Parameterisation of a stochastic model for human face identification

Samaria, Ferdinando; Harter, Augustin

doi:10.1109/acv.1994.341300

Cited by 1,794 publications

(1,068 citation statements)

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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%

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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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Section: Resultsmentioning

confidence: 99%

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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“…To evaluate the performance of our algorithm, we choose ORL [13] and Yale [14] face databases. The ORL database consists of 40 individuals and 10 gray-scale images per subject.…”

Section: Image Database and Experimental Resultsmentioning

confidence: 99%

Combination of Gabor and Curvelet Texture Features forFace Recognition Using Principal Component Analysis

Rahman¹,

Naim²,

Farooq³

et al. 2012

IJCEE

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Abstract-Biometric face Recognition is a new generation technology for identification and verification. Many techniques have been developed for this purpose in past two decades. A good face recognition technique must require unique, effective and efficient features from face images. Content based image retrieval (CBIR) can extract features that can be useful for face recognition. Recently, Gabor and curvelet texture features have been successfully used in image retrieval research. In this paper, we propose a novel face recognition method that uses texture features obtained by calculating mean and standard deviation of Gabor and curvelet transformed face images. PCA is then applied to the feature vectors instead of entire transformed images which traditional methods do. Using this process, we build four classifiers using mean and standard deviation calculated from Gabor and curvelet transformed face images. For identification purpose, a new matching strategy is proposed that checks goodness of four matching results of the classifiers. As we consider only mean or standard deviation features, the image representation has comparatively lower dimensions. Furthermore, our proposed method does not necessarily require all the input images to be of same resolution. We evaluate the proposed method using ORL and Yale face databases. The recognition results of the experiments show that our approach is significantly better than the conventional methods.Index Terms-Content based image retrieval (CBIR); gabor and curvelet transform; principal component analysis (PCA).

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“…Euclidean distance, are not satisfactory. The reason is faces of the same person may have large variation caused by difference in lighting [2], pose [30], appearance, expression [11], partial occlusion [28] and cluster as illustrated in Fig. 2.…”

Section: Introductionmentioning

confidence: 99%

Face identification using reference-based features with message passing model

Shen

Wang

et al. 2013

Neurocomputing

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In this paper, we propose a system for face identification. Given two query face images, our task is to tell whether or not they are of the same person. The main contribution of this paper comes from two aspects: (1) We adopt the one-shot similarity kernel [35] for learning the similarity of two face images. The learned similarity measures are then used to map a face image to reference images. (2) We propose a graph-based method for selecting an optimal set of reference images. Instead of directly working on the image features, we use the learned similarity to the reference images as the new features and compute the corresponding matching score of the two query images. Our approach is effective and easy to implement. We show encouraging and favorable results on the "Labeled Faces in the Wild" -a challenging data set of faces.

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Parameterisation of a stochastic model for human face identification

Cited by 1,794 publications

References 6 publications

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

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

Combination of Gabor and Curvelet Texture Features forFace Recognition Using Principal Component Analysis

Face identification using reference-based features with message passing model

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