The FERET database and evaluation procedure for face-recognition algorithms

Phillips, P. Jonathon; Wechsler, Harry; Huang, Jen-Peng; Rauss, Patrick J.

doi:10.1016/s0262-8856(97)00070-x

Cited by 2,098 publications

(1,157 citation statements)

References 11 publications

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“…Gray-scale images of 68 different faces were selected from the FERET database (Phillips et al, 1998), excluding those with facial hair or glasses. Images were closely cropped and then scaled to have identical vertical extent.…”

Section: Stimulimentioning

confidence: 99%

Spatial maps in frontal and prefrontal cortex

2006

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Section: Stimulimentioning

confidence: 99%

Spatial maps in frontal and prefrontal cortex

2006

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“…The testing of the proposed algorithm has been done on test facial images from web and FERET database (Phillips et al (2000(Phillips et al ( , 1998) and comparison done with some of the existing techniques in terms of quality metrics SSIM, PSNR, SNR and MSE. It has been observed that considerable speedup was achieved by the parallel execution of proposed face resolution enhancement algorithm as compared to its serial version.…”

Section: Resultsmentioning

confidence: 99%

“…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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“…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.

show abstract

The FERET database and evaluation procedure for face-recognition algorithms

Cited by 2,098 publications

References 11 publications

Spatial maps in frontal and prefrontal cortex

Spatial maps in frontal and prefrontal cortex

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

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

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