A Review of Face Recognition Techniques for In-Camera Applications

Iancu, Claudia; Corcoran, Peter; Costache, Gabriel

doi:10.1109/isscs.2007.4292641

Cited by 9 publications

(6 citation statements)

References 13 publications

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“…With PCA, good success rates can be obtained by detecting patterns from images captured in ideal environments particularly with good illumination -or by employing several image processing techniques to enhance images before feature extraction. In addition, it is computationally efficient compared with other similar methods [7] because dimensionality reduction can be performed easily to accelerate the calculations. Thus, we employ PCA to extract the most important features from visual objects, because of its extensive and successful application to many datasets.…”

Section: Feature Extraction and Selection And Distance Measurementmentioning

confidence: 99%

Novel hybrid object-based non-parametric clustering approach for grouping similar objects in specific visual domains

Kuru

Khan

2018

Applied Soft Computing

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Current widely employed clustering approaches may not yield satisfactory results with regard to the characteristics and distribution of datasets and number of clusters to be sought, especially for visual domains in multidimensional space. This study establishes a novel clustering methodology using a pairwise similarity matrix, Clustering Visual Objects in Pairwise Similarity Matrix (CVOIPSM), for grouping similar objects in specific visual domains. A dimensionality reduction and feature extraction technique, along with a distance measuring method and a newly established algorithm, Clustering in Pairwise Similarity Matrix (CIPSM), are combined to develop the CVOIPSM methodology. CIPSM utilizes both Rk-means and an agglomerative, contractible, expandable (ACE) technique to calculate a membership degree based on maximizing inter-class similarity and minimizing intra-class similarity. CVOIPSM has been tested on several datasets, with average success rates on downsized subsamples between 87.5% and 97.75% and between 81% and 87% on the larger datasets. The difference in the success rates for small and large datasets is not statistically significant (p>0.01). Moreover, this method automatically determines the likely number of clusters without any user dictation. The empirical results and the statistical significance test on these results ensure that CVOIPSM performs effectively and efficiently on specific visual domains, disclosing the interrelated patterns of similarities among objects.

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Section: Feature Extraction and Selection And Distance Measurementmentioning

confidence: 99%

Novel hybrid object-based non-parametric clustering approach for grouping similar objects in specific visual domains

Kuru

Khan

2018

Applied Soft Computing

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“…On the other hand, other similar studies having a fixed number of syndromes up to 10 in number are implemented to reveal the potential rather than to be an application to serve medical professionals for their everyday needs. Right diagnosis and consequently right treatments can influence progression of disease, especially in term of removing the effects of environmental factors 11 . For instance, when you supplement a patient having a syndrome with hormone, the patient may get better.…”

Section: Comparison To Other Published Studiesmentioning

confidence: 99%

“…PCA can be computed as an optimal compression scheme that minimizes the mean squared error between an image and its reconstruction [9] as well as it may achieve good results of up to 96% recognition under ideal conditions [10] which can be provided with either capturing images in ideal environments by having a good illumination and using a good capturing device or preprocessing of images with several image processing techniques before extracting features. PCA using eigenfaces have been identified as computational efficient on even reduced hardware [11]. Additionally it is used for dimension reduction as in our study from 2D to 1D to ease and speed up the calculations.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic Decision Support System in Dysmorphology

Kuru¹,

Tunca²

2012

Decision Support Systems

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“…Face recognition has been developing quickly, and it seems that there is not a limit for the capacity of this system, because the data entry of these systems can be really big. For this reason, researchers aim to improve face recognition systems by introducing new characteristics and new working lines that can be valid for the development of these kinds of systems (Iancu et al 2007).…”

Section: Introductionmentioning

confidence: 99%

Face Recognition by Fast and Stable Bi-dimensional Empirical Mode Decomposition

Gallego-Jutglà

Al-Baddai

Al-Subari

et al. 2015

Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing

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In this study the use of a new fast and stable decomposition technique, bi-dimensional empirical mode decomposition, is used for face recognition tasks. Images are decomposed individually, and then the distance with reference images is computed. Three different types of distances are tested. Then class association is based on minimum distance and by using a classifier. Preliminary results (90.0% of classification rate) are satisfactory and will justify a deep investigation on how to apply this bi-dimensional decomposition technique for face recognition.

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A Review of Face Recognition Techniques for In-Camera Applications

Cited by 9 publications

References 13 publications

Novel hybrid object-based non-parametric clustering approach for grouping similar objects in specific visual domains

Novel hybrid object-based non-parametric clustering approach for grouping similar objects in specific visual domains

Diagnostic Decision Support System in Dysmorphology

Face Recognition by Fast and Stable Bi-dimensional Empirical Mode Decomposition

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