Recognition of JPEG compressed face images based on statistical methods

Eickeler, Stefan; Müller, Stefan; Rigoll, Gerhard

doi:10.1016/s0262-8856(99)00055-4

Cited by 118 publications

(79 citation statements)

References 17 publications

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“…There are some studies which model local appearance with overlapping blocks [5], [6]. In this paper, we integrate part of the neighboring frequency information by utilizing the nature of the Gabor wavelet filters, in which the local filter response is smoothed with neighboring pixels.…”

Section: Introductionmentioning

confidence: 99%

Multi-resolution Local Appearance-Based Face Verification

Gao

Ekenel

Fischer

et al. 2010

2010 20th International Conference on Pattern Recognition

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Abstract-Facial analysis based on local regions / blocks usually outperforms holistic approaches because it is less sensitive to local deformations and occlusions. Moreover, modeling local features enables us to avoid the problem of high dimensionality of feature space. In this paper, we model the local face blocks with Gabor features and project them into a discriminant identity space. The similarity score of a face pair is determined by fusion of the local classifiers. To acquire complementary information in different scales of face images, we integrate the local decisions from various image resolutions. The proposed multi-resolution block based face verification system is evaluated on the experiment 4 of Face Recognition Grand Challenge (FRGC) version 2.0. We obtained 92.5% verification rate @ 0.1% FAR, which is the highest performance reported on this experiment so far in the literature.

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Section: Introductionmentioning

confidence: 99%

Multi-resolution Local Appearance-Based Face Verification

Gao

Ekenel

Fischer

et al. 2010

2010 20th International Conference on Pattern Recognition

View full text Add to dashboard Cite

show abstract

“…The states of the embedded HMMs are in turn modeled by GMMs. This approach was used for the face identification task in [7], [20] and the training process is described in detail in [14]. As shown in Fig.…”

Section: Pseudo-2d Hmmmentioning

confidence: 99%

“…number of FAs number of impostor accesses (6) FRR = number of FRs number of true claimant accesses (7) To aid the interpretation of accuracy, the two error measures are often combined using the Half Total Error Rate (HTER), defined as [2]:…”

Section: Banca Database and Experiments Protocolsmentioning

confidence: 99%

“…Various techniques have been proposed for face classification; some examples are systems based on Principal Component Analysis (PCA) feature extraction [24], modular PCA [16], Elastic Graph Matching (EGM) [6], and Support Vector Machines [19]. Examples specific to statistical models include one-dimensional Hidden Markov Models (1D HMMs) [20], pseudo-2D HMMs [7] and Gaussian Mixture Models (GMMs) [3], [21] (which can be considered as a simplified version of HMMs). A recent review of related literature can be found in [11].…”

Section: Introductionmentioning

confidence: 99%

“…see [7], [14], [20]); in only relatively few publications performance evaluation is found while using automatic face localization (e.g. [3], [19]).…”

Section: Introductionmentioning

confidence: 99%

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