The FERET evaluation methodology for face-recognition algorithms

Phillips, P. Jonathon; Moon, Hyeonjoon; Rizvi, Syed A.; Rauss, Patrick J.

doi:10.6028/nist.ir.6264

Cited by 550 publications

(758 citation statements)

References 10 publications

(16 reference statements)

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“…The system is tested on a range of images, which are much more heterogeneous than normally used in reports of automatic recognition systems (Phillips et al, 2000;Zhao et al, 2003). Using this realistic range of superficial image characteristics, not normally noticeable until one sees them all together, as in Figure 3, there is immediate advantage for storing an average of learning images, over storing them all individually.…”

Section: Discussionmentioning

confidence: 99%

“…However, it is a difficult problem to solve across a realistic variation in images. In the DARPA-sponsored FERET evaluation of face recognition systems (Phillips, Moon, Rizvi & Rauss, 2000), several algorithms performed well when matching two images of a face, taken in the same sitting, with the same camera, but varied expression. For example, recognition rates of 95% are reported for analyses based on Principal Components Analysis (Moghaddam, Nastar & Pentland, 1997; and see below) and on wavelet-based systems (Wiskott, Fellous, Kruger & von der Malsburg, 1997).…”

Section: Automatic Face Recognitionmentioning

confidence: 99%

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Robust representations for face recognition: The power of averages

Burton

Jenkins

Hancock

et al. 2005

Cognitive Psychology

268

297

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We are able to recognise familiar faces easily across large variations in image quality, though our ability to match unfamiliar faces is strikingly poor. Here we ask how the representation of a face changes as we become familiar with it. We use a simple imageaveraging technique to derive abstract representations of known faces. Using Principal Components Analysis, we show that computational systems based on these averages consistently outperform systems based on collections of instances. Furthermore, the quality of the average improves as more images are used to derive it. These simulations are carried out with famous faces, over which we had no control of superficial image characteristics. We then present data from three experiments demonstrating that image averaging can also improve recognition by human observers. Finally, we describe how PCA on image averages appears to preserve identity-specific face information, while eliminating non-diagnostic pictorial information. We therefore suggest that this is a good candidate for a robust face representation.2 Robust representations for face recognition: the power of averages

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Section: Discussionmentioning

confidence: 99%

Section: Automatic Face Recognitionmentioning

confidence: 99%

Robust representations for face recognition: The power of averages

Burton

Jenkins

Hancock

et al. 2005

Cognitive Psychology

268

297

View full text Add to dashboard Cite

show abstract

“…Then, the classification method briefly described in Section 2 was applied, and the classification accuracy of each dataset was recorded. The face datasets that were tested are FERET (Phillips et al, 1998(Phillips et al, , 2000, ORL (Samaria & Harter, 1994), JAFFE (Lynos et al, 1998), the Indian Face Dataset (Jain & Mukherjee, 2002), Yale B (Georghiades, Belhumeur, & Kriegman, 2001), and Essex face dataset (Hond & Spacek, 1997;Spacek, 2002). The sizes and locations of the non-facial areas that were cut from the original images is described in Table 1, and the accuracy of automatic classification of these images are also specified in the table.…”

Section: Resultsmentioning

confidence: 99%

“…The primary method of assessing the efficacy of face recognition algorithms and comparing the performance of the different methods is by using pre-defined and publicly available face datasets such as FERET (Phillips et al, 1998(Phillips et al, , 2000, ORL (Samaria & Harter, 1994), JAFFE (Lynos et al, 1998), the Indian Face Dataset (Jain & Mukherjee, 2002), Yale B (Georghiades, Belhumeur, & Kriegman, 2001), and Essex face dataset (Hond & Spacek, 1997).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Face Datasets as Tools for Assessing the Performance of Face Recognition Methods

Shamir

2008

Int J Comput Vis

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Face datasets are considered a primary tool for evaluating the efficacy of face recognition methods.Here we show that in many of the commonly used face datasets, face images can be recognized accurately at a rate significantly higher than random even when no face, hair or clothes features appear in the image. The experiments were done by cutting a small background area from each face image, so that each face dataset provided a new image dataset which included only seemingly blank images. Then, an image classification method was used in order to check the classification accuracy. Experimental results show that the classification accuracy ranged between 13.5% (color FERET) to 99% (YaleB). These results indicate that the performance of face recognition methods measured using face image datasets may be biased. Compilable source code used for this experiment is freely available for download via the internet.

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“…Two folders have been created for testing, each containing 20 test images. One of the folders contains gray-scale images and second constitutes color facial images from database color FERET (Phillips et al (2000(Phillips et al ( , 1998). Table 1 and Table 2 shows the mean values of measured quality metrics and time taken for gray-scale and color images respectively.…”

Section: Discussionmentioning

confidence: 99%

Graphical Processing Unit Accelerated Face Resolution Enhancement using Pixels- Homogeneity and RelativeRatios

2017

IJCRR

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This work presents a GPU (Graphical Processing Unit) accelerated spatial domain oriented face resolution enhancement algorithm based on the homogeneity levels and relative-ratios of the pixels with respect to its surrounding pixels. The algorithm has been developed, implemented as well as tested in the MATLAB environment. MATLAB is slow in processing but at the same time a resourceful environment for the development in the area of image processing owing to its extremely rich set of functions and programmer-friendly integrated development environment. However, to compensate for the speed loss in testing and implementation phase, we have made use of GPU computing i.e. done parallelization of the algorithm on NVIDIA GPU using CUDA (Compute Unified Device Architecture) interface in the MATLAB environment. It is a simple but efficient algorithm in which kernel matrices are created encoding the homogeneity levels and relative-ratios of all pixels in surrounding four quadrants. Kernel matrices are subsequently applied to reconstruct the HR (High-Resolution) version from input LR (Low-Resolution) facial image.

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The FERET evaluation methodology for face-recognition algorithms

Cited by 550 publications

References 10 publications

Robust representations for face recognition: The power of averages

Robust representations for face recognition: The power of averages

Evaluation of Face Datasets as Tools for Assessing the Performance of Face Recognition Methods

Graphical Processing Unit Accelerated Face Resolution Enhancement using Pixels- Homogeneity and RelativeRatios

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