Robustness to expression variations in fractal-based face recognition

Ebrahimpour-Komleh, Hossein; Chandran, Vinod; Sridharan, S.

doi:10.1109/isspa.2001.949852

Cited by 9 publications

(5 citation statements)

References 11 publications

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“…We used a set of geometrical parameters ( In [13] domain numbers for each range block are used in a feature vector. They also used the quadtree partitioning geometry and applied each feature vector value at its geometrical position to normalize the size of each vector.…”

Section: Fractal Featuresmentioning

confidence: 99%

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Character representation and recognition using quad tree-based fractal encoding scheme

Mozaffari

Faez

Ziaratban

2005

Eighth International Conference on Document Analysis and Recognition (ICDAR'05)

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In this paper we proposed a new method for isolated handwritten Farsi/Arabic numerals recognition using quadtree-based fractal representation and iterated function system. Fractal codes represent affine transformations which when iteratively applied to the rangedomain pairs in an arbitrary initial image, the result is close to the given image. Each fractal code consists of six parameters such as the corresponding domain coordinates for each range block, brightness offset and an affine transformation. Based on fixed point theorem in iterated function system, we introduced fractal transformation classifier for optical character recognition. We also used Euclidean distance between fractal codes of a query image and fractal codes of all images in the database as a measure of distance for classification. Since fractal codes have different lengths, we applied PCA algorithm to normalize their lengths. There are ten digits in Farsi/Arabic language and since two of them are not used in Iran postal codes, therefore 8 classes are needed for digits. By using fractal codes with nearest neighbor classifier and fractal transformation, the recognition rate of 92.6% is obtained on our numeral database which contains 480 samples per digit and was gathered from more than 200 people with different ages and different educational background.

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Section: Fractal Featuresmentioning

confidence: 99%

“…In [12] and [13] fractal feature and fractal neighbor distances or fractal dimensions have been used for face recognition. A writer identification system has been utilized based on fractal construction of a reference base in [14].…”

Section: -Introductionmentioning

confidence: 99%

Character representation and recognition using quad tree-based fractal encoding scheme

Mozaffari

Faez

Ziaratban

2005

Eighth International Conference on Document Analysis and Recognition (ICDAR'05)

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“…Given the size of the database-40 facial images per person from a total of 117 subjects, or 4680 images-a hybrid approach that combines BPNN-based classification models with Decision Tree Clustering (DTC) was employed in this work to facilitate processing. Optical character recognition (Wilson et al, 1996), face image recognition (Ebrahimpour-Komleh et al, 2001), and data classification (Kaewchinporn, 2011) are just a few of the research projects that have employed the decision tree clustering approach to increase classification efficiency. Hierarchical clustering was used to classify the entire database into several super-classes.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Object Recognition Using Decision Tree Clustering by Incorporating Multi-Level BPNN Classifiers and Hybrid Texture Features

Kumar

2024

International Journal of Information Retrieval Research

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This work proposes a novel approach to object recognition, particularly for human faces, based on the principle of human cognition. The suggested approach can handle a dataset or problem with a large number of classes for classification more effectively. The model for the facial recognition-based object detection system was constructed using a combination of decision tree clustering based multi-level Backpropagation neural network classifier-TFMLBPNN-DTC and hybrid texture feature (ILMFD+GLCM) and applied on NS and ORL databases. This model produced the classification accuracy (±standard deviation) of 95.37 ±0.951877% and 90.83 ± 1.374369% for single input and 96.58 ±0.5604582% and 91.50 ± 2.850439% for group-based decision for NS and ORL database respectively. The better classification results encourage its application to other object recognition and classification issues. This work's basic idea also makes it easier to improve classification management for a wide range of classes.

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“…Furthermore, since calculation of fractal dimension of an object gives an idea of its spread within the support space (Feder, 1989) therefore it appears that one can try with a fractal dimension based feature to make up the gap created after PCA based study. A Fractal based image coding has been referenced in Face recognition (Tan & Yan, 1999;Ebrahimpour-Komleh et al, 2001;Kouzani et al, 1997). Fractals have been successfully used in Texture classification (Ferens & Kinsner, 1995) in which they measured generalized information content in the gray level.…”

Section: Introductionmentioning

confidence: 99%

Significant Enhancement of Object Recognition Efficiency Using Human Cognition based Decision Clustering

Kumar

Lahiri

2013

International Journal of Computer Vision and Image Processing

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It is well known that human can recognize object-pattern better using its temporal description. In this paper both theoretical study and experiments were performed to translate this cognition principle into mathematical formula. In the implementation phase we considered breaking up of temporal data of human face into an assembly of time series data for each of which we obtained a decision as output of the chosen classifier. An assembly of decisions was thus resulted for a single temporal input data which was further judiciously clustered to obtain the final decision. Interestingly, the work also showed that the successive order of the time series data was not needed to be maintained; rather an assembly of randomly chosen multiple test data was important to obtain quite significant level of enhancement of classification accuracy. Thus, it gives new interpretation on temporal data based human cognition. The work also indicated that augmentation of this method with any classifier including those which used decision clustering tree, might yield quite a significant enhancement of recognition efficiency.

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Robustness to expression variations in fractal-based face recognition

Cited by 9 publications

References 11 publications

Character representation and recognition using quad tree-based fractal encoding scheme

Character representation and recognition using quad tree-based fractal encoding scheme

A Novel Approach for Object Recognition Using Decision Tree Clustering by Incorporating Multi-Level BPNN Classifiers and Hybrid Texture Features

Significant Enhancement of Object Recognition Efficiency Using Human Cognition based Decision Clustering

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