Facial Features for Template Matching Based Face Recognition

Chai, Tong-Yuen; Rizon, M.; San, Woo San; Seong, Tan Ching

doi:10.3844/ajassp.2009.1897.1901

Cited by 16 publications

(11 citation statements)

References 21 publications

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“…Furthermore, a study has been proposed, combining template iris and a template that contains both eyes in order to detect more precisely the eyes (Yuen et al, 2009). Where this study proposed the candidate irises and assigns a cost for each pair of irises.…”

Section: Resizing Based Templates Are Known As a Multi-scalementioning

confidence: 99%

Eye Detection Using Composite Cross-Correlation

Nanaa¹

2013

American Journal of Applied Sciences

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This study presents a new eye detection method depending on composite template matching for facial images. The objective of this study is to utilize template match method to detect the eyes from given images and to improve this method to obtain higher rate of detection. The idea of our method is to integrate cross correlations of various eye templates. Thus, the correct values of single template matching based eye detection dominated the final output. It also contributed to the re-correct the detection in the event of failure of all single templates. The study also presents a method to obtain candidate eye pixels which contribute to abbreviate the time required to implement up to 91%. The formula of composite cross correlation has been generalized taking into account the differences between the sizes, shifts and irregular single templates. The experiments applied on PICS database reported 98.76% as eye detection rate

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Section: Resizing Based Templates Are Known As a Multi-scalementioning

confidence: 99%

Eye Detection Using Composite Cross-Correlation

Nanaa¹

2013

American Journal of Applied Sciences

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“…The drawbacks of the existing methods are that they do not return correspondences, suffer from the need for human designed templates and are applied to 2D images and limited 3D images. Chai et al (2009) presented to reduce the effect of beard and moustache for facial features detection and introduced facial features based template matching as the classification method. The proposed algorithm gives better retrieval rate compare to existing methods.…”

Section: Feature Based Methodmentioning

confidence: 99%

Shape Retrieval through Angular Distance with Shortest Augmenting Path Algorithm

Chitra¹

2011

Journal of Computer Science

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Problem statement: The shape of an object is very important in object recognition. Shape matching is a challenging problem, especially when articulation and deformation of a part occurs. These variations may be insignificant for human recognition but often cause a matching algorithm to give results that are inconsistent with our perception. Approach: We proposed a customized approach to measure similarity between shapes and exploit it for shape retrieval. The similarity was measured using the correspondence between the points on the two shapes and applying the aligning transformation. The correspondence was solved by the shape context with shortest augmenting path algorithm. Based on the correspondence, the aligning transformation is applied which best aligns the two shapes. Thin Plate Spline (TPS) with angular distance was to provide the better class of transformation maps. The matching error was calculated by the errors between the correspondence points on the two shapes and energy required in aligning transformation. Object recognition was achieved by the k-nearest neighbor algorithm. Result: The algorithm was efficient method for shape matching which performs the well on bulls eye test and produce 91.23% of retrieval rate on MPEG database. Conclusion: The proposed method is simple, invariant to noise and gives better error rate compare to the existing methods. It can also be extended to the handwritten characters, industrial objects, face recognition and COIL data base.

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“…I found that the author also has to compare with some recent paper that published recently that can be explored to enhance the state of the art of the study such (Yuen et al, 2009;Ross et al, 2006, Xinjian et al, 2006Sharat et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

Comment on "Method of Fingerprint Identification"

Gaol¹

2011

Journal of Computer Science

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Problem statement: Paper that written by Surachai Panich reported matching two fingerprints taken from small database of fingerprints. Approach: In this study we used extensive literature review and research publication. We also compare with some best practices that applied in the business and industrial field for matching the fingerprints. Results: Surachai Panich used his method only for around one hundred data. However, we found that in the recent research most of the fingerprint problems are explore in million of data. I found some of the literature review are quiet old. Since the advancement of Fingerprint matching is increase very rapidly so the comparison with the recent publication is a must. The comparison has to be in the experimental using million of data as the input. We do not find such experimental result on this paper. Conclusion/Recommendations: The paper should explore more in biometrics feature extraction for matching process. The biometrics have analogy to the use of physiological or biological characteristics to measure the identity of an individual. These features are assuming unique to each individual and remain unaltered during a person's lifetime. These features make biometrics a promising solution to the society.

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Facial Features for Template Matching Based Face Recognition

Cited by 16 publications

References 21 publications

Eye Detection Using Composite Cross-Correlation

Eye Detection Using Composite Cross-Correlation

Shape Retrieval through Angular Distance with Shortest Augmenting Path Algorithm

Comment on "Method of Fingerprint Identification"

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