Robust Face Recognition Using Advanced Correlation Filters with Bijective-Mapping Preprocessing

Savvides, Marios; Xie, Chunyan; Chu, Nancy; Kumar, B. V. K. Vijaya; Podilchuk, Christine I.; Patel, Archana; Harthattu, Ashwath; Mammone, Richard J.

doi:10.1007/11527923_63

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…Figure 3 shows the ROC curve for the given algorithm on one of the Face Recognition Grand Challenge (FRGC) dataset [6] Version 2 Experiment 1 consisting of 16,028 faces or approximately 257 million scores to form the similarity matrix that is used to generate the ROC curve. The baseline algorithm was provided by NIST and the results are shown along with the early version of the algorithm proposed here [3,4] as well as the new p-edit distance algorithm described here. Note that at a false accept rate (FAR) of 0.1%, the baseline algorithm provides a false rejection rate (FRR) of 30% while the p-edit distance algorithm provides a FRR of 10%.…”

Section: Resultsmentioning

confidence: 99%

“…A new face or object recognition algorithm has been recently introduced [1,2] that is an extension of previous work [3,4]. The framework introduced here, referred to as a pictorial-edit or p-edit distance, is similar to edit distances used in text searches and other searches where the data is represented by a one dimensional string of discrete symbols such as letters and numbers.…”

Section: Introductionmentioning

confidence: 99%

“…The new method does not rely on training data either to build a reliable model or to train the classifier. An early version of this algorithm was applied to the problem of face recognition [3] as well as image preprocessing and registration for face recognition [4].…”

Section: Introductionmentioning

confidence: 99%

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Face recognition using a pictorial-edit distance

Hulbert

Podilchuk

Mammone

2008

2008 15th IEEE International Conference on Image Processing

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A new face recognition algorithm is introduced that is based on finding an optimal mapping between two images and measuring the properties of the mapping to determine the similarity between the two images. This new method is shown to be extremely robust to variations in pose, camera position and lighting for the problem of face recognition especially in an uncontrolled environment such as video surveillance. The technique does not depend on locating or training on facial features and can be applied in general to any object recognition problem. We present recognition results on current databases available to the research community and will demonstrate a real-time version of the software for access control and surveillance applications at the conference.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Face recognition using a pictorial-edit distance

Hulbert

Podilchuk

Mammone

2008

2008 15th IEEE International Conference on Image Processing

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“…An early version of the algorithm proposed here has been successfully applied to preprocessing for improved face recognition performance [11] as well as a stand-alone face recognition system [12,13]. This algorithm has also been evaluated in the Face Recognition Grand Challenge (FRGC) [14].…”

Section: Introductionmentioning

confidence: 98%

Face recognition for uncontrolled environments

et al. 2010

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A new face recognition algorithm has been proposed which is robust to variations in pose, expression, illumination and occlusions such as sunglasses. The algorithm is motivated by the Edit Distance used to determine the similarity between strings of one dimensional data such as DNA and text. The key to this approach is how to extend the concept of an Edit Distance on one-dimensional data to two-dimensional image data. The algorithm is based on mapping one image into another and using the characteristics of the mapping to determine a two-dimensional Pictorial-Edit Distance or P-Edit Distance. We show how the properties of the mapping are similar to insertion, deletion and substitution errors defined in an Edit Distance. This algorithm is particularly well suited for face recognition in uncontrolled environments such as stand-off and other surveillance applications. We will describe an entire system designed for face recognition at a distance including face detection, pose estimation, multi-sample fusion of video frames and identification. Here we describe how the algorithm is used for face recognition at a distance, present some initial results and describe future research directions.

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