an alternative to the traditional devices like personal computer and laptop. As the storage and the processing capabilities of these devices are increasing they are been termed as smart devices. The amount of personal data stored in these devices has increased many folds. In order to ensure that the critical data that is stored in these devices is secured, it is essential to put in place authentication processes. Authentication can be done at multiple levels, ranging from a password to face recognition. Algorithms like Eigenfaces, Fisherfaces are being used as a part of authentication applications. This paper tries to provide a comparative study on most commonly used face recognition algorithms.

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“…Probe images are projected onto the Gabor jets and compared, through elastic graph matching, to the learned images based on both the extracted coefficients at the corresponding locations and distances between the formed graphs. This method requires manual definition of the grid structure, but a fully automatic version was published recently [Arca et al 2003]. …”

Section: Radial Angular Hyperbolicmentioning

confidence: 99%

Face recognition based on polar frequency features

Zana

César

2006

ACM Trans. Appl. Percept.

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A novel biologically motivated face recognition algorithm based on polar frequency is presented. Polar frequency descriptors are extracted from face images by Fourier-Bessel transform (FBT). Next, the Euclidean distance between all images is computed and each image is now represented by its dissimilarity to the other images. A Pseudo-Fisher Linear Discriminant was built on this dissimilarity space. The performance of Discrete Fourier transform (DFT) descriptors, and a combination of both feature types was also evaluated. The algorithms were tested on a 40-and 1196-subjects face database (ORL and FERET, respectively). With 5 images per subject in the training and test datasets, error rate on the ORL database was 3.8, 1.25 and 0.2% for the FBT, DFT, and the combined classifier, respectively, as compared to 2.6% achieved by the best previous algorithm. The most informative polar frequency features were concentrated at low-to-medium angular frequencies coupled to low radial frequencies. On the FERET database, where an affine normalization preprocessing was applied, the FBT algorithm outperformed only the PCA in a rank recognition test. However, it achieved performance comparable to state-of-the-art methods when evaluated by verification tests. These results indicate the high informative value of the polar frequency content of face images in relation to recognition and verification tasks, and that the Cartesian frequency content can complement information about the subjects' identity, but possibly only when the images are not pre-normalized. Possible implications for human face recognition are discussed.

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A Face Recognition System Based on Local Feature Analysis

Cited by 19 publications

References 9 publications

A Survey of 2D Face Recognition Techniques

A Survey of 2D Face Recognition Techniques

Face Recognition Techniques for Authentication in Smart Devices - Comparative Study

Face recognition based on polar frequency features

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